University of Western Australia€¦ · Hamid Ahmed Mohammed Ghafram Al Shahri BSc. (Hons.,...

University of Western Australia

The Impact of Permeability Heterogeneity on the Effectiveness of Alkaline Surfactant Polymer Enhanced

Oil Recovery Process

Hamid Ahmed Mohammed Ghafram Al Shahri

BSc. (Hons., University of Leeds), MPetEng. (Curtin University)

School of Mechanical and Chemical Engineering

This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia

2012

iii

Abstract

Alkaline surfactant polymer (ASP) flooding is one of the applied chemical enhanced oil

recovery (EOR) techniques that have been proven successful in field pilot tests.

Heterogeneity of rock layers in terms of permeability is known to affect the oil recovery

processes. The performance of the ASP flooding process in heterogeneous porous

medium has been studied by few researchers and these studies mainly focused on

transverse/vertical (multi-layer) heterogeneity, meaning each layer being

macroscopically homogenous itself but possessing different magnitudes of permeability

compared to other layers. Results of those studies have demonstrated that the ASP

process mitigates the heterogeneity effects. One of these studies provided valuable

insight on the impact of vertical heterogeneity on ASP flooding. However, the impact of

longitudinal heterogeneity on the ASP flooding process is not well understood, and

requires further investigation. The goal of this study is to investigate how the efficiency

of the ASP flooding process depends on the permeability alterations along the ASP flow

path.

In order to investigate the impact of longitudinal heterogeneity on ASP flooding

processes, six well controlled sand pack ASP floods were conducted in packs with

different heterogeneity configurations. Only one variable was allowed to change. All

variables which could have influence on the amount of oil recovered by the ASP

process were eliminated or equalised except for the heterogeneity. The heterogeneity in

terms of permeability variations, in the direction of flow, was the only variable in these

floods that was altered. The oil saturations in these sand packs before and after the ASP

flooding were precisely determined based on mass measurements to evaluate the

heterogeneity impact on the ASP process. It was not possible to tightly control the

microscopic heterogeneity while the macroscopic heterogeneity was reasonably

repeatable in the long sand packs (1.5 m) at least in operational terms. The phase

behaviour state of the ASP/oil system was well in the lower Winsor phase.

The concentration profiles of the ASP components and droplet size distribution of

emulsion produced in the ASP floods could aid interpreting the heterogeneity impact.

The interfacial tension (IFT) measurements of the ASP/oil system are important to

ensure the effectiveness of the process. In this study, two attempts were made to enable

the use of more convenient approaches to study the ASP flooding process. Firstly, we

iv

attempted to improve an existing spectrophotometric method for surfactant

determination in pure samples to enable the determination of the surfactant in

contaminated samples containing ASP flood effluents and some emulsion. The attempt

was partially successful, therefore, it was only used as a secondary guide to aid the

explanation of the effects of heterogeneity on the ASP EOR recovery. Secondly, a

method based on captive drop technique is proposed to enable simple and easy

estimation of ultra low IFT for semi-transparent oils. The original method requires a

sulphonated fluoropolymer coating which makes it difficult to apply; our modified

method does not have the coating with limitation to transparent and semitransparent oils.

We also employed relatively low cost optics compared to other IFT methods. IFT

estimation with this in-house-made setup could reach down to 0.002 mN/m. The size

distributions of the emulsions produced in the ASP floods were determined by the

established technique of nuclear magnetic resonance- pulsed field gradient stimulated

echo (NMR-PFG-STE).

Experimental observations and results indicated that the ASP flooding processes are

history dependent on the longitudinal heterogeneity. This thesis reveals that longitudinal

heterogeneity has obvious impact on ASP flooding and there is a preferred flooding

direction in which oil recovery is slightly improved. An extra amount of oil originally in

place is recovered when the flooding direction is coincident with the direction of a

decreasing trend of permeability. Another important observation is the existence of

some degree of dependence between the size of in-situ generated emulsion in ASP

flooding and the permeability (pore size). The results of this study, although more

experimental work is needed, could indicated to oil producers that, if well injectivity

allows, it is better to inject and flood the ASP slug from lower to higher permeability

zones for EOR maximisation.

v

Acknowledgements

Thanks to Allah for making physical knowledge accessible to human brain, the one who

made us, humans, into races and nations to know each other and interact, otherwise, a

place as big as our universe would seem empty and lonely. I hold respect and

admiration to my parents, my prime educators, for working to their sincere capacity to

see us, my siblings and me, growing respectful and become knowledge seekers. I thank

UWA for allowing me to be a citizen of its campus and giving me the chance to

undertake a PhD study. I raise my respect to the acknowledgement of UWA, that its

campus is situated on Noongars land, where I also lived for the last few years; I carry on

the same acknowledgment.

I thank my coordinating supervisor, Professor Jishan Liu, for having the courage to

supervise such a challenging multi-disciplinary topic as the ASP EOR process. His kind

non-invasive academic approach allowed this research to stay on focus and keep up

momentum all the way. I, also, thank my supervisor Dr Ben Clennell for his supervision

and dealing with the experimental aspects of this PhD, despite being extremely busy

allowed me to come unannounced. Many thanks must also go to Allan McKinley for his

supervision, training and valuable advices as well as for kindly allowing me to use his

laboratory. Thanks are due to Dr Keyu Liu of CSIRO for the kind support in CSIRO

laboratories, I have to admire his energy and high interest in research. I’m in debt to

A/Professor Farid Boussaid of UWA for his continues encouragement to keep up good

overall performance, I’m far short to thank him enough for the advices and reflections

on my work. I’m very grateful to Dr Lindsay Byrne of UWA for his kindly energetic

attitude and assistance with nuclear magnetic resonance spectroscopy. I would like to

admire and thank the high professionalism of both UWA library and Curtin University

library staff for providing the much needed books and literature for the completion of

this PhD.

I thank Petroleum Development Oman (PDO) for the generous scholarship and

sponsoring this PhD study, I’m grateful for their financial support. I thank

Commonwealth Scientific and Industrial Research Organisation (CSIRO) for allowing

me to use their facilities and laboratories. Thanks to the following companies for

providing chemicals: Stephan, Sasol North America and SNF.

This PhD has proven to be multidisciplinary, this gave me the privilege to meet and

interact with many people from different backgrounds. Bob Middleton, the elder chief

of the tribe with the white beard at CSIRO, was there when I needed a hammer or a saw

to make my day, many thanks Bob. His help to make the special cores of calcite in-situ

vi

precipitation system (CIPS) is acknowledged. Thanks go to Bruce Many and his team at

CSIRO workshops for the assistance with core flooding instruments and lending tools

when necessary. I would like to thank Brat La Greca at the National Measurements

Institute (Australia) for his advice and assistance with the analysis of one of the solid

samples. At UWA, I would like to extend my thanks to Mark Henderson and Mike Reid

at the school of Mechanical and Chemical Engineering workshop for machining the

interfacial tension cell. I also thank Charles Hammond of Sasol North America for

providing the data of interfacial tension measurements using spinning drop technique.

At Allan McKinley laboratory, I was lucky to meet his students: Majed Alotaibi and

Ramiz Boulos, all were very helpful and provided a friendly atmosphere in the

laboratory. I wish to thank my office mates; Wu Yu, Tomasz Woloszynski, and KyYu

Wang for the nice time we spend in and out of the office, the table tennis matches and

dinners out nights. I also thank my group members for the interaction and the many

activities done together especially Bashirul Haq and Zhongwei Chen.

I warmly thank those who made regular check calls just to know everything is ok.

Finally, I thank my family for the patience over my long overseas travel and the

encouragement to achieve this PhD.

vii

Table of Contents: Abstract…...…………………………………………….……………………..….iii Acknowledgments……………………………………….…...………….……..….v

List of Figures…………………………………………….……...……………...xiii List of Tables……………………………….….….…….….………………....…xix List of Abbreviations and Units…………………...…………………….….....…xx

1 Introduction ....................................................................................... 1

1.1 Importance of Crude Oil ................................................................................ 1 1.2 Oil Field Production Life............................................................................... 2 1.3 EOR Methods................................................................................................ 4 1.4 Target Oil for EOR Application..................................................................... 4 1.5 Chemical EOR .............................................................................................. 5 1.6 ASP Flooding ................................................................................................ 5 1.7 Soap-to-Surfactant Ratio................................................................................ 6

1.7.1 Important Factors in the ASP process..................................................... 7 1.8 Effects of Heterogeneity on the ASP Process................................................. 8 1.9 Thesis Objectives and Contribution ............................................................. 10 1.10 Overall Experimental Methodology............................................................. 11

1.10.1 Heterogeneity Formulation and Control ............................................... 11 1.10.2 Control Factors of the ASP Flood ........................................................ 12

1.11 Chapters Summary ......................................................................................14

2 Chemical EOR and Fluid Flow in Porous Media........................... 16 2.1 Introduction................................................................................................. 16 2.2 Fundamentals of Fluid Flow in Porous Media.............................................. 17

2.2.1 Porous Medium ................................................................................... 17 2.2.2 Porosity and Storage Capacity of Porous Medium................................ 18 2.2.3 Fluid Saturation in Porous Medium...................................................... 19 2.2.4 Wettability and Phase Distribution in Pores ......................................... 19 2.2.5 Imbibition and Drainage ...................................................................... 20 2.2.6 Residual Saturations ............................................................................ 20 2.2.7 Permeability......................................................................................... 21 2.2.8 Permeability and Porosity Correlation by Porous Media Models .......... 21 2.2.9 Darcy’s Law ........................................................................................ 22 2.2.10 Relative Permeability and End Point Relative Permeability.................. 23 2.2.11 Mobility Ratio .....................................................................................24 2.2.12 Surface and Interfacial Tension............................................................ 24 2.2.13 Capillary Length .................................................................................. 25 2.2.14 Capillary Pressure................................................................................ 26 2.2.15 Capillary Number ................................................................................ 27 2.2.16 Bond number (Buoyancy Number) ...................................................... 28 2.2.17 Trapping Number ................................................................................ 29 2.2.18 Total Acid Number and Petroleum Acids............................................. 29 2.2.19 Displacement Efficiency and Volumetric Sweep Efficiency................. 30

2.3 Heterogeneity Definition and Measures ....................................................... 31 2.3.1 Heterogeneity Measures....................................................................... 31

2.4 Surfactant Flooding ..................................................................................... 32 2.4.1 Mechanism of Oil Recovery by Surfactant Flooding ............................ 32 2.4.2 Surfactant Molecule............................................................................. 33 2.4.3 Surfactant Classification ...................................................................... 34

viii

2.4.4 Hydrophilic-Lipophilic Balance (HLB)................................................ 35 2.4.5 Micelle Formation and Critical Micelle Concentration (CMC) ............. 35 2.4.6 Solubilisation of Oil by Surfactants...................................................... 37 2.4.7 Stability of the Sulphate and Sulphonate Surfactants............................ 37 2.4.8 Surfactant Chemical Stability: Hydrolysis and Precipitation................. 38 2.4.9 Surfactant Retention............................................................................. 38

2.5 Alkaline Flooding ........................................................................................ 40 2.5.1 Oil Recovery Mechanism of Alkaline Flooding.................................... 40 2.5.2 Alkali Agents Used in EOR ................................................................. 41 2.5.3 Geochemistry Modelling of Alkaline Flooding..................................... 42 2.5.4 Alkali Consumption ............................................................................. 44 2.5.5 Dynamic Nature of IFT in Alkaline Process ......................................... 45 2.5.6 Heterogeneity Effects on Alkaline Flooding......................................... 45

2.6 Polymers Flooding....................................................................................... 45 2.6.1 Oil Recovery Mechanism in Polymer Flooding .................................... 46 2.6.2 Structure and Molecular Confirmation of Partially Hydrolysed Polyacrylamide.................................................................................................... 46 2.6.3 Polymer Flow ...................................................................................... 48 2.6.4 Polymer Stability ................................................................................. 48 2.6.5 Polymer Retention ............................................................................... 48 2.6.6 Permeability Reduction and Relative Permeability Modification.......... 49 2.6.7 Residual Resistance Factor................................................................... 50 2.6.8 Inaccessible Pore Volume .................................................................... 50 2.6.9 Polymer Impact on IFT ........................................................................ 51 2.6.10 Gelation Process ..................................................................................51

2.7 ASP Flooding .............................................................................................. 51 2.7.1 Oil Recovery Mechanisms of ASP ....................................................... 51 2.7.2 Advantages of ASP Process ................................................................. 52 2.7.3 Drawbacks ........................................................................................... 52 2.7.4 Injection Sequence of ASP Flood......................................................... 53

2.8 Emulsion and Microemulsions..................................................................... 53 2.8.1 Emulsion size and Chemical Concentration.......................................... 55 2.8.2 Permeability Reduction and Emulsion Flow in Porous Medium ........... 56

2.9 Emulsion Winsor Phase Behaviour .............................................................. 57 2.9.1 Phase Behaviour Mechanisms .............................................................. 57 2.9.2 Phase Behaviour Salinity Scans ........................................................... 58 2.9.3 Parameters Affecting the Phase Behaviour ........................................... 59 2.9.4 Solubilisation Parameters and IFT Correlation with Phase Behaviour ..59 2.9.5 Phase Behaviour and Maximum Oil Recovery ..................................... 61 2.9.6 Emulsion Electrical Conductivity......................................................... 61 2.9.7 Non-typical Winsor Phase Behaviour................................................... 62

2.10 Emulsion Droplet Size and Size Distribution ............................................... 63 2.10.1 Techniques for the Determination of Emulsion Droplets Size Distribution ......................................................................................................... 63 2.10.2 Determination of the Emulsion Size Distribution Using NMR.............. 63 2.10.3 Molecular Diffusion............................................................................. 64 2.10.4 Unrestricted Diffusion.......................................................................... 65 2.10.5 Restricted Diffusion and Emulsion Size Distribution............................ 66 2.10.6 Limitation of NMR for Droplet Size Distribution Determination.......... 69

2.11 Analytical Determination of Surfactant and Polymer ................................... 70 2.11.1 Polyacrylamide Analytical Determination Review ............................... 71 2.11.2 Size Exclusion Chromatography for Polyacrylamide............................ 72

ix

2.11.3 The N-Bromination of the Amide Group- Starch Iodide Method.......... 72 2.11.4 The Step and Mechanism of the N-Bromination Process...................... 73 2.11.5 Surfactant Determination ..................................................................... 74 2.11.6 ISO 2271 .............................................................................................74 2.11.7 HLPC for Surfactant Determination..................................................... 75 2.11.8 Spectrophotometric Methods ............................................................... 76

2.12 Impact of ASP Chemicals on Environment .................................................. 77

3 Chemical Analysis of the ASP Slug Components........................... 79 3.1 Background and Motivation ........................................................................ 79 3.2 Description of the Samples ..........................................................................80 3.3 Representative Sample and Sampling Uncertainty ....................................... 81 3.4 Beer’s Law and Spectrophotometry ............................................................. 81 3.5 The Spectrophotometer Model and Detector Linearity................................. 82 3.6 Sampling of ASP Floods Effluents .............................................................. 83 3.7 Surfactant Determination.............................................................................84

3.7.1 Spectrophotometric Method Based on Brilliant Green.......................... 84 3.7.2 Spectrophotometric Properties of Brilliant Green................................. 85 3.7.3 Brilliant Green Leuco-Base Reaction ................................................... 86 3.7.4 Essential Modifications to the Brilliant Green Method ......................... 88 3.7.5 Material Used in the Preparation of BGS ............................................. 88 3.7.6 Preparation of Brilliant Green Mother Solution.................................... 89 3.7.7 Preparation of BG Reagent Samples .................................................... 90 3.7.8 Scanning Procedure ............................................................................. 90 3.7.9 Analytical Calibration Curve................................................................ 91 3.7.10 Elimination of the Effect of NaOH Concentration................................ 92 3.7.11 Time Effects and Aging of the BGRS .................................................. 93 3.7.12 Optimisation of the Volumes of BGMS and DW in BGRS................... 94 3.7.13 95% Confidence Level and Error Determination.................................. 96 3.7.14 Emulsion Interference.......................................................................... 97 3.7.15 Polymer Interference............................................................................ 99 3.7.16 Mathematical Model to Correct for Contamination ............................ 100

3.8 Polymer Quantitative Determination.......................................................... 104 3.8.1 The Analytical Calibration Curve for Polymer ................................... 105 3.8.2 Interferences on Polymer Determination by N-Bromination Method.. 106

3.9 Measurement of the Alkali Concentration.................................................. 106 3.9.1 Surfactant and Polymer Presence Interference on pH ......................... 108 3.9.2 Crude Oil and Emulsion Presence ...................................................... 109 3.9.3 The pH Meter, Buffers, Electrode and Calibration Procedure............. 109

3.10 Fourier Transform Infra Red- Attenuation Total Reflection ....................... 110 3.11 Conclusion ................................................................................................ 113

4 The Physicochemical Properties of ASP Slug and Oil ................. 115 4.1 ASP Slug Properties .................................................................................. 115 4.2 Oils and Chemicals.................................................................................... 116

4.2.1 Chemicals Selection and ASP Slug Design ........................................ 116 4.2.2 Materials............................................................................................ 117 4.2.3 Mixing the Stag Crude and Ondina Oil 15.......................................... 118 4.2.4 Preparation of the ASP Slug............................................................... 120

4.3 Winsor Phase Behaviour of Oil 3/Surfactant System.................................. 121 4.3.1 Salinity Scan for Winsor Phase Behaviour ......................................... 121 4.3.2 Electrical Resistivity Test for Emulsion Type .................................... 124

x

4.3.3 Emulsion Resistance Measurement Procedure.................................... 125 4.4 Interfacial Tension Measurement ............................................................... 126

4.4.1 Interfacial Tension Measurement Methods......................................... 126 4.4.2 Pendant Drop..................................................................................... 127 4.4.3 Estimation of IFT Using Winsor Phase Behaviour ............................. 128 4.4.4 Motivation to Build In-House IFT Cell .............................................. 129 4.4.5 Captive Drop ..................................................................................... 129 4.4.6 Failure of Original Cell Duplication ................................................... 131 4.4.7 Simplification to Make the Method Work .......................................... 132 4.4.8 Modified Captive Drop Method ......................................................... 132 4.4.9 Camera and Optics............................................................................. 132 4.4.10 Sliding Head ...................................................................................... 134 4.4.11 Droplet Chamber................................................................................ 134 4.4.12 Illumination .......................................................................................134 4.4.13 Procedure...........................................................................................134 4.4.14 Distance Scale Calibration ................................................................. 135 4.4.15 Refractive Index................................................................................. 136 4.4.16 Image Processing and IFT calculations............................................... 138 4.4.17 Teflon Platform Lipophilicity and Contact Angle............................... 138 4.4.18 Cross-Check with Spinning Drop Method.......................................... 140 4.4.19 Measurements and Results ................................................................. 142 4.4.20 Limitation of the Method ................................................................... 144 4.4.21 Discussion of IFT Measurements ....................................................... 144

4.5 Conclusion for the Chapter ........................................................................ 145

5 ASP Floods in Homogenous and Heterogeneous Sand Packs ..... 147 5.1 Experimental Approach to Study Heterogeneity Impact............................. 147 5.2 Target Permeabilities for Chemical Flooding ............................................. 148 5.3 Experimental Work Flow........................................................................... 149 5.4 Sand Pack Preparation ............................................................................... 149

5.4.1 Materials of the Sand Packs ............................................................... 149 5.4.2 Sand Packs Dimensions ..................................................................... 151 5.4.3 Heterogeneity Construction and Configuration................................... 151 5.4.4 Sand Washing .................................................................................... 152 5.4.5 Sand Mixing and Permeability Control .............................................. 153 5.4.6 Construction of Lower and Higher Permeability Sections................... 153 5.4.7 Sand Packing Procedure..................................................................... 154 5.4.8 Sand Pack Pairs.................................................................................. 154 5.4.9 Air Removal from Sand Packs ........................................................... 156

5.5 Water and ASP Floods............................................................................... 156 5.5.1 Experimental Parameters.................................................................... 156 5.5.2 Sand Pack Flooding Setup.................................................................. 158

5.6 Flooding Procedure....................................................................................162 5.6.1 Installation and Removal of the Sand Pack on the Flooding Rig ......... 162 5.6.2 Injection Sequence............................................................................. 163 5.6.3 Pore Volume Determination............................................................... 166 5.6.4 Oil and Water Saturations Determination Method .............................. 166 5.6.5 Measurements of the Production Rates............................................... 167 5.6.6 Constant Flow Rate Control ............................................................... 167 5.6.7 Flow Impairment in the Sand Packs ................................................... 170 5.6.8 Injection System Performance During Flow Impairment.................... 171

5.7 Constant Phase Behaviour ......................................................................... 173

xi

5.7.1 Chemical Slug and Sand Stability ...................................................... 174 5.8 Results and Discussion .............................................................................. 175

5.8.1 Sand Pack Permeabilities and Porosity Repeatability Quality............. 175 5.8.2 Water Density Influence on Oil Recovery Calculations...................... 177 5.8.3 Oil Recovery...................................................................................... 177 5.8.4 Emulsion Production.......................................................................... 179 5.8.5 Phase Behaviour of Emulsion in ASP Floods..................................... 179 5.8.6 Production Rate and Oil Cut .............................................................. 180 5.8.7 Chemical Profile of the ASP Components in the Produced Water ...... 183 5.8.8 Injection Pressure Responses to ASP Flood ....................................... 186 5.8.9 Colouration of Sampled ASP Effluents .............................................. 189

5.9 Conclusion ................................................................................................ 190

6 Investigations of ASP Flooding Flow Impairment and Permeability Impact on Emulsion Droplet Size Distribution................................... 194

6.1 Background ............................................................................................... 194 6.2 ASP Flooding Flow Impairment Investigation ........................................... 194

6.2.1 Elimination of Wax and Asphaltene Deposition ................................. 196 6.2.2 Elimination of Surfactant Precipitation .............................................. 198 6.2.3 Elimination of Fine Migration............................................................ 198 6.2.4 Elimination of Polymer Plugging ....................................................... 198 6.2.5 Elimination of Polyacrylamide Polymer Gelation Process.................. 200 6.2.6 Eliminating Meshes Impact on Flow Impairment ............................... 200 6.2.7 Polymer Adsorption Contribution to Flow Impairment....................... 201 6.2.8 Emulsion Contribution to the Flow Impairment ................................. 203

6.3 Emulsion Droplet Size Distribution ........................................................... 204 6.3.1 Experimental Procedure of NMR-PFG-STE Experiments .................. 204 6.3.2 NMR Diffusions Coefficients and Signal Attenuation Results............ 205 6.3.3 Numerical Procedure of NMR Experiments ....................................... 206 6.3.4 The Results of Emulsion Droplet Size Distribution............................ 206 6.3.5 Discussion on Emulsion Droplet Size Distribution............................. 209

6.4 Average Droplet Size of In-Situ Generated Emulsion and Permeability ..... 214 6.4.1 A Proposed Explanation of the Flow Impairment ............................... 215 6.4.2 Determination of Winsor Phase Behaviour Using NMR..................... 216 6.4.3 Further Discussion on the Polyacrylamide and NMR Results ............. 217

6.5 Conclusion ................................................................................................ 218

7 General Conclusions and Proposals for Future Work................. 219 7.1 Conclusions............................................................................................... 219 7.2 Future Work .............................................................................................. 221

8 References ...................................................................................... 223

9 Appendix A .................................................................................... 236

9.1 Appendix A1: Statistical Tables Related to Brilliant Green Analytical Method 236 9.2 Appendix A2: Reagents and Procedures of the N-Bromination Method .... 238 9.3 Appendix A3: ICP-AES Analysis .............................................................. 240

10 Appendix B ................................................................................. 241

10.1 Appendix B1: Derivation of the Mass Balance Equation Used for the Determination of Water and Oil Saturations .......................................................... 241

xii

10.2 Appendix B2: Image Processing for the Measurements of Liquids Production Rates 242 10.3 Appendix B3: Tables of Chapter 5............................................................. 244

11 Appendix C................................................................................. 245

11.1 Appendix C1: MATLAB® Code to Model the Attenuation of NMR Signal in Spherical Cavities/ Emulsion Droplets .................................................................. 245 11.2 Appendix C2: Roots of the Bessel Function............................................... 247 11.3 Appendix C3: Instructions on Using MATLAB® Function lqcurvefit for the Determination Size Emulsion Droplet Size Distribution ........................................ 248

xiii

List of Figures

Figure 1-1: Adapted permeability configuration of sand packs for the control of longitudinal macroscopic heterogeneity. ..................................................................... 12

Figure 2-1: a) Packed sand grains create connected pores which allow for fluid flow and storage (Scale bar is equal to 500 µm in the left image). b) Closer zoom-in image showing the pores and pore throats.............................................................................. 18

Figure 2-2: The contact angle (θ) between the solid substrate and water drop surrounded by oil in sessile drop configuration [from Tiab and Donaldson, 2004] ......................... 20

Figure 2-3: Illustration of the curves of relative permeabilities and the end point relative permeabilities. ............................................................................................................ 23

Figure 2-4: Water rise in capillary tube by capillary forces [based on Ahemd, 2001]... 26

Figure 2-5: Typical capillary number curve and recovery of residual oil (from Austad and Milter, 2000). ....................................................................................................... 28

Figure 2-6: Sketch of a generic surfactant molecule structure [from Ottewill (1984) cited in Green and Willhite (1998)]............................................................................. 33

Figure 2-7: The CMC is the concentration where micelles start to form and the concentration of surfactant monomer remains almost constant (after Lake, 1989). ...... 36

Figure 2-8: Some physical properties show change in the vicinity of the CMC [After Preston, 1948]............................................................................................................. 37

Figure 2-9: Typical S Shaped adsorption isotherm for an ionic surfactant in an oppositely charged substrate (From Rosen, 2004) ....................................................... 39

Figure 2-10: The structure of partially hydrolysed polyacrylamide and its sodium salt [Sorbie, 1991]. ............................................................................................................ 47

Figure 2-11: Possible HPAM conformations in response to salinity [Sorbie, 1991]. .... 47

Figure 2-12: Illustration of polymer retention mechanisms in porous media [Sorbie, 1991]. ......................................................................................................................... 49

Figure 2-13: Illustrations of basic emulsion types, gray colour represent water and black represents oil [Edited from Schramm, 2005]. .............................................................. 54

Figure 2-14: Bicontinuous structure of middle phase where both oil and water are continuous [Rosen, 2004]............................................................................................ 55

Figure 2-15: Illustration of multiple emulsion structure of oil-in-water-in-oil .............. 55

Figure 2-16: Oil droplet enters pore constriction. [McAuliffe, 1973] ........................... 56

Figure 2-17: Droplet capture mechanisms in porous media [Edited from Soo and Radke, 1986] .......................................................................................................................... 57

Figure 2-18: Typical Winsor phase behaviour as a function of salinity [Based on Healy et al., 1976; Bavière et al., 1997; Green and Willhite, 1998]. ...................................... 58

Figure 2-19: Behaviour of solubilisation parameters and IFT against Salinity [from Healy et al., 1976]....................................................................................................... 60

Figure 2-20: Electrical resistivity of w/o (Phase +II) emulsion is bigger than the resistivity of o/w emulsion (Phase -II) [Edited from Healy et al., 1976) ...................... 62

xiv

Figure 2-21: PFG- CPMG-NMR pulse sequence used for the measurements of unrestricted diffusion coefficients and emulsion droplet size distribution [based on Packer and Rees, 1972] ............................................................................................... 65

Figure 2-22: Pulse sequence of NMR-PFG-STE [Adapted from Hollingsworth and Johns, 2003]................................................................................................................ 68

Figure 2-23: NMR signal attenuation curves for restricted and unrestricted diffusion as function of field gradient magnitude for o/w emulsion with different average droplet sizes for ∆=400 ms, δ= 2 ms, D (diffusion coefficient of oil) =3.75 x 10-11 m2/s. ......... 70

Figure 2-24: The restricted and unrestricted curves of w/o emulsion with given sizes for ∆=400 ms, δ= 2 ms, D (diffusion coefficient of ASP water) =2.20 x 10-9 m2/s............. 70

Figure 3-1: Linearity check of the spectrophotometer light detector. ........................... 83

Figure 3-2: The structure of the propoxylated alcohol sulphate that was used in the ASP slug, commercially known as Alfoterra® 145-S4. ....................................................... 84

Figure 3-3: The photo shows the brilliant green (green-blue) on the left and its colourless leuco-base on the right. The real colour is green-blue but the camera captured it as blue...................................................................................................................... 85

Figure 3-4: The absorbance spectrum of brilliant green in water. Note at 490 nm, there is a spectral flat zone. .................................................................................................. 86

Figure 3-5: Proposed reaction of colour restoration of BG leuco base upon addition of surfactant. ................................................................................................................... 87

Figure 3-6: Analytical Calibration Curve of BGR with sulphate surfactant.................. 91

Figure 3-7: Analytical Calibration Curve of BGRS with sulphate surfactant within linear absorbance region. ...................................................................................................... 91

Figure 3-8: The sodium hydroxide reduced the absorbance of 0.4% surfactant when low capacity borate buffer is used (solid squares), Higher capacity borate dropped the absorbance and effectively sustained the colour intensity (empty squares), the colour was maintained for weeks indicting the elimination of any possible slow side reaction.................................................................................................................................... 92

Figure 3-9: The behaviour of BGS absorbance with different surfactant concentrations for 11 minutes. The blue line is of a sample that also contain polymer......................... 93

Figure 3-10: The absorbance of 0.005% sulphate. One scan was made at 634 nm and the other at 634 nm of the same sample. For the calibration curve the maximum absorbance value around the region of 634 nm was used. .............................................................. 94

Figure 3-11: The effect of adding more BGMS on the absorbance of BGRS with different surfactant concentration, 1%, 0.1%, 0.4% and 0.7%...................................... 95

Figure 3-12: The ±95% confidence range as a percentage of the mean. A power plot is used to approximate interpolation of the 95% confidence range of remaining concentrations. ............................................................................................................ 97

Figure 3-13: The scans of three samples one uncontaminated and two contaminated with emulsion, note the absorbance at 850 and 340 (nm). ............................................ 98

Figure 3-14: The peak absorbance (at 634 nm) of contaminated and uncontaminated samples is influenced by the degree of contamination which is reflected with increase 99

Figure 3-15: The polymer effect on the absorbance of BG at different surfactant concentrations, the legend above is %w concentration of sulphate surfactant............. 100

xv

Figure 3-16: Comparison plot between real concentrations and observed concentration before and after the application of correction factor. The trend is the best fit of the corrected points (solid circles). (For S=1.25, (A340-A850) reference =0.0316).................. 104

Figure 3-17: Analytical calibration curve of polyacrylamide by N-bromination method with standards diluted from ASP slug of SP 23 (1550 ppm). ..................................... 105

Figure 3-18: Analytical calibration curve of polyacrylamide by N-bromination method with standards diluted from 1550 ppm polyacrylamide in DW. ................................. 106

Figure 3-19: Dilution of ASP slug and the pH reading............................................... 107

Figure 3-20: The pH reading as function of Sodium hydroxide concentration in water.................................................................................................................................. 108

Figure 3-21: The surfactant was easily detected with FTIR-ATR, note the characteristic peaks of sulphonate at 1050 cm-1............................................................................... 111

Figure 3-22: FTIR-ATR spectrum of polyacrylamide in water, after subtracting the background spectrum. The N-H band was detected ~1640 cm-1 but at very high concentrations........................................................................................................... 111

Figure 3-23: Analytical calibration curve obtained from sulphonate surfactant concentration and absorbance of the sulphonate groups in the FTIR-ATN spectrum.. 112

Figure 4-1: Polyacrylamide (Flopaam 3630 S) viscosity as a function of its concentration in deionised water with exponential fitting and correlation factor (R2). 118

Figure 4-2: Viscosity and density of the mixed oil (Oil 3) used in all of the sand pack floods........................................................................................................................ 119

Figure 4-3: Salinity scan of Oil 3 with 0.2% (w/v) Alfoterra 145-S4 and variable salinity, the salinity is shown in the textboxes as % NaCl (w/v). The emulsion in the tubes is 4 months old. ............................................................................................................... 122

Figure 4-4: Closer image of the two emulsions, brown and white, formed in the 7% NaCl, 0.2% (w/v) Alfoterra 145-S4........................................................................... 123

Figure 4-5 : a) Microscopic photograph of the white emulsion seen at 7% NaCl (w/v) and 0.2% (w/v) surfactant. b) Oil fluorescence (blue) shows that oil is surrounded by water (black) constituting oil-in-water emulsion........................................................ 124

Figure 4-6: Simple setup to measure resistivities of oil, ASP slug and emulsion........ 125

Figure 4-7: Pendant drop profile and input diameters for IFT calculations (adapted from Song and Springer, 1996A). ...................................................................................... 127

Figure 4-8: Illustrative Sketch of Sessile drop ........................................................... 129

Figure 4-9: The curve of the polynomial function that describes the shape factor of the sessile drop as a function of the ratio of its height to its diameter. ............................. 131

Figure 4-10: Side view schematic of the sessile drop IFT cell with the drop resting on the Teflon platform. .................................................................................................. 133

Figure 4-11: Photograph of the sessile drop IFT cell apparatus.................................. 133

Figure 4-12: Illustration of the plane of focus of a lens.............................................. 135

Figure 4-13: Calibration images of vernier scale for 1:1 lens focus, each division is 1 mm. .......................................................................................................................... 136

Figure 4-14: Bearing ball image in air and oil used to check possible optical size change................................................................................................................................. 137

xvi

Figure 4-15: Droplet age: 5 minutes. System: Dodecane against a solution of 0.05% Alfoterra 145-S4, 5.14% NaCl. Height =27 (pixel) = 50 (µm) diameter =2763 (pixel) = 5103 (µm). Oil density: 0.746 (g/mL) and surfactant solutions density: 1.032 (g/mL), temperature = 25oC. .................................................................................................. 138

Figure 4-16: Droplet age: 0.3 minutes. System: Dodecane against a solution of 0.025% Alfoterra 145-S4, 5.14% NaCl. Height =352 (pixel) = 647 (µm), Diameter =1511 (pixel) = 2791 (µm). Oil density: (0.746 g/mL) and surfactant solutions density: assumed 1.032 g/mL, temperature =25oC. ......................................................................................... 138

Figure 4-17: a) Image of a resting water drop on Teflon platform surface surrounded by oil in the sessile drop. b) Close up look of the contact angle of the Teflon surface showing that the contact angle is close to 180o. c) A processed image of image in (b) to aid visual observation of the contact angle between the black and red lines. .............. 139

Figure 4-18: Illustration of spinning drop at angular frequency ω [adapted from Tadros, 2005] ........................................................................................................................ 141

Figure 4-19: Dynamic IFT for different combinations of alkali, surfactant and polymer against Oil 3.............................................................................................................. 144

Figure 5-1: Heterogeneity configurations of the sand packs with sudden permeability change....................................................................................................................... 148

Figure 5-2: Grain size distribution of the -300 µm silica sand before sand washing, note that the primary and secondary x-axes are not equally scaled. ................................... 150

Figure 5-3: Grain size distribution of the -75 µm silica sand before sand washing, note that the primary and secondary x-axes are not equally scaled. ................................... 150

Figure 5-4: Diagram showing dimensions and configuration of the heterogeneous and the homogenous Sand Packs...................................................................................... 151

Figure 5-5: Image shows the boundary between the lower and higher permeability sections. The lower permeability section is to the left of the dark mark (on glass tube wall), while the higher permeability is to the right of the mark. ................................. 152

Figure 5-6: Permeability of the 150 cm long sand packs as a function of the ratio of the amount of -75 µm and -300 µm sand......................................................................... 154

Figure 5-7: Schematic diagram of the sand pack flood experiments setup.................. 160

Figure 5-8: Photograph of the experimental setup of the sand pack. The sand pack is fixed to the wooden base by strings and nails. The wooden base is clamped and fixed to the rig. The flow direction is upwards. ...................................................................... 160

Figure 5-9: Calibration line of motor stepping and pump discharge. Each point in the graph is an average of 5 or more measurements of discharge rate of the pump at a given stepping speed........................................................................................................... 161

Figure 5-10: Photograph of SP16 vials, with ruler as a reference. The vials contain the oil bank and emulsion. The initially transparent ASP attained a brownish colouration in samples 14 and 15. .................................................................................................... 167

Figure 5-11: An ill-controlled water flood of secondary recovery in the trial floods... 169

Figure 5-12: Images shows the configuration of the two stage pressure regulation..... 169

Figure 5-13: Well-controlled water flood for secondary oil recovery. Note that the pump pressure is set to about 520 psi. ................................................................................. 170

xvii

Figure 5-14: A trial sand pack (SP11) suffered from flow impairment after switching from ASP injection to water drive. The glass was broken because this pressure build up was not expected and no pressure protection was in place at that time. Injection pressure transducer reached its upper limit (blue line), approximate pressure reading could be taken from pump pressure (pink line). ....................................................................... 171

Figure 5-15: The two stage pressure regulation reaction to flow when the flow is impaired by sand pack heterogeneity action on ASP flood and water drive. Note that the pressure regulator is set to maintain pump pressure at 520 psi and constant flow rate.172

Figure 5-16: SP15 flooding results, which should be compare to its pair SP18. ......... 180

Figure 5-17: SP18 flooding results, which should be compared to SP15. Note there is no flow impairment in the ASP flood of SP18................................................................ 181

Figure 5-18: SP16 flooding results, which should be compared to results of SP17, but the profiles of SP17 were not obtainable. This SP16 behaves same like SP19, higher-to-lower permeability transition..................................................................................... 181

Figure 5-19: SP19 flooding results, which should be compared to SP23.................... 182

Figure 5-20: SP23 flooding results, which should be compared to SP19.................... 182

Figure 5-21: Concentrations of polymer, surfactant and NaOH in the produced water in SP15. Most of the polymer and NaOH were produced out, while the surfactant was retained. Liquids collection started after the start of ASP injection as showed by the dashed line in Figure 3-16......................................................................................... 183

Figure 5-22: Concentrations of polymer, surfactant and NaOH in the produced water in SP16. Liquids collection started after the start of ASP injection as showed by the dashed line in Figure 5-18.....................................................................................................184

Figure 5-23: Concentrations of polymer, surfactant and NaOH in the produced water in SP17. ........................................................................................................................ 184




Figure 5-27: Pressure Responses of all ASP floods for comparison. Note that SP18 and SP23 are plotted on the Pressure axis on the right side of the graph for better scale resolution. ................................................................................................................. 187

Figure 5-28: Injection pressure response of the ASP floods in homogenous cases of SP15 and SP18. Note the pressure dip at PV~ 0.4 at which switch to water drive occurred.................................................................................................................... 187

Figure 5-29: Injection pressure response of the ASP floods in heterogeneous cases of SP16 and SP17. Note the pressure dip at PV~ 0.4 at which switch to water drive occurs. The lower-to-higher case showed less pressure build up and higher EOR. The polymer used in the ASP is 3630 S, it has higher molecular weight than 3430 S. .................... 188

xviii

Figure 5-30: Injection pressure response of the ASP floods in heterogeneous cases of SP19 and SP23. Note the pressure dip at PV~ 0.4 at which switch to water drive occurs. The lower-to-higher case showed less pressure build up and higher EOR. The polymer in the ASP is 3430 S, lower molecular weight than 3630 S........................................ 188

Figure 5-31: Coloured effluent from injecting ASP slug in a blank sand pack, it is emphasised here that there was no oil in the sand pack. It also show gradual decrease in the collected water because of the flow impairment discussed earlier. ....................... 190

Figure 6-1: Pressure responses of all ASP floods for comparison. Note that SP18 and SP23 are plotted on the pressure axis on the right side of the graph for better scale resolution. ................................................................................................................. 195

Figure 6-2: Flow rate impairment in the ASP floods happened after switching to water drive.......................................................................................................................... 195

Figure 6-3: Solid particles suspended in Oil 3, image taken through the camera of the IFT cell described in Chapter 4. ................................................................................ 197

Figure 6-4 : No flow impairment in SP22 was observed during the injection 1.4 PV of ASP slug for EOR without water drive......................................................................199

Figure 6-5: Change in injection pressure to water drive after ASP flood in two sand packs of which one was saturated with DW (SP21) and was not subjected to oil saturation, the other sand pack was saturated with ASP slug and was subjected to oil saturation (SP22)....................................................................................................... 203

Figure 6-6: Observed and fitted curves of restricted diffusion of emulsion formed in the ASP flooding of the sand packs for ∆=300 ms, δ= 3.6 ms, D (diffusion coefficient of oil =3.75 x 10-11 m2/s). ................................................................................................... 207

Figure 6-7: Observed and fitted curves of restricted diffusion of emulsion formed in the ASP flooding of the sand packs for ∆=300 ms, δ= 3.6 ms, D (diffusion coefficient of water in ASP slug =2.20 x 10-9 m2/s)......................................................................... 208

Figure 6-8: Droplet size distribution of emulsion produced in ASP floods in the heterogeneous sand packs (SP16, SP17, SP19 and SP23) using NMR-PFG-STE....... 208

Figure 6-9: Droplet size distribution of emulsion produced in ASP floods in the homogenous sand packs (SP15 and SP18) using NMR-PFG-STE. ............................ 209

Figure 6-10: EDSD based on image processing of emulsion images of SP15 and SP18. Only about 150 droplets were analysed in each of these two emulsions and the histograms are plotted to show the actual size ranges. ............................................... 212

Figure 6-11: An image showing the emulsion of SP18 with clear evidence of multiple emulsions. Note the much smaller droplets within the larger droplets. ....................... 212

Figure 6-12: An image showing the emulsion of SP15. ............................................. 213

Figure 10-1: Correlation line between liquid volume and liquid height in the 3.5 mL glass vials which were used to collect produced fluids............................................... 242

xix

List of Tables

Table 2-1: Classification of surfactants based on head charge ..................................... 34

Table 3-1: pH reading from pure and oil contaminated samples ............................... 109

Table 4-1:Change in apparent drop width and height in oil compared to air as seen by the camera lens ......................................................................................................... 137

Table 4-2: Sessile drop IFT results compared to spinning drop IFT measurements of dodecane against Alfoterra 145-S4 solutions at temperature of 25 oC* and NaCl concentration of 5.14 % (w/v) ................................................................................... 142

Table 4-3: IFT between different combinations of ASP chemicals and Oil 3 or Ondina 15 ............................................................................................................................. 143

Table 4-4: Comparison of capillary number (Nc) in Sand pack floods ...................... 145

Table 5-1: Viscosities and Densities of ASP slugs at start of each ASP slug.............. 157

Table 5-2: Porosities, mass gradients and Permeabilities of Sand Packs .................... 176

Table 5-3: Secondary oil recovery and ASP EOR results of the Sand Packs. Note: Polymer 3430S was used in the ASP slug of SP19 and SP23 pair While Polymer 3630S was used in SP17 and SP16 pair and the homogenous pair. ....................................... 178

Table 5-4: Amounts of emulsion produced in ASP floods of the Sand Packs. ............ 179

Table 5-5: Sand packs masses before and after different flooding stages ................... 192

Table 5-6: Oil recovery calculations based on Table 5-5 and the lengths of sand packs sections..................................................................................................................... 192

Table 5-7: Sand pack dimensions, porosities and mass gradients............................... 193

Table 6-1: NMR-PFG-STE attenuation of oil peak.................................................... 205

Table 6-2: NMR-PFG-STE attenuation of water peak ............................................... 205

Table 6-3: Mean droplet diameter and distribution width obtained from curve fitting based on oil NMR signal (Oil-in-Water emulsion) .................................................... 207

Table 6-4: Mean droplet diameter and distribution width obtained from image processing of emulsion of the homogenous sand packs (SP15 and SP18) .................. 211

Table 9-1: Absorbance of BGRS with different surfactant and polymer concentrations................................................................................................................................. 236

Table 9-2: Statistical processing of the data in Table 9-1.......................................... 236

Table 9-3: Absorbance of BGRS with different surfactant and polymer concentrations................................................................................................................................. 237

Table 9-4: Statistical processing of the data in Table 9-3.......................................... 237

Table 9-5: ICP-AES Analysis of several samples from water and ASP floods* ......... 240

Table 10-1: Relative concentration of metals which were detected in the sample of residues collected from the container of Stag Crude using ICP-AES * ...................... 244

xx

List of Abbreviations and Units

Abbreviations BG: brilliant green 1D: one dimensional 3D: three dimensional DW: Deionised water EDSD: emulsion droplet size distribution EOR: enhanced oil recovery GOIP: gas originally in place I.D.: internal diameter ICP-AES: Inductive Coupled Plasma- Atomic Emission Spectroscopy IFT: interfacial tension min: minutes N.A: not applicable N.D: not determined NMR-PFG-STE: Nuclear Magnetic Resonance- Pulsed field Gradient- Stimulated spin-echo O.D.: outer diameter OHSE: Occupational Health Safety and Environment OOIP: original oil in place Pinj: injection pressure ppm: particles per million= milligram of a substance in one litre of the solution PV: pore volume qt: total flow rate SP: sand pack SPE: society of petroleum engineers STE: stimulated spin echo TAN: total acid number Th: temperature T1: spin-lattice (longitudinal) relaxation time of nuclear magnetic spins T2: spin-spin (transverse) relaxation time of nuclear magnetic spins Unites cm: centimetre cP: centipoise= mPa.S D: Darcy=0.9869 x 10-12 m2 Dalton: unified atomic mass unit = 1.66×10−27Kg

G: Gauss = 10−4 Tesla (magnetic field unit) g: grams Kg: kilogram L: litre= 1000 mL m: meter M: molarity = moles of a substance in one litre of the solution mD: millidarcy = 1x10-3Darcy mg: milligrams mm: millimetres

xxi

MΩ: Ohm as defined by Ohms law MHz: mega Hertz minutes: minutes of time mL: millilitre mN: milliNewton N:Newton psi: pounds per inch square s: seconds of time T: Tesla = 1 N s/(C m) (T is magnetic field unit and C is charge unit coulomb)

1

1 Introduction

This chapter gives the background of enhanced oil recovery processes and presents the

Alkaline Surfactant Polymer process. The chapter also describes the thesis objectives,

contributions and structure along with the adopted experimental methodology.

1.1 Importance of Crude Oil Crude oil is a scarce resource and achieving a sustainable supply of this resource is

vitally important for the contemporary world economy (Park, 1976). The continuous

demand for crude oil and its price volatility in the global market is self evidence of its

importance. Oil production is the main export of several countries and their main source

of revenue. Continuous and stable oil production is very important for such countries to

provide and sustain a secure and decent living style for their citizens (Maachou, 1982;

Sherbiny and Tessler, 1976). However, crude oil is a limited resource and some

countries have started to see their oil production declining. For example, in the Arab

world, Oman has passed its oil production peak and Enhanced Oil Recovery (EOR)

methods are becoming a routine activity in the Omani oil industry to sustain economical

oil production for a longer time (Al-Adawy, Nandyal, 1991; Al-Mutairi and Kokal,

2011; Stoll et al., 2010).

Conventional oil recovery methods like water flooding and gas injection only recover a

portion of the crude oil initially in reservoirs. Substantial amounts of such oil are left

behind and could be further exploited by EOR (Thomas, 2008). In addition, the rapid

increase in global population and the present technological limitations of utilising other

energy resources have accelerated the development of several unconventional methods

to recover crude oil. Consequently, over the decades, several methods were tested,

evaluated and continuously developed to improve oil recovery. These methods are

collectively known as Improved Oil Recovery (IOR) (Taber et al., 1997A). A subset of

such methods that require direct engineering intervention into the reservoir flow process

is the Enhanced Oil Recovery (Thomas, 2008). Alkaline surfactant polymer flooding

(ASP) is one of the chemical EOR techniques that have received increased attention

during the last decade (Sheng, 2010). Despite the large body of research on ASP

Chapter 1: Introduction and Thesis Objectives

2

process, this process is still not fully understood and still considered a complex process

(Liu et al., 2008; Mohammadi et al., 2009; Weatherill, 2009). This PhD thesis stems

from the need to improve our understanding of the ASP process. More specifically,

heterogeneity of the rock formation plays an important role in oil recovery (Ahmed,

2001; Green and Willhite, 1998). The impact of the lateral heterogeneity on the ASP

process was studied systematically in only one publically published paper and no single

paper was found on the longitudinal heterogeneity. The term ‘longitudinal

heterogeneity’ used in this thesis refers to the variations of permeability in a direction

parallel to the direction of the fluid flow in the porous medium. The effects of

longitudinal heterogeneity are the main focus of this thesis.

1.2 Oil Field Production Life

Oil field life consists of four phases namely: 1) discovery and appraisal, 2) development,

3) production and finally 4) abandonment. During the discovery and appraisal phase,

the hydrocarbon reserves are estimated; original oil in place (OOIP) and gas originally

in place (GIIP). Then, the development phase begins, during which, the number of wells

and surface facilities for the optimum production are decided. The production phase

then starts during which the oil is recovered. The production and development phases

could overlap to expedite oil production and provide early revenue. In the production

phase, generally there are three possible oil recovery stages which are classified based

on the main active driving mechanism of oil recovery in the reservoir. The common

classifications of these stages of oil recovery are called: primary recovery, secondary

recovery and tertiary recovery (Green and Willhite, 1998). These stages could overlap

to optimise the economics of the oil field. After the production phase is completed, the

oil field abandonment comes and the wells are shut permanently.

• The primary recovery comes in the early life of the field. In this stage, the

reservoir may have enough energy to push reservoir fluids from the reservoir

formation to the surface. The driving mechanisms in the primary recovery could

be one or combinations of the following: reservoir pressure, solution gas

depletion, gas cap expansion, formation compaction, gravity drive, and/or

aquifer encroachment. This stage can typically recover 5-10% of OOIP. This

recovery can go higher when a strong aquifer is supplying energy to the

reservoir.


3

• The secondary recovery is introduced when the reservoir pressure (natural

energy) is not large enough to push oil out of the formation and to the surface.

As production continues, the production rate may gradually decrease and

eventually halt altogether. The field operators may not wait until the production

stops or sharply decreases in the primary recovery, but will probably implement

secondary recovery while the primary recovery stage is still active. In some

situations, it is preferred to start the secondary recovery mechanism in the first

production stage especially when the initial pressure of the reservoir is low. The

driving mechanism in the secondary recovery usually involves the injection of

water or gas into the reservoir. Lift mechanisms of reservoir like gas lift or

submersible electrical pumps are likely to be employed during the secondary

recovery. If reservoir conditions allow, then gas can be injected as a secondary

recovery technique. It is usually injected to the gas cap if one exists or just

below the reservoir cap rock (reservoir seal). It aims to maintain reservoir

pressure and slowly push oil downwards to the perforations of production

well(s). In case of water flooding, the water would be injected into the oil

bearing formation in the reservoir with the aim to sweep the formation and

displace oil to producing well(s) as well as to maintain reservoir pressure. At

some later stage the secondary recovery reaches an economical limit, and the

production becomes economically unjustified. This recovery stage can roughly

recover 20-50% of OOIP.

• Tertiary Recovery comes after the secondary recovery stage has been

completed. Currently, the only way to further produce more oil from a reservoir

after secondary recovery has been completed is EOR. This explains this

interchangeable use of the term tertiary recovery with EOR. If no EOR is

planned for the oil field, it is most likely that the field will be abandoned.

• EOR could be applied at any stage of the oil production phase: primary,

secondary or tertiary recoveries. In some cases, operators may implement the

EOR as a secondary recovery mode (Wyatt et al., 2004). EOR may involve

further development and capital investment, which could be substantial. The

recovery from this stage has to be above a certain economical limit to justify the

cost which is largely decided by the market price of crude oil barrel (Taber et

al., 1997B).


4

1.3 EOR Methods

EOR processes apply different techniques to extract further amounts of crude oil from

the reservoir rock. There are miscible, thermal and chemical EOR processes (Green and

Willhite, 1998). Some authors may have some reservation on this terminology. They

pointed out that the term “tertiary recovery” should be reserved for EOR projects when

the secondary recovery of the field has been completed (Taber et al., 1997A). This

probably indicates there is a degree of confusion on the classification of what is

regarded as EOR. Lake (1989, pp.1) gave a straightforward definition of EOR: “EOR is

oil recovery by injection of materials not normally present in the reservoir”. From the

above definition, the tertiary recovery is a special case of EOR while EOR could be

applied in any recovery stage provided it is economically and technically viable.

1.4 Target Oil for EOR Application

EOR targets trapped oil which could be in water flooded zones or partially flooded

zones. Crude oil is stored in reservoir rock inside the pores. After water flooding,

significant amounts of oil remain in the reservoir as mentioned above. There are several

factors that can hinder full recovery of oil. Two such factors are the heterogeneity and

capillary forces within the porous medium. Capillary forces resulting from interfacial

tensions (IFT) may continue to trap the oil in these pores in patches/drops/

discontinuous ganglia during the water flooding (secondary oil recovery), making the

oil difficult to recover. This oil is deemed as unmoveable OOIP or unrecoverable oil.

Although, the absolute pore size may not affect the amount of oil trapped, these

capillary forces are the primary reasons for the high percentages of OOIP remaining in

swept areas of the reservoirs after the primary and secondary oil recovery stages

(Chatzis et al., 1983).

If one manages to reduce the IFT between the oil and the flooding water to an ultra low

value in the order of 0.001 mN/m, then the resulting capillary forces will virtually

vanish and then it would be easier to move the oil that was initially deemed as

unmoveable or unrecoverable, thus, increasing the oil recovery (Austad and Miller,

2000). Another reason for the remaining high percentage of OOIP could be the

heterogeneity of the reservoir rock. This would inherently reduce the sweep efficiency

of the water flood leaving considerable amount of reservoir rock unswept by water


5

flood. In the same manner, if one increases the water flood sweep efficiency, then the

ultimate oil recovery will increase.

1.5 Chemical EOR

Chemical EOR processes involve: improving the sweep efficiency by improving the

mobility control, reducing IFT (Green and Willhite, 1998) and to some extent the

alteration of reservoir’s rock wettability to a favourable wetting state (Nasr-El-Din et al.,

1992). Sweep efficiency can be achieved by increasing the viscosity of water by adding

a water soluble polymer. Reduction in the IFT could be achieved by adding surfactant

and alkali to the flooding water. Thus, to simplify the process, one could combine the

three chemicals to improve the sweep efficiency and reduce the IFT. This is essentially

the ASP process.

1.6 ASP Flooding

ASP flooding is one of the applied chemical EOR methods (Sheng, 2010). ASP has

been proven to be more cost effective and simpler compared to the binary injection of

chemicals (French, 1996; Sheng, 2010). ASP flood or process may refer to the

sequential injection of alkali, surfactant and polymer or alkali/surfactant slug followed

by polymer (French, 1996). ASP process or flood in a more contemporary sense refers

to the injection of a mixture of ASP chemicals in one slug. It is this concept of ASP

flooding that is investigated in this thesis.

The recovery mechanisms of ASP slug are discussed in Chapter 2. In brief, the alkali

reacts with oil acids to produce in-situ soaps and acts synergistically with the injected

surfactant to lower the IFT to ultra low values which help to reduce the capillary forces

trapping the oil. The polymer improves the mobility control of the flood. More recently,

its viscoelastic behaviour has been demonstrated as a contributor to oil recovery from

oil blobs trapped in pores based on laboratory scale experiments (Urbissinova et al.,

2010) as well as field experiences (Wang et al., 2011). This very recent view of the

polymer recovery mechanism conflicts with the common view that the polymer does

not mobilise residual oil and only improves the sweep efficiency (Lake, 1989).


6

The ASP flood is relatively cost effective compared to other chemical techniques. For

example, polymer flooding will cost more than US$10 per barrel recovered compared

with a rough cost of 2-5 US$ per barrel recovered by the ASP flooding (Wyatt et al.,

2002; Chang et al., 2006). China appears to be a major contributor to the field pilot tests

and evaluation studies on ASP floods (Chang et al., 2006). Daqing oil field in China is

famous for such studies.

Works on ASP process extend from colloidal science, surfactants, polymers, surface

and interface, fluid flow to emulsion flow to name few areas. Since the late 1990’s, a

significant number of papers have been published on the ASP process including:

Field pilot tests (Chang et al., 2006 ; Qu et al., 1998; Wyatt et al., 2004),

Finding and testing new chemicals for more efficient ASP slugs (Iglauer et al.,

2010; Levitt et al., 2009; Berger and Lee, 2006),

Overcoming the salinity effects on ASP chemicals (Flaaten et al., 2010; Berger

and Lee, 2006),

Study the stability of emulsion produced by ASP floods (Deng et al., 2002),

Find emulsion breakers (de-emulsifiers) for these emulsions (Nguyen et al.,

2011),

Modelling and simulation of ASP process (Bhuyan, 1989; Delshad et al., 2002;

Mohammadi et al., 2009; Delshad et al., 2011),

Scale inhibitors during the ASP process (Cao et al., 2007),

Polymer stability in ASP slug (Levitt et al., 2011),

Chromatographic separation of ASP components (Wang et al., 2009; Li et al.,

2009),

Surfactant adsorption in ASP process (Hou et al., 2005),

Investigation of simpler ways to design optimum ASP slug (Flaaten et al., 2009).

Mechanisms of oil recovery of trapped oil in pores of transparent micro-models

(Tong et al., 1998; Liu et al., 2002).

1.7 Soap-to-Surfactant Ratio

A remarkable study on ASP process is attributed to Hirasaki’s team (Liu, Zhang, Yan,

Puerto, Hirasaki, and Miller, 2008; Liu, Li, Miller, Hirasaki, 2010). They showed the

importance of soap-to-surfactant ratio in the ASP process. Rosen realised that certain


7

ratios of two surfactants (one short and one long chain) are effective in reducing the IFT

between oil and water (Rosen, Wang, Shen, and Zhu, 2005). This is very similar to the

observation of Hirasaki’s team that the ultra low IFT in the ASP/oil system is governed

by the ratio of in-situ generated surfactants (soap) to the injected synthesized surfactant.

The difference between the work of Rosen’s and Hirasaki’s teams lies in the source of

the shorter chain surfactant. Rosen’s team added the surfactant to their solution while in

the ASP process, tested by Hirasaki’s teams, the shorter surfactant was brought in by

the generation of in-situ-surfactant (soap) by the injection of the alkali. This ratio leads

to the generation of soap/surfactant gradient in the ASP process, which maintains lower

IFT over a wider salinity window. This gave an explanation on the observed higher

ability of the ASP process to recover more oil compared to surfactant or alkaline

standalone flooding.

1.7.1 Important Factors in the ASP process

There are several factors affecting the oil recovery in ASP process. Naturally, the

important factors for each standalone process of water flooding, alkaline flooding,

surfactant flooding and polymer flooding are inherited into the ASP process. Standalone

process of alkaline flooding, surfactant flooding and polymer flooding are discussed in

Chapter 2. The ASP process is rather complex, with the most important variables being

(Sheng, 2010; Ahmed, 2001; Green and Willhite, 1998; Lake, 1989):

ASP slug size and composition

Target oil composition

IFT between oil and ASP slug

Phase behaviour

Flow rate

Heterogeneity of the target porous medium

Loss and consumption of ASP chemicals to porous media and stagnant oil

Salinity and hardness of the formation water

Temperature

Residual oil saturation at start of flood

Nature of the porous medium


8

1.8 Effects of Heterogeneity on the ASP Process The ultimate recovery in oil recovery processes, whether primary or secondary, is

generally affected by the reservoir heterogeneity (Ahemd, 2001). Similarly, chemical

EOR is affected by the heterogeneity level of the porous medium formation (Gupta et

al., 1988).

Wright et al. (1987) showed both experimentally as well as by simulation that

heterogeneity negatively impacts on chemical flooding performance. Their work was

based on a 2D physical model of stratified glass beads layers. The boundaries between

the layers were in communication. This later work showed that the chemical slug size

should be at least of about 1 pore volume (PV) to withstand mixing and dilution

imposed from the heterogeneity. In practice, 1PV of chemical slug including surfactant

is expensive for real reservoir. Their work underlines the possible negative impact of

transverse heterogeneity on chemical flooding.

Arihara et al. (1999) stated that a minor heterogeneity in parallel cores complicates the

ASP process outcome. Gupta et al. (1988) showed the effects of heterogeneity on

chemical flooding by simulation. They found that recovery decreases with increasing

permeability contrast between layers. They also showed the significance of the salinity

gradient effect on the chemical flooding.

More recently, Shen et al. (2009) conducted a more specific experimental study on the

ASP flooding. They monitored the ASP process performance in a heterogeneous

physical model. They used a physical model with three isolated layers of sand packs

which shared a common injection inlet and a common production outlet. They

monitored the oil, water and ASP flow through the sand pack layers by saturation

probes and differential pressure transducers. They found that the ASP helps to rectify

the flood front by getting first into higher permeability layers. The ASP slug also forms

oil bank/microemulsion thereby increasing the entry pressure to that layer. As a result,

the ASP slug moves to other layers with lower permeability, which in turn increases oil

recovery.

Shen et al. (2009) investigated the ASP performance in a heterogeneous configuration

based on a vertical varying permeability transverse to the fluid flow direction. Their

study was effectively focused on the performance of ASP in lateral heterogeneity


9

(transverse) and did not investigate the longitudinal heterogeneity (parallel to the fluid

flow). Although Arihara et al. (1999), pointed out the negative impact of heterogeneity

on the ASP process in parallel core floods, they did not effectively study the effects of

the heterogeneity on the ASP process.

Wang et al. (2009) studied the chromatographic separation of flowing ASP slug

components in a homogenous long channel. The channel had a maze-like shape of 600

cm long and an area of 0.6 cm by 0.8 cm. It was filled with a mixture of 90% quartz

sand and 10% clay before being saturated with water. The permeability of the channel

was about 841 mD. The ASP slug injected was 0.3 pore volumes (PV), polymer was

then injected and pushed by water drive. The separation and loss of chemicals were

noticeable, but they did not report any oil recovery results to relate the effect of this

relative disintegration of the ASP slug to EOR.

A number of studies on the chemical flooding performance in heterogeneous layers

have been reported above. None of the prior studies specifically targeted the

heterogeneity effects on ASP process apart from the work of Shen et al. (2009) which

primarily focused on the performance of the ASP process in transverse/vertical

heterogeneity. They reported, to the best of our knowledge, the only experimental study

on the performance of the ASP process in a deliberately pre-set vertically heterogeneous

porous medium but there is no study on the heterogeneity effects on the ASP process

within one layer (longitudinal heterogeneity).

The transverse heterogeneity may be intuitively perceived to have more impact on the

chemical EOR process than the longitudinal heterogeneity. This may explain why most

previous work focused on study transverse heterogeneity (i.e. mimicking layered

reservoirs).

The heterogeneity within one layer may affect the chromatographic separation of the

ASP chemicals, the formation of oil bank and the flow of emulsion, and thus the

enhanced oil recovery. The ASP is slightly different from the other chemical methods as

it involves the co-injection of three chemicals and the efficiency of the process could be

affected by the co-existence of these three chemicals.


10

1.9 Thesis Objectives and Contribution

It seems all previously published works ignored the impact of longitudinal

heterogeneity (in terms of permeability) on the ASP process, this is because:

• The complexity of the ASP process makes it difficult to separate between the many

variables controlling the process.

• The reservoir vertical heterogeneity has been perceived to have more impact on

chemical EOR process than its longitudinal heterogeneity (Shen et al., 2009, Green

and Willhite, 1998; Ahmed, 2001).

The objective of this research is to fill this knowledge gap and improve our

understanding of the ASP process by addressing the following questions:

• Does the efficiency of ASP process depend on longitudinal heterogeneity?

• How does the longitudinal heterogeneity influence the ASP process?

• Does it make a difference to flood from a lower permeability to a higher

permeability or vice versa?

To address these questions, it was necessary to overcome several challenging research

milestones, which resulted in several experimental contributions, most notably:

o The formulation of a successful ASP slug that can be use in EOR process.

This was achieved by considering: Oil composition and physical properties,

sand nature, temperature, salinity and anticipated permeabilities in the

porous medium. The slug was effective in reducing the IFT with the

targeted oil and was successful in mobilising trapped oil left after

secondary recovery (water flooding).

o The enhancement of flooding setup design to allow three phase injection of

oil, water and ASP slug. Originally, the system was only designed for one

phase injection with no proper control over the injection rates at low


11

injection pressures. Several modifications were made to this setup to

enable flooding at low constant injection rates suitable for EOR

experiments such as ASP flooding.

o Design of a low-cost in-house-made apparatus for the estimation of ultra

low IFT. IFT measurements or estimations between oil and chemical slugs

are important to ensure that the chemical slug is effective in EOR process.

The cost of a typical IFT cell would exceed $40000. The proposed

apparatus is easy to build and contributes an alternative to researchers who

need quick estimations of ultra low IFT.

o A method for the determination of sulfate and sulfonate surfactants based

on the brilliant green dye was improved. The success of the analytical

method was limited, however, it showed valuable information on the ASP

flooding.

1.10 Overall Experimental Methodology As mentioned earlier, the overall objective of this thesis is to evaluate the impact of

longitudinal heterogeneity alone (in terms of permeability) on the performance of the

ASP process. The performance is mainly evaluated by the oil recovery in the process. In

order to achieve this, all variables in the experiment should be kept constant across

several runs, except for the longitudinal heterogeneity. The oil recovery in each run

should then be evaluated. It is common to use sand packs for the study of EOR

processes including ASP floods (Wu et al., 2010; Ma et al., 2007; Hou et al., 2005;

Wang et al., 2009; Liu et al., 2008). This study will also be carried out using carefully

prepared sand packs.

1.10.1 Heterogeneity Formulation and Control

The heterogeneity was introduced in terms of permeability change in the experiments of

water and ASP floods. Macroscopically homogenous and heterogeneous sand packs

were made, following the designs shown in Figure 1-1. The homogenous sand packs

were packed with one sand type along the whole tube length so as not to exhibit

changes in permeability. On the other hand, the heterogeneous sand packs were packed


12

into halves, each half with a different sand to provide permeability variation along the

fluid path. One sand was selected to construct higher permeability zones, while, a

mixture of sands was used to construct lower permeability zones. The water and ASP

floods where then injected in the direction of increasing or decreasing permeability as

shown in Figure 1-1.

Figure 1-1: Adapted permeability configuration of sand packs for the control of longitudinal macroscopic heterogeneity.

The sudden permeability transitions from lower-to-higher or lower-to-higher may not be

a precise mimic of the reservoir. Nevertheless, it should reflect some aspects of the

behaviour of ASP flooding in presence of a longitudinal permeability change.

1.10.2 Control Factors of the ASP Flood

There are several experimental variables that can affect the ASP process performance,

including flow rate, ASP slug size and composition, oil type and composition,

temperature and several more which have been listed in Section 1.7.1. In order to study

the effects of longitudinal heterogeneity alone on ASP process efficiency, the following

tasks were conducted:

Legend: Low Permeability High Permeability

Flow direction

Low-to-High Permeability High-to-Low Permeability High Permeability Low Permeability

Heterogeneous Heterogeneous Homogenous Homogenous

1.5 m


13

1) Macroscopically heterogeneous silica sand packs were built with repeated and

controlled macroscopic heterogeneity in terms of permeability variation with high

permeability-to-low permeability transition, low permeability-to-high permeability

transition. Macroscopically homogenous sand packs were built either with high

permeability or low permeability. These sand packs were made in pairs with

similar predefined heterogeneity. Figure 1-1 shows an illustrative sketch of these

sand packs (Chapter 5).

2) These sand packs were saturated with both deionised water (DW) and model oil

that contained some naturally occurring acids in crude oils. Secondary water

floods and EOR ASP floods were applied vertically to reduce gravity effects

(Chapter 5).

3) The oil recovery before and after the ASP flood was measured for these different

heterogeneity configurations. The impact of these longitudinal heterogeneities on

the EOR was then evaluated.

4) The concentration of ASP chemicals was measured in the produced water after

ASP flooding.

5) The pressure response of the ASP flood was monitored.

6) The size of produced emulsion of the ASP flood was measured.

In order to relate the results to the impact of the heterogeneity without ambiguity and

eliminate or mitigate the impact of other variables of the ASP process, the following

precautions were taken:

1. Ensure that the process is actually an ASP process where the polymer, alkali and

surfactant are all engaged in the process. For example, if there are no natural

acids in the oil, then the presence of the alkali would not produce in-situ

surfactants (soaps), reducing the process to surfactant/polymer flood. (Chapter 4)

2. Ensure that ultra low IFT is achieved because of the combined action of the

alkali and surfactant. (Chapter 4)


14

3. Ensure that the phase behaviour of the system is the same for all floods. In this

study it was kept at lower Winsor phase behaviour (Chapter 4 an 5).

4. Perform well controlled ASP floods in EOR mode, keeping the same amount of

injected water and ASP slug. (Chapter 5)

In addition to the above variables of ASP process, there are three other variables which

can possibly affect the flooding experiments outcome whenever sand packs are used.

They are: the amount of sand packed in the sand pack, the temperature and sand pack

inclination. Therefore, in all sand packs, care was taken to ensure that all packs have the

same sand mass gradient (mass in grams per centimetre). In addition, all floods were

applied in a vertical configuration. Therefore, inclination effects on recovery were

eliminated. Temperature was not controlled but was kept within room temperature.

Several ASP floods were performed for different heterogeneity configurations. In each

ASP flood, all experimental variables were kept constant except for heterogeneity.

Therefore, any changes to the final oil recovery and ASP process performance in

the different runs could be mainly ascribed to the impact of heterogeneity.

1.11 Chapters Summary The thesis consists of seven chapters including this introduction chapter.

Chapter 2 presents the technical background necessary to understand and explain some

of the findings found in the experimental chapters of the thesis. The ability of the

porous medium to transmit and trap fluids is reviewed. Chemical EOR methods are

presented, including further insight into the ASP process. Emulsion formation and flow

in porous media is included because it is a common by-product of chemical floods. The

literature of emulsion droplet size distribution using nuclear magnetic resonance

techniques is also covered. The potential impact of ASP chemicals on the environment

are briefly discussed at the end of the chapter.

Chapter 3 gives the details of the analytical methods adapted and improved to analyse

the concentration of ASP components in the effluents from the ASP floods. Fourier

transform infrared- attenuation total reflection (FTIR-ATR) method was trialled to

determine simultaneously concentrations of both polymer and surfactant. The method


15

and its experimental limitations are reported. A spectrophotometric method based on

brilliant green dye was improved to measure the surfactant concentration. The success

of the method was limited. The steps and procedure to determine the polymer and

alkaline are described. The application of the proposed analytical methods on ASP

floods are reported in Chapter 5.

Chapter 4 describes the steps and chemicals used to make the ASP slug. It reports the

measurement of IFT and the development of a simple in-house-made sessile drop IFT

cell using superhydrophobic Teflon surface. It also details the determination of the

phase behaviour of the ASP component. The IFT results and the phase behaviour are

reported in this chapter. In this chapter, it is shown that the ASP slug is stable and

successful to reduce the interfacial tension between oil and water to ultra low values

which qualify the ASP slug for EOR application.

Chapter 5 details the experimental setup and execution of ASP flooding in

heterogeneous/homogeneous sand packs. The packing process of the sand is also

reported. The methods which were used to control experimental parameters such as the

injection rate of the flooding experiments are presented. The results and observation are

reported in the chapter including the results of the methods from chapter 3 and 4. The

chapter shows the oil recovery profiles from water flooding and ASP flooding, the oil

cut, the chemical profiles in produced fluids and injection pressure responses. In this

chapter, these experimental observations are used to evaluate the impact of longitudinal

heterogeneity

Chapter 6 presents the possible mechanisms that may be at the origin of the flow

impairment observed in the experiments of Chapter 5. In addition, Chapter 6 describes

nuclear magnetic resonance techniques used to determine the distribution of the size of

the emulsions’ droplets (EDSD) produced during the ASP floods. Finally, the chapter

discusses the relationship between the EDSD of the in-situ generated emulsions and

permeability.

Chapter 7 concludes this work and summarises the main findings of this PhD thesis.

Further future works are also suggested. The chapter highlights the knowledge

generated from this research.

16

2 Chemical EOR and Fluid Flow in Porous Media

This chapter presents the essential background knowledge and concepts related to

chemical EOR. It starts with fluid flow in porous medium and some physical properties

of fluids and porous medium which affect the oil recovery. The heterogeneity of porous

medium is defined and its common measures are presented. The chapter reports the

generic methods of EOR chemical flooding: alkali, surfactant and polymer as well as

further insight on ASP flooding. The properties and structures of chemicals involved in

ASP flooding are discussed. This chapter also discusses emulsion formation, emulsion

flow in porous medium, phase behaviour of emulsions, and emulsion droplet size

distribution. The impact of the ASP chemicals on environment is discussed.

2.1 Introduction

As mentioned in Chapter 1, the research on ASP flooding extends to several disciplines,

thus, Chapter 2 covers a wide range of literature related to this PhD research. As a result,

reader may find it easy to loss focus and loss the point on how this literature is related to

the core of this study. This introduction is intended to help the reader to have a bigger

picture of Chapter 2 and relate the different subjects covered to the focus of this study.

The main theme of this study, as stated in Chapter 1, is the impact of porous medium’s

longitudinal heterogeneity (in terms of permeability variation) on the efficiency of the

ASP EOR process. The flow of ASP slug in porous medium is governed by the classical

Darcy’s Law. Therefore, fundamentals of fluid flow and storage in porous media and

several concepts related to chemical flooding including interfacial tension and

mechanism of oil trapping and recovery are presented in the first section of this chapter.

Since heterogeneity of porous medium is central to this study, a separate section is

dedicated to the definition of heterogeneity and its common quantifying measures.

Further to this, the reader would probably need some background on the nature of the

chemicals used in the ASP flooding.

Chapter 2: Chemical EOR and Fluid Flow in Porous Media

17

The ASP slug could consist of three or more chemicals which could be used in

standalone floods such as surfactant flooding, alkaline flooding and polymer flooding.

A separate section was dedicated to each of these standalone floods include the recovery

mechanisms and the chemicals structures. Then, further insight into the ASP flooding is

presented in a separate section. The ASP slug promotes ultra low interfacial tension

between oil and the ASP slug, as a result emulsion may form. Therefore, a section in

this chapter presents what is an emulsion and reports literature on the flow of emulsions

in porous medium. Furthermore, the structure of emulation could be governed by

Winsor phase behaviour, thus, a section on this subject is included. The structure and

droplets size distribution of emulsions produced in the ASP flooding process are needed

to understand the performance of the process. A section on the determination of

emulsion droplet size distribution is presented in this chapter. Also, the concentrations

of the chemicals produced water during the ASP floods can help interpreting the impact

of heterogeneity on the process, thus, a section on analytical methods used to determine

the ASP slug chemical is included in the chapter. Finally, the environment is becoming

a global concern. There is a remote concern that the ASP slug could contain chemicals

which may or may not have some impact on the environment. The reader environmental

consciousness is thus invoked by adding a section on the possible impacts of ASP slug

chemicals on the environment and human health.

2.2 Fundamentals of Fluid Flow in Porous Media

Petroleum oil recovery processes take place underground and involve multiphase flow

in porous medium, except for some places where extra heavy oil (oil sands or bitumen)

is recovered by surface mining (Schramm et al., 1984). Understanding of fluid flow in

porous media is thus essential in this PhD research. Material flow within the porous

media can be described by Darcy’s Law. Some of the important concepts related to

multiphase flow in porous media and oil recovery are discussed in this section.

2.2.1 Porous Medium

Porous medium is a continuous solid structure imbedded with pores, which can be

isolated, partially, or totally interconnected by channels. The collective volume of both

the pores and the channels constitute the void volume, which is called the pore volume.


18

Porous media can be rock, soil or man made such as etched glass, glass beads packs or

sand packs. When a collection of sand grains are packed together, it may form a porous

medium. The geometry of the grains will intrinsically leave some unoccupied space

between the packed grains; these are called pores. Figure 2-1-a shows an example of

packed silica sand grains. The image was taken by environmental electron scan

microscopy (EESM) model Philips XL 40, at the Commonwealth Scientific and

Industrial Research Organisation (CSIRO), Kensington. The pores are connected by

narrower channels which are called pore throats as shown in Figure 2-1-b. The pore

volume of geological formations could store fluids and allow the flow of fluids. The

fluid flow in porous media is a function of pressure gradients across the porous media,

Darcy’s Law could be used as a governing equation for the flow.

Figure 2-1: a) Packed sand grains create connected pores which allow for fluid flow and storage (Scale bar is equal to 500 µm in the left image). b) Closer zoom-in image showing the pores and pore throats.

2.2.2 Porosity and Storage Capacity of Porous Medium

Porosity is a measure of storage capacity of the porous medium. Absolute porosity is

the ratio of the void volume to the total bulk volume of the porous medium. The

effective porosity is the ratio of interconnected void volume to the total bulk volume of

the porous media (Ahmed, 2001). Many rock formations show some degree of

correlation between porosity and permeability.

Pores Sand Grains

Pore Throats

(a) (b)


19

2.2.3 Fluid Saturation in Porous Medium

The pore volume in a porous medium can be filled with one or more phases. A phase

could be aqueous, oil or gas. The saturation of a phase is the ratio of the phase volume

to the pore volume. The aqueous phase can be fresh water or brine. With regard to

chemical EOR the aqueous phase can be surfactant solution, polymer solution, alkali

solution or combination of these. One or more phases can co-exist in a single pore. If

only oil and water co-exist in the porous medium, then, the sum of water saturation (Sw)

and oil saturation (So) is equal to unity:

wo SS +=1

2.2.4 Wettability and Phase Distribution in Pores

Wetting is the tendency of a solid surface to maintain contact with one phase compared

to another phase. Wettability is a measure of the tendency of a solid surface to allow

the adherence or spread of one fluid in the presence of other immiscible fluids (Ahemd,

2001). A water-wet surface will tend to spread water and form a water film on its

surface. In this case, the water is the wetting phase and the oil is the non-wetting phase.

Whereas an oil wet surface tends to allow the formation of oil film on its surface; the oil

is the wetting phase and the water is the non-wetting phase.

The wettability (hydrophobicity and hydrophilicity) could be roughly estimated by the

contact angle formed between a drop of the liquid of interest and the surface. It is

usually measured from the denser fluid, Figure 2-2. Gao and McCarthy (2008)

emphasised that the contact angle should be treated as a rough estimate of

hydrophobicity of surface and not as an affirmative measure of wettability. They

pointed out that the measurement of the advancing and receding contact angles of a drop

between two surfaces are a more appropriate method to find the hydrophobicity and

hydrophilicity of the surface.

Regarding wettability towards water and oil, generally surfaces which form contact

angles of 0 o - 75o are considered water-wet. Those forming contact angles of 76 o -105 o

are intermediate wet while those which make contact angles between 106 o -180o are oil-

wet (Donaldson and Alam, 2008). These angle values constitute rough guides on which

2-1


20

surface is wetting and non-wetting (Gao and McCarthy, 2008). There are several

existing methods and scales to evaluate wettability such as the Amott wettability test

and the United State Bureau of Mines (USBM) method (Tiab and Donaldson, 2004, pp.

371).

In porous medium, generally one fluid will be closer or adherent to the walls of the pore

and may form a film depending on the wettability of the pore walls (Donaldson and

Alam, 2008). The other phase or phases can be confined within the pore but separated

from the pore wall by the adherent film.

Figure 2-2: The contact angle (θ) between the solid substrate and water drop surrounded by oil in sessile drop configuration [from Tiab and Donaldson, 2004]

2.2.5 Imbibition and Drainage

Imbibition is the process where the wetting phase is increasing, for instance, the

displacement of oil by water flood in water-wet porous medium. Drainage is the process

where the non-wetting phase is increasing, for instance, the displacement of oil by water

in oil wet porous medium.

2.2.6 Residual Saturations

The porous medium has the ability of retaining some amounts of a fluid when flooded

by another immiscible fluid. When one fluid is displaced by another immiscible in a

porous medium, the displaced fluid will move out of the porous media and be replaced

partially by the displacing fluid. The saturation of the displaced fluid decreases while

the displacing fluid’s saturation increases. Initially, only the displaced fluid comes out.

Water-Wet Intermediate-Wet Oil-Wet


21

After some time, the displacing fluid breaks through and comes out simultaneously with

the displaced fluid. After sufficient time of displacement at constant injection rate, most

of the displaced fluid will move out of the porous medium. Nevertheless, an amount is

retained by the porous medium and further displacement cannot push it out. This

imposes a minimum or terminal saturation of the phase; a residual saturation. When

water is displaced by oil, the residual saturation of water is referred to as the irreducible

water saturation (Swirr). In reservoirs, the naturally-occurring water saturation after the

oil migration process is referred to as the connate water saturation (Swc). When water is

displacing oil, the residual oil saturation is referred to as residual oil saturation (Sor).

The most important value is the residual oil saturation after primary and secondary

recovery are complete (target oil for tertiary recovery).

2.2.7 Permeability

Permeability is a measure of the ease with which a fluid flows within the rock and it is

one of the proportionality constants in Darcy Law. It is an intrinsic property of the rock

and when one single phase exists in the porous medium, the permeability is termed

absolute permeability. When two or more phases flow simultaneously in the porous

medium, then each phase will see different permeability. The permeability seen by each

phase is termed the effective permeability of the phase. The effective permeability is

usually less than the absolute permeability. The effective permeability is usually divided

by the absolute permeability to give the relative permeability of each phase (Ahmed,

2001). However, it can also be divided by a reference permeability such as the effective

permeability of one of the phases.

2.2.8 Permeability and Porosity Correlation by Porous Media Models

There are several porous medium theoretical models which relate the porosity to

permeability. Often there exists, but not always, an observable correlation between the

porosity and permeability of the porous media (Ehrenberg and Nadeau, 2005). Yet,

there is no simple formula to quantify this correlation. Shepherd (1989) showed that for

unconsolidated sands the following relation relates the grain size to the permeability:

badK = 2-2


22

where K is the absolute permeability, d is the grain or particle size, a and b are

dimensionless constants. For unconsolidated sediments with grain size of 1.10<d<2.05

mm, Shepherd found that 1.65<b<1.85, enforcing the common understanding that the

pores and grain size do correlate with permeability of the porous medium.

The Kozeny-Carman relation is another common correlation that can be used to

estimate the permeability if some data about the sand grains and porosity are available.

2

3

2 )1(5

1

φφ−

=

VgrSK Kozeny-Carman correlation

where,

pVp r

S2=

and,

−=

φφ

1VpVgr SS

VpS is the specific surface area per unit pore volume, VgrS is the specific surface area

per unit grain volume, φ is the porosity and rp is the radius of the pores. There are other

forms of this correlation (Tiab and Donaldson, 2004, pp. 105). This correlation may not

give accurate permeability values as the size of the pores is a distribution rather than a

single size. Nevertheless, in many cases there is correlation between the size of the

pores and the absolute permeabilities of some porous media.

2.2.9 Darcy’s Law

Darcy’s Law is an empirical relationship between the pressure gradient and flow rate.

The proportionality constant is a function of three constants: the permeability of rock to

fluid flow, the viscosity of the fluid and the cross-section area of the rock subjected to

the flow. The law takes the following expression for one dimensional (1D) porous

media and single phase flow with selected conventional units:

)9.101177.14

(ghP

L

AKq a ρ

µ−∆

∆=

2-3

2-4

2-5

2-6


23

where q is the flow rate (mL/s), Ka is the absolute permeability of the porous medium,

the unit of permeability is the Darcy (D) and one Darcy is equal to 0.9869 x 10-12 m2 in

International System of Units (Dandekar, 2006). A is the cross-sectional area of the

rock/porous medium perpendicular to the flow direction (cm2), µ is the viscosity of the

fluid (cP), ∆P is the pressure drop (psi) across distance ∆L (cm) of the porous medium

in the direction of flow, ρ is the density of the fluid (g/cm3), g is gravitation acceleration

(m/s2) and h is the elevation across ∆L (cm). The factors 14.7 and 10117.9 are unit

conversion factors.

2.2.10 Relative Permeability and End Point Relative Permeability

Darcy’s Law deals with single fluid flow. The concept of relative permeability is needed

to extend the law to two or more fluids flowing simultaneously in a porous medium.

The effective permeability for each fluid is dependent on its saturation. Darcy’s Law

takes the following form for a two phase flow in 1D porous media:

)9.101177.14

()( ghPASKK

qi

iriai

ρµ

−∆=

where qi is the flow rate of phase i, Kri is the relative permeability of phase i, with the

remaining symbols are the same as in the definition of Darcy’s Law.

Figure 2-3: Illustration of the curves of relative permeabilities and the end point relative permeabilities.

0 Water Saturation (Sw) 1

Rel

ativ

e P

erm

eabi

lity

1

EndrwK : End Point Relative

Permeability of Water

EndroK : End Point Relative

Permeability of Oil

2-7


24

The end point relative permeability is a special case of relative permeability. Figure 2-3

illustrates the curves of relative permeabilities and shows the end point relative

permeabilities. As mentioned earlier, the relative permeability of a phase is a function of

the saturation of the phase. When a phase is at its terminal saturation, then the relative

permeability of the phase is called the end point relative permeability. Polymer injection

can change the relative permeability of a porous media towards water. This change may

have significant impact on the ASP process as will be discussed in Chapter 6 in this

thesis.

2.2.11 Mobility Ratio

The mobility ratio (M) is the ratio of the displacing phase’s maximum velocity to the

displaced phase’s maximum velocity. When water is displacing oil, the mobility ratio

(M) can be defined as:

where EndrwK and End

roK are defined above in Figure 2-3, while wµ and oµ are the

viscosities of water and oil respectively. M is preferred to be equal to or smaller than

unity for efficient immiscible displacement. It is possible to alter M by changing the

viscosity of the water. The viscosity of water could be changed by adding water soluble

polymers like polyacrylamide or polysaccharide.

2.2.12 Surface and Interfacial Tension

Interfacial tension (IFT) between aqueous and oleic phases is detrimental for oil

recovery and very important to ASP flooding. IFT is of high importance to other

chemical EOR processes as well (Green and Willhite, 1998). The surface or interfacial

tension can be defined as the minimum work required for expanding the contact surface

or interface between two phases by one square meter (Rosen, 2004, pp. 1). There are

several methods to measure IFT, for example, pendant drop and spinning drop

(Schramm and Marangoni, 2000). IFT could be lowered drastically by the addition of

o

Endro

w

Endrw

K

K

M

µ

µ= 2-8


25

surface active agents (surfactants). The classical Young-Laplace equation is the starting

equation for the measurement of IFT between two fluids:

+=∆

21int

11RR

P σ

where ∆Pint is the pressure difference at interface, σ is the interfacial tension between

the two fluids, R1 and R2 are the radii of interface curvature. The pendant drop and

sessile drop IFT determination techniques stem from this equation (de Gennes,

Brochard-Wyard, and Quere, 2004: Paddy, 1969).

2.2.13 Capillary Length Capillary length ( 1−κ ) is the length beyond which gravity forces become important for

the fluid shape and motion and below which capillary forces dominate. It can be defined

using the following expression of de Gennes, Brochard-Wyard, and Quere (de Gennes,

Brochard-Wyard, and Quere, 2004):

Egρσκ

∆=−1

where σ is the surface tension or IFT between the fluids, gE is the Earth’s gravity

acceleration constant (9.8 m/s2) and ∆ρ is the difference in the fluids density. For

instance, in the case where oil and water co-exist in pores, 1−κ ≈ 3 mm for a water/oil

system with ∆ρ of 145 kg/m3 and IFT of 12 mN/m. The pores in a porous medium are

usually smaller than this value of 1−κ making the gravity effect negligible. While in the

case of ultra low IFT chemical flooding with IFT= 0.005 mN/m, 1−κ ≈ 60 µm for the

same ∆ρ. This capillary length is comparable to the sizes of the pores. Fluids within the

pores may thus form droplets with sizes close to this 1−κ which can flow through pores

under gravity action. As a result, gravity may become important and influential on the

flow process. Consequently, vertical flooding is preferred as it ensures that gravity does

not affect the experiments of oil recovery at ultra low IFT. The flooding experiments in

this PhD work were thus conducted in vertical configuration.

2-10

2-9


26

2.2.14 Capillary Pressure

Capillary pressure is the phenomenon that causes a wetting phase to rise up in a

capillary tube when immersed in the phase. Figure 2-4 illustrates the capillary pressure

using water and air as an example. From a petroleum engineering standing point,

capillary pressure is the resultant pressure of the pressures of two fluids present in a

narrow channel (capillary tube), one wetting and one non-wetting:

wnwc PPP −=

where Pc is the capillary pressure, Pnw is the pressure of the non-wetting fluid and, Pw is

the pressure of the wetting fluid. Capillary pressure can also be defined in terms of the

radius of a cylindrical capillary tube (rc), the IFT between the two fluids in this case

air/water (σwa) and the contact angle (θ) between the air/water interface and the tube

wall as shown in Figure 2-4. Water is assumed to be the wetting phase in this example.

The water column in the capillary tube with height (y) is raised by the capillary forces

and balanced down by the gravitational force of the water column mass. At height y

there is a static equilibrium between the capillary forces pulling the water column up

(wetting phase) and the gravitational forces which pull the column down against the

capillary force. The capillary pressure is given by:

c

wac r

Pθσ cos2

=

Figure 2-4: Water rise in capillary tube by capillary forces [based on Ahemd, 2001].

2-11

2-12

Water

Air θ

σ

y

rc


27

The definition of capillary pressure in Equation 2-12 implies that narrower tubes or

channels will require higher pressures for non-wetting fluid to get through. This applies

to water wet medium when oil (non-wetting phase) is pushed through pores and pore

throats. When the porous medium is oil-wet, water needs to be at higher pressures to

reach the narrower channels. In summary, capillary forces have to be overcome to

recover more oil in oil-wet or water-wet porous medium. This can be achieved by

lowering IFT between the fluids.

2.2.15 Capillary Number The concept of capillary number helps to justify the ability of porous medium to trap

residual saturations of different fluid phases. Capillary number (Nc) is the ratio between

viscous forces to capillary forces (surface tension). It can be defined, as the follows for

the case when aqueous phase is displacing oil (Green and Willhite, 1998):

ow

wwc

uN

σµ=

where uw is the interstitial speed of water in the porous medium, owσ is the IFT

between oil and aqueous phase and, wµ is the viscosity of the aqueous phase.

Another common expression to define the capillary number includes the Darcy

velocity of water (qw) and contact angle between the water/oil interface and the

porous medium (Li et al., 2007):

θσµcoswo

wwc

qN =

It is believed that lower IFT (higher Nc) increases the recovery of petroleum. Figure 2-5

shows a typical curve of Nc in water flooding. The figure is a plot of the mobilised

residual oil against the capillary number. The nature of this curve changes with rock

type (Lake, 1989).

In reservoir water flooding, the typical range for Nc is 10-5-10-7. The trapped oil may

start to be mobilised when Nc reaches a critical value (Ncri) of about 10-5. Complete

mobilisation of the oil may occur when Nc is in the range 10-2-10-1 (Austad and Milter,

2-13

2-14


28

2000). From this, Nc needs to be increased by a factor of 103-104. The only practical

way of achieving this is by reducing the IFT by a factor of 103-104 (Austad and Milter,

2000). This means reducing IFT from typical oil/water IFT of 30 to 0.03 mN/m or

lower. Surfactants are effective in reducing IFT (Rosen, 2004) and increasing the

capillary number (Green and Willhite, 1998).

Figure 2-5: Typical capillary number curve and recovery of residual oil (from Austad and Milter, 2000).

2.2.16 Bond number (Buoyancy Number) Buoyancy is an important transport mechanism when there is a significant density

difference between the fluids. It is quantified by a dimensionless number that is called

the bond number. The bond number (NB) is the ratio of gravity to capillary force, and

can be defined by Equation 2-15 (Li et al., 2007):

θσρ

coswo

ErwB

gKKN

∆=

where gE is the Earth’s gravitational acceleration. All the remaining symbols have been

defined in the sections of Darcy’s Law (Section 2.2.9), relative permeability (Section

2.2.10) and capillary number (Section 2.2.15).

2-15

Water flood

Ncri

10-7 10-6 10-5 10-4 10-3 10-2

40

30 20

10

Nc (dimensionless)

S

or (

%)


29

2.2.17 Trapping Number

The trapping number (NT) is a combination of the bond and capillary numbers. Trapping

number (NT) is used in modelling of oil recovery by chemical floods. It can be

expressed in the following form (Li et al., 2007):

22 sin2 BBCCT NNNNN ++= α

where α is the angle of the fluid flow with respect to the horizontal plane. Li et al. (2007)

reported that the typical values of NT are of the order of 10-5 and complete mobilisation

of trapped oil occurs at NT values of the order 10-3.

2.2.18 Total Acid Number and Petroleum Acids

Crude oil may contain petroleum acids as part of its composition. The amount of acids

in a crude oil is important for chemical EOR that utilise alkali to increase recovery.

There is no definitive known structure of these acids (Green and Willhite, 1998, pp.287),

but they are most believed to be mostly carboxylic acids (Rivas et al., 1997). Schramm

et al. (1984) reported that the maximum recovery of oil extracted from oil sands is

independent of the pH. They showed that there is a critical carboxylic acid

concentration to achieve maximum oil recovery; and this is independent of the pH of

the system. Resins and asphaltenes are fractions of crude oil believed to have surface

activity that enhance the formation and stability of emulsions (Graham et. al., 2008).

Jennings (1975) defined the Total acid number (TAN) as the number of milligrams of

potassium hydroxide (KOH) required to neutralise the acids in one gram of oil. TAN is

a measure of the amount of petroleum acids existing in oil regardless whether it

participates in reduction of IFT. Fan and Buckley (2007) showed an improved method

to determine TAN of oils.

Despite the importance of the TAN, it does not always correlate with oil recovery

(Green and Willhite, 1998, pp.288). Liu (2007) showed a method to extract the portion

of the oil acids that is believed to be active to reduce IFT. The TAN concept was used in

this thesis to ensure that the alkali is engaged in the ASP process to produce in-situ

surfactants.

2-16


30

2.2.19 Displacement Efficiency and Volumetric Sweep Efficiency The effectiveness of an oil recovery process can be evaluated by the recovery efficiency

(ERi). Recovery efficiency is a result of two efficiencies: Volumetric sweep and

displacement efficiencies. The volumetric sweep efficiency (EVi) with respect to oil

recovery reflects the portion of the porous medium that was contacted by the flood. The

displacement efficiency (EDi) reflects the ability of the flood to mobilise the oil that it

contacted. These efficiencies can be defined as the following for oil recovery (Lake,

1989):

ViDiRi EEE =

contacted oil ofAmount

displaced oil ofAmount =DiE

placein oil ofAmount

contacted oil ofAmount =ViE

Displacement efficiency can be also defined by Equation 2-20 (Ahemd, 2001).

oi

oroiDi S

SSE

−=

It is very frequent to report the success of an oil recovery process in terms of percentage

of the original oil in place (OOIP):

100covRe

)(%cov ×=PlaceInOriginallyOilofAmount

eredOilofAmountOOIPeryreOil

Or simply

100)(%cov ×−=oi

ooi

S

SSOOIPeryreOil

where Soi is the initial oil saturation, oS is the oil saturation at the end of recovery

process. Equation 2-22 is used in this thesis to evaluate oil recovery from the sand

packs.

2-17

2-18

2-19

2-20

2-21

2-22


31

2.3 Heterogeneity Definition and Measures Irregularities in the intrinsic rock properties at both the microscopic and macroscopic

level will cause the flow to speed up or slow down in different portions of the rock/

reservoir. Therefore, parts of the flood front itself will experience different velocities.

Consequently, the front will spread in accordance to the variations in the rock intrinsic

properties. A definition of heterogeneity based on variations in the geological properties

of the reservoir is given by Ahmed as the following: “The reservoir heterogeneity is

then defined as the variation in reservoir properties as a function of space” (Ahmed,

2001, pp. 255).

A flow-based definition of heterogeneity is given by Lake and Jensen which defines the

heterogeneity as: “The quality of the medium which causes the flood front- the boundary

between the displacing and displaced fluids- to spread as the displacement proceeds.

For a homogeneous medium, the rate of spreading is zero.” (Lake and Jensen, 1989, pp.

2).

This would mean higher degrees of heterogeneity will cause more spreading of the

flood front. Variation in properties like permeability, porosity, cation exchange capacity

and clays content can be used to quantify heterogeneity (Lake, 1989). In general,

permeability is probably the most important factor affecting the flow. Often

permeability is considered and used to quantify heterogeneity and other properties are

more or less ignored (Jensen and Lake, 1988).

2.3.1 Heterogeneity Measures

There are a few measures of heterogeneity and they can be grouped into two main

categories: dynamic and static measures of heterogeneity (Lake and Jensen, 1989).

While dynamic measures depend on monitoring the effects of heterogeneity on the flow,

static measures depend on static properties of rock like porosity and permeability.

Usually, a dynamic measure needs a flow tracer(s) and some technique to quantify the

tracer(s) in the produced fluids which could reveal heterogeneity level of the medium by

plot of flow capacity-storage capacity (Shook, and Forsmann, 2005). Two static

measures are commonly used: Dykstra –Parson permeability variation coefficient (VDP)

and Lorenz coefficient (LH). For a reservoir, the values of these coefficients can range


32

from 0 to 1, with 0 being homogenous and 1 being infinitely heterogeneous (Lake, 1989,

pp. 196). In reality, there is no such homogenous reservoir or infinitely heterogeneous

reservoir. Real formation would possess a value between these two, for example,

Daqing oil field has Dykstra –Parson coefficients greater than 0.5 (Chang et al., 2006).

Ahmed (2001) and Lake (1989) have elaborated on these two measures and have given

numerical examples.

Though not stated, it appears that these measures assume a vertically multi-layered

reservoir. Therefore, it may not be suitable to evaluate the heterogeneity of thin narrow

sand packs (being single layer). In this PhD, none of these measures were evaluated for

the sand packs nor were tracer tests preformed to evaluate the heterogeneity. This is

because these are not applicable to narrow sand packs since the heterogeneity was

introduced in the sand packs longitudinal to the flow direction.

2.4 Surfactant Flooding

Surfactants are one of the three main components of the ASP EOR process. Standalone

surfactant flooding is one of chemical flooding techniques practiced in enhanced oil

recovery (Green and Willhite, 1998). The main task of surfactants is to reduce the

interfacial tension (IFT) between the flooding water and the oil within the pores

(Morrow, 1991). Surfactants are molecules which have surface activity and have the

ability to adsorb between the oleic and aqueous phases, causing the IFT between the

two phases to decrease (Schramm and Marangoni, 2000). The area, occupied per

molecule at the interface, will affect the number of molecules which can share the

interface at the same time and in turn this will affect the magnitude of IFT reduction

(Rosen, 1989). If the IFT between oil and flooding water is reduced to a vanishing value

in the order of 0.001 mN/m, then the capillary forces which trap the residual oil will be

much weaker, thus, allowing some of the residual oil to be recovered (Austad and

Milter, 2000).

2.4.1 Mechanism of Oil Recovery by Surfactant Flooding The main action of surfactant flooding to enhance oil recovery is the reduction of IFT

(Green and Willhite, 1998). Emulsion or microemulsion may form in the process of

surfactant flooding giving it common name of micellar flooding. The IFT between oil


33

Head Group

Chain (Tail) Linear or Branched Hydrocarbon or Fluorocarbon

and water in normal situation, when no surface active agents are present, will be 10-40

mN/m. The forces produced by high IFT between oil and water will hinder the

breakdown of oil droplets to smaller droplets, with the former getting stuck in the pores

and their throats. At some stage of flooding, the force produced by the drag force of

flooding phase’s viscosity is not large enough to move the trapped drops. When IFT is

dramatically reduced, the capillary forces are reduced. When the IFT is low enough, the

droplets are allowed to rupture into smaller size, enabling them to get through pores and

pore throats (Arriola et al., 1983). Lower IFT makes the formation of smaller droplets

easier (Schramm, 1992, pp. 17). Surfactant can also induce alterations in wettability

(Spinler and Baldwin, 2000).

2.4.2 Surfactant Molecule A surfactant molecule generally consists of at least one polar part (commonly known as

head) and at least one non-polar part (commonly known as tail), as depicted in Figure

2-6.The head is hydrophilic (lipophobic) which generally prefers to be in water or ionic

liquids and generally is repelled by oil or non-polar liquids. The tail is lipophilic

(hydrophobic) that prefers to be in oil and is repelled by water. This is very simplistic

view of the thermodynamics of surfactants which in reality is more complex. The tail

may be branched or contain alcohol groups.

Figure 2-6: Sketch of a generic surfactant molecule structure [from Ottewill (1984) cited in Green and Willhite (1998)]

The hydrophobic nature of the surfactant tail may cause distortion to the solvent (water)

structure. Therefore, minimizing the energy requirement –in the water phase- is

achieved by expelling some of the surfactants to the surface or interface with their tails


34

pointing out of the water phase and their heads staying within the water phase. The

existence of the head will not allow the total separation of the surfactant molecules into

a distinct phase. Another way of achieving the energy minimization besides adsorbing

at the surface and interface is by confining and arranging the hydrophobic tails in a

sphere-like shape with the tails pointing inside this sphere and the heads facing the polar

liquid (Rosen, 2004). This configuration is known as micelles and will be presented in

more details below.

2.4.3 Surfactant Classification

The surfactant can be classified either based on the charge of surfactant molecule or the

type of emulsion that the surfactants tend to form. The head group can be nonionic or

ionic. The ionic surfactants molecule normally dissociate in water to give positive,

negative or zwitterionic head group. Nonionic surfactants do not disassociate and their

head groups depend on molecules or groups which have affinity to water. The

zwitterionic surfactant is one that has two opposite charges on the surfactant head. This

last type can be sensitive or in-sensitive to the pH of the solution which decides what

the effective charge is. Examples of these surfactant types are given in Table 2-1.

Table 2-1: Classification of surfactants based on head charge

Type Head

Charge Example Structure

Anionic

Negative Soap

alkylbenzene sulfonate

RCOO-Na

+

RC6H4SO3-

Na+

Cationic

Positive

salt of a long-chain amine

quaternary ammonium chloride

RNH3 +Cl

-

RN(CH3)3+Cl

-

Nonionic

No charge monoglyceride of long-

chain fatty acid polyoxyethylenated alcohol

RCOOCH2CHOHCH2OH

R(OC2H4)x OH

Zwitterionic (Amphoteric) Positive

and Negative

long-chain amino acid

sulfobetaine

RN+H2CH2COO

-

RN+(CH3)2CH2CH2SO3

-

*R is a hydrocarbon chain, x is an integer number.

+ -

+

-


35

In the literature, it is generally accepted that those surfactants or surfactant mixtures

which have equal affinities towards oil and water are more effective in reducing the IFT,

and thus can maximise oil recovery (Rivas et al., 1997; Rosen, 2004; Schramm and

Marangoni, 2000). In order to quantify this balance between hydrophilic-lipophilic

affinities an empirical scale was developed by Griffin (Rosen, 1989), known as

Hydrophilic-Lipophilic Balance (HLB). However, the affinity of the surfactant to the

oleic or aqueous phase is sensitive to the salinity and other ions present in the system.

Phase behaviour screening is an established way to find the salinity of a specific

surfactant or surfactant mixture that will give the lowest IFT between specific oil in

contact with aqueous phase of a specific composition of ions (Healy et al., 1976).

Recently, Gary Pope and his team affirmed the robustness of the use of phase behaviour

screens to identify good surfactants for EOR process (Levitt, Jackson, Heinson, Britton,

Malik, Dwarakanath, Pope, 2009).

2.4.4 Hydrophilic-Lipophilic Balance (HLB)

Hydrophilic-Lipophilic Balance (HLB) can be defined as the ratio between the

hydrophilic and lipophilic affinities (Schramm and Marangoni, 2000). It is a scale from

0-40 based on the work of Griffin (Rosen, 2004) and can be determined based on the

structure of the surfactant molecule. The general use of the HLB is as an empirical

indicator to predict what type of emulsion a surfactant going to form, however, it does

not indicate the efficiency. The HLB value of a surfactant is based on the structure of

the molecule. Surfactant molecules with long tails will tend to be more lipophilic, and

those with shorter chains will be dominated by the head polar group, making them more

hydrophobic. In addition, as the chain (tail) length is increased, the chain will start to

bend. This, in turn, increases the area occupied by the molecule at the interface, thus,

the surfactant effectiveness in reducing the IFT will decrease (Rosen, 2004). HLB can

help in the design of surfactants for EOR applications (Schramm and Marangoni, 2000;

Austad and Milter, 2000).

2.4.5 Micelle Formation and Critical Micelle Concentration (CMC)

Micelle formation by surfactant molecules is a fundamental property of surfactants

(Rosen, 2004). A micelle is an aggregation of surfactants molecules. In this aggregation,


36

the tails of the surfactants are associated inside the aggregation, whereas, the heads will

be pointing outwards facing the water phase (Schramm and Marangoni, 2000). This

type of surfactant aggregation will form a sphere-like shape. There is also the case

where the heads are pointing to inwards the sphere and the tails are pointing to outside,

which is called inverse-micelle. The critical micelle concentration (CMC) is the

concentration at which the micelles start to form, Figure 2-7. Below the CMC, the

surfactants are present in the aqueous solution as monomers. When CMC is reached, the

surfactant monomer concentration will remain constant even if more surfactant is added

to the solution, Figure 2-7. This is because above the CMC the excess monomers start

to form micelles.

Figure 2-7: The CMC is the concentration where micelles start to form and the concentration of surfactant monomer remains almost constant (after Lake, 1989). The critical micelle concentration is an important parameter of surfactants. As the CMC

is approached many of the physical properties of a solution such as surface tension (ST),

interfacial tension (IFT) and electrical conductivity will experience an abrupt change in

behaviour (Preston, 1948), Figure 2-8. For example, with regard to conductivity, below

CMC a surfactant will behave like a normal electrolyte, the conductivity will slowly and

almost linearly decrease with electrolyte increase. However, surfactant solutions will

exhibit an abrupt drop in the conductivity when the CMC is reached. In the same figure,

note that the IFT decreases sharply and becomes almost constant for surfactant

concentrations above CMC. Although Figure 2-8 shows specifically the behaviour of

sodium lauryl sulphate, it generally reflects the typical behaviour of other surfactants.


37

Figure 2-8: Some physical properties show change in the vicinity of the CMC [After Preston, 1948].

2.4.6 Solubilisation of Oil by Surfactants The surfactant above the CMC reduces the IFT between aqueous phase and oil. This

allows the solubilisation of oil. It is believed that the oil solubilises within the tails

inside the micelle sphere for the case of oil-in-water emulsion and the water will be

solubilised between the heads in the case of water-in-oil emulsion (Rosen, 2004;

Schramm and Marangoni, 2000).

2.4.7 Stability of the Sulphate and Sulphonate Surfactants Sulphonate and sulphate surfactants are commonly used in EOR chemical floods

(Austad and Milter, 2000; Green and Willhite, 1998; Hirasaki et al., 2008). In one hand,

these families of surfactants are cheaper and broadly speaking are commercially

available in sufficiently large quantities to support oil field applications. On the other

hand, they suffer from intrinsic limitations such as hydrolysis, precipitation, adsorption

on rock, retention by residual oil, phase separation and performance sensitivity to

electrolytes in the aqueous phase.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Sodium Lauryl Sulphate Concentration (%)

Critical concentration

DETERGENCY

Uni

t of

Mea

sure

men

t of

Eac

h P

rope

rty


38

2.4.8 Surfactant Chemical Stability: Hydrolysis and Precipitation

Sulphonate surfactants are more stable than sulphate surfactants (Tadros, 2005; Hirasaki

et al., 2008). Sulphate may hydrolyse and those with unsaturated hydrocarbon chain

may suffer oxidation and colour formation (Rosen, 2004). Divalent ions, if in

sufficiently high concentration, may cause the ionic surfactants to precipitate, however,

ethoxylated and propoxylated sulphate surfactants have tolerance to divalent ions like

calcium ions. Ethoxylated sulphates may not hydrolyse at low temperature, but would at

higher temperatures (Tally, 1988). The sulphate used in this PhD thesis is an alcohol

branched propoxylated sulphate. There was no available work on the stability of this

surfactant. However, given its similar structures to the ethoxylated sulphate, it should

have similar stability level, especially when at room temperature.

2.4.9 Surfactant Retention Retention is the loss of surfactant during the process of flooding. Retention of surfactant

by porous media which bears oil is generally dominated by four main phenomena:

precipitation, adsorption, ion exchange and phase trapping. In practice, when the

reduction of IFT is the aim of the EOR process, the surfactant is the main active

chemical (Wesson and Harwell, 2000). Losing the chemical to the rock surface or to

stagnant oil in the porous medium will reduce the recovery and increases the cost of the

project. In any field project, loss of the surfactant during the flood must be addressed.

Precipitation occurs in hard water when it has sufficient concentration of divalent ions

(D2+). It is the process in which the surfactant ( −− 3SOR ) reacts with the calcium or

magnesium ions present in the water. The reaction produces solid which precipitate:

↓−→+− +−23

23 )(2 SORDDSOR

The precipitation causes the loss of the inventory surfactant in the chemical slug as well

as a possible source of permeability reduction.

Ion exchange is a reaction that occurs between the solution electrolyte and electrolyte

adsorbed on the rock and could be considered as a special case of adsorption (Green and

Willhite, 1998). The two equations below were used by Hill and Lake (1978) as an

2-23


39

Log Equivalent bulk concentration of surfactant

illustrative example of ion exchange. As given by Equation 2-24, the anionic surfactant

reacts with divalent cation (M2+) on the porous medium matrix. This results in

monovalent cation (surfactant-divalent ion)+ and then the exchange take place by

freeing a cation (Na+) from the mineral to the solution and the surfactant molecule will

adsorb to the clay as described by Equation 2-25:

++− −→+− 32

3 SOMRMSOR

)(33 freeNaClaySOMRSOMRClayNa ++ +−−→−+−

Adsorption is the process in which the surfactant adsorbs on charged sites of the pores.

Minerals in the pore may be charged with opposite charge to that of the surfactant. The

charge of the site might be pH dependent. The surfactant, when they have opposite

charge to that of the surface, will adsorb to the surface to maintain electrical neutrality.

Langmuir type adsorption suits quite well the surfactant adsorption in porous media

(Wesson and Harwell, 2000). A typical isotherm of surfactant adsorption is shown in

Figure 2-9.

Figure 2-9: Typical S Shaped adsorption isotherm for an ionic surfactant in an oppositely charged substrate (From Rosen, 2004)

Rosen (2004) reported that region 1 in Figure 2-9 is possibly due to ion exchange,

region 2 is due to chain-chain interaction between the adsorbed and incoming surfactant

molecules. Region 3 is due to the hemi-micelle formation and finally region 4 is the

micelle formation, after which adsorption reaches a plateau. The key points are that

although the adsorption can be described by the simple Langmuir adsorption type, it is

in reality a complex process highly dependent on the pH, the electrolyte of the solution

Log

ads

orpt

ion

of s

urfa

ctan

t

2-25

2-24


40

as well as the nature of the solid. Also when high enough surfactant concentration is

deployed, the adsorption reaches a plateau which means all adsorption spots have been

filled and no more surfactant loss occur.

Phase trapping is the process in which the surfactant is lost into a stagnant portion of

the oil in the porous media (trapped oil). The phase trapping of surfactants is more

pronounced when the phase behaviour is in the upper phase (Glover et al., 1979). Phase

behaviour is discussed further below in this chapter.

2.5 Alkaline Flooding

Alkaline or caustic flooding is one of the chemical enhanced oil recovery processes that

have been applied in the petroleum industry (Green and Willhite 1998). It involves

adding some alkali in small amount to water, then the injecting this aqueous solution

(the alkaline solution) into the oil bearing formation. Squires (1916) patented a process

to recover oil and gas from wells which cease to flow naturally. He discussed water

injection and named it “flood” to increase reservoir pressure and recover more oil from

such wells. He opened the possibility to add alkali to enhance the recovery. In 1927,

Atkinson explicitly patented the adding of strong alkali to water flood to recover oil

from abandoned wells after water flooding failed to recover any more oil from the

formation (Atkinson, 1927). This is what we call today enhanced oil recovery.

According to Johnson (1976), the first patent on caustic flooding was granted in 1927 to

Atkinson.

2.5.1 Oil Recovery Mechanism of Alkaline Flooding

There are several proposed mechanisms of oil recovery by alkaline flooding. The most

prominent is the reduction of IFT by the action of in-situ generated surfactants, which

are the product of interaction, between the alkali and existing acids in the crude oil. The

alkali increases the pH of the water and provides hydroxide ions which strip proton

from some of the acids present in the crude oil. This reaction forms acid salts, which are

referred to as in-situ soaps. Subkow (1942) patented a process which involves the

injection of alkaline solution into bitumen formation. He explicitly mentioned that the

alkali may react with the organic acids naturally found in bitumen to produce

emulsifying soaps. Besides bitumen, carboxylic acids can be found in other crude oils


41

(Qian et al., 2001; Borgund et al., 2007). Thus, the alkali flooding can be extended to

other crude oils.

Some of these in-situ generated soaps are believed to reduce the IFT (Jennings, 1975;

Poteau, et al., 2005). This reduction in the interfacial tension between the crude oil and

the alkaline solution will lead to a reduction in the capillary forces. Therefore, it will be

easier for trapped oil droplets to move to production wells, which in turn increases the

oil recovery.

Johnson (1976) published a good first review on alkaline flooding. He stated several

proposed oil recovery mechanisms involved in the alkaline flooding:

(1) Emulsification and entrainment,

(2) Wettability reversal (oil-wet to water wet),

(3) Wettability reversal (water-wet to oil-wet), and

(4) Emulsification and entrapment.

Jennings (1975) studied 164 crude oils samples from 78 fields. These samples were

analysed to study the effect of the alkaline solution on the surface activity of crude oils.

Of these, 131 samples showed surface activity in response to the addition of caustic

solution. This surface activity correlated with acid number, viscosity and gravity

number of the oil. He indicated that there are many proposed mechanisms of the

enhanced recovery of the caustic flooding. However, interfacial tension reduction seems

to be a common ground variable. Two important points of his conclusion are: (i) the

alkali concentration that is required to show a marked surface activity is in the order of

0.1 % wt and (ii) the surface activity is dependent on the water content of dissolved

solids.

2.5.2 Alkali Agents Used in EOR Alkalis used in EOR studies have included sodium hydroxide (NaOH), sodium

carbonate (Na2CO3), ammonium hydroxide (NH4OH), tripolyphosphate, sodium

metasilicate (Subkow, 1942; Mayer et al., 1983; Doll, 1988; Schramm, et al., 1984; Sun

et al., 2008). Recently, sodium metaborate has been used as alkali and its advantages

have been reported by Flaaten et al. (2009). Metaborate shows tolerance to divalent


42

cations more than other conventional alkalis used in the EOR. Na2CO3 has another

advantage that is the carbonate/bicarbonate (CO32-/HCO3

-) is a potential determining ion

for calcite and carbonate minerals. In general, such potential determining ions can

change the wettability of the formation (Hirasaki and Zhang, 2004; Saneie and Yanis,

1993).

The alkali reduces the surfactant adsorption (Hirasaki and Zhang, 2004). Carbonate

precipitation is reduced when sodium carbonate is used as alkali, thereby not adversely

changing formation permeability as compared to sodium hydroxide (Hirasaki and

Zhang, 2004). Baviere et al. (1993) found that sodium carbonate is more effective than

sodium hydroxide in reducing the sulphonate surfactant adsorption on kaolinite at a

lower pH value. Therefore, lower pH may reduce rock dissolution and alkali agent

consumption.

2.5.3 Geochemistry Modelling of Alkaline Flooding

Breit et al. (1979) simplified all the understanding of alkaline recovery mechanisms

down to a simple relative permeability change. It implies that whatever the mechanism

of displacement of the caustic flooding, the most pronounced effect is the change in

relative permeability, and, this on its own, is enough to predict the performance of the

alkaline flooding regardless of the minute details of the recovery mechanism.

Ramakrishnan and Wasan (1983) modelled the interfacial activity of the caustic solution

and crude oil. They included some of the proposed chemistry involved in the process.

They assumed that the mixture of acid species in the system can be represented by one

pseudo component, HA. It is distributed in the oleic phase as HAo and HAa in the

aqueous in a constant distribution ratio. Their model takes into account the adsorption at

the interfaces of different phases. However, their model did not take into account the

effect of salinity.

DeZabala et al. (1982) made the first oil recovery simulation of the caustic flooding by

including some of the proposed chemistry involved in the alkaline flooding in core scale.

Their simulation results were qualitatively in agreement with the experimental results.

They stated that there are at least eight proposed mechanisms of alkaline enhanced oil

recovery. One of the reasons for the late appearance of alkaline flooding simulation


43

since its first appearance might be attributed to the wide divergence of opinions on the

mechanism of caustic oil recovery. They concluded that the ion exchange may cause

chromatographic lag of the alkaline flooding at low pH. They thus suggested that

flooding should be done at higher pH values. They also indicated that mobility control

will increase the efficiency of the alkaline flooding. This implies the usage of polymers

for flood mobility control.

Ahmed and Arnold (1989) used the same concept of DeZabala et al. (1982) for the

alkaline chemistry in oleic and aqueous phases. They worked on improving the

chemistry and displacement model of DeZabala. Their results of alkaline flooding

simulations of cores matched the results of core flooding experiments. They extended

their work to make prediction for the alkaline project of the Wilmington Field located in

the USA, CA. The alkaline pilot was conducted in the Ranger Zone. The simulation

result was lower and close to the field trial results.

Bhuyan et al. (1990 and 1991) modelled and simulated high pH alkaline flooding. This

model is similar to that of DeZabala but they added and included many more reactions.

In addition, it was extended to develop a simulator for alkaline/ surfactant/ polymer

process.

The acids in crude oil are hardly soluble in water with very low partition coefficients of

the order of 10-4 (DeZabala et al., 1982). The partitioning of acids between oil and water

could be represented by pseudo acid component (HA):

KD

HAo HAw

where KD is the partition coefficient, HAo represents pseudo acids present in oil, HAw

is the component of the pseudo-acids partitioned into the water. Some of the acids

partitioned in the water may disassociate with a dissociation coefficient Ka following

Equation 2-27:

Ka

HAW H+ + A-

[ ]O

WD HA

HAK

][=

2-26

[ ][ ][ ]W

a HA

AHK

−+=

2-27


44

This reaction does not produce sufficient conjugate base ions (weak acid; soaps or in-

situ surfactants) to cause significant surface activity. If the hydroxide ions are present in

the water by alkali injection, more acid molecules tend to ionise to produce more in-situ

surfactants. This reaction could be described by Equation 2-28, assuming

thermodynamical equilibrium has been reached with equilibrium constant of eqK2 .

HAW + OH- H2O + A-

Keq2

The ionisation process of the acids present in water produces more acid anions

(conjugate base) which are surface active with the respect to the surfaces and interfaces

and could have significant impact on the IFT. It is worth mentioning that not all acidic

components in oil could produce effective surface active anions (Liu, 2007). There are

more complex reactions taking place in alkaline flooding such as exchange reactions

with micelles and porous medium matrix, dissolution reaction and precipitation as well.

These are well described and discussed in many references (Bhuyan, 1989).

2.5.4 Alkali Consumption Alkali reacts with some acid components in the crude oil, causing its concentration to

deplete. Sun et al. (2008) studied the consumption of alkali agents by crude oil. They

studied two alkali agents namely NaOH and Na2CO3. They found that, the injected

NaOH reacted completely with acid components in the crude oil, while NaCO3 was

found to react partially and slowly with the acid components. However, Na2CO3 was

better at reducing the IFT due to its buffering effects. According to Krauskopf and Bird

(1995), buffers are solutions capable of absorbing considerable amounts of H+ or OH-

without much change in pH. The Na2CO3 solution is considered as a buffer solution and

has more ability to regulate the pH than the NaOH.

Alkali can also react with the reservoir rock (Saneie and Yanis, 1993; Mayer et al.,

1983). Saneie and Yanis worked on modelling and simulating the injection of alkaline

solution with hot water (Saneie and Yanis, 1993). They modelled the alkali reaction

with acid in a similar approach to DeZabala approach (DeZabala et al., 1982). They also

[ ] [ ][ ]W

eq

HA

AHK

−+=2

2-28


45

included the alkaline reaction with the silica in the quartz surface. They assumed in their

modelling instantaneous thermodynamical equilibrium of the alkaline reaction with oil

acids and quartz of the sands. They gave a good overview of the pH effects on the

surface nature of silica and the dissolution rate. Their work gives good insight on the

wettability change of silica by means of pH change. The silica is virtually insoluble in

deionised water below 150 oC.

2.5.5 Dynamic Nature of IFT in Alkaline Process

Hornof et al. (2000) discussed acidifying paraffinic oil, and flooding it with alkaline

solution. They reported that at high flow rates both alkaline and water flooding recover

similar amounts of oil. They also reported that at low flow rate most of the alkaline is

depleted, and the oil recovery is improved. They attributed this dependence between the

flow rate and the oil recovery in alkaline flooding to the dynamic nature of IFT of

alkali/oil systems. In the case of high flow rate, it seams that the alkali did not have

enough time to react and reach the IFT minima, thus has similar recovery of water

flooding. When the flow rate is low, the alkali finds enough time to react and reduce

IFT which in turn increases the oil recovery.

2.5.6 Heterogeneity Effects on Alkaline Flooding

Heterogeneity affects the efficiency of alkaline flooding. Dokla (1981) studied the

effects of heterogeneity and temperature on the alkaline flooding in sand packs. The

caustic flooding of this system was most effective when the pH was about 10. The

caustic flooding results in higher oil recovery compared to water flooding in both

stratified and heterogeneous sand packs, yet heterogeneity lowered the recovery

compared to the homogenous sand packs. Dokla also indicated that an increase in

temperature increases the recovery.

2.6 Polymers Flooding Water soluble polymers can be used in petroleum applications such as water-based

drilling muds, water production control by relative permeability modification, and

mobility control of oil recovery floods. The polymer is one of the main constituents of


46

ASP flooding. Polymer addition to the water flood increases the viscosity of water. A

target viscosity is set to decrease the mobility ratio (M). The viscosity can be controlled

by the polymer concentration at a given temperature and electrolyte composition. The

increase in viscosity may increase the capillary number, but, this change is not

significant in mobilising the residual oil.

Two main polymer families are used in EOR applications: the polyacrylamides-based

polymers and polysaccharides (Sorbie, 1991). Polyacrylamides are the most commonly

used polymers in EOR applications (Lake, 1989, pp. 319). The polyacrylamide type was

used in this PhD research.

2.6.1 Oil Recovery Mechanism in Polymer Flooding

The main mechanism of oil recovery by polymer flooding in field application is the

improvement in the volumetric sweep efficiency. It permits the flood to reach unswept

portions of the reservoir or porous medium (Lake, 1989; Sorbie, 1991). The polymer

flood does not increase oil recovery by mobilising the residual oil saturation (Needham

and Doe, 1987). The polymers are mainly used in EOR floods to achieve stability of the

flood fronts against fingering; mobility control. Another possible mechanism is the

viscoelastic drag of trapped oil droplets by the polymer flood, which improves the

microscopic sweep efficiency (Urbissinova et al., 2010; Wang et al., 2011). This

contradicts the classical view that polymer flooding does not recover residual oil (Lake,

1989; Sorbie, 1991; Green and Willhite, 1998). In chemical floods like ASP flooding,

the polymer is used as a complement to the alkali or/and surfactant to improve the

overall effectiveness of the process.

2.6.2 Structure and Molecular Confirmation of Partially Hydrolysed Polyacrylamide

The polyacrylamide polymer is a linear chain of acrylamide monomers. The polymer

form that is used in EOR applications is partially hydrolysed polyacrylamide (HPAM),

in which, some of the acrylamide groups are hydrolysed by conversion to carboxyl

group (COO-) giving the polymer an anionic nature. The degree of hydrolysis is usually

around 30% to optimise the polymer’s properties for EOR applications (Lake, 1989, pp.

317). The structure of HPAM and its salt is illustrated in Figure 2-10.


47

The HPAM could be described as a flexible coil with molecular weight that can range

from 0.5 to 30 million Daltons (Green and Willhite, 1998, pp. 101). The HPAM chain is

flexible coil that tends to be straight in low ionic strength (low salt), but its

conformation changes when the ionic strength is high (high salt) as illustrated in Figure

2-11 (Sorbie, 1993, pp. 21). Although, the commercially available HPAM would be

supplied with a stated degree of hydrolysis, when in practical use the amount of

hydrolysis can increase (Sorbie, 1991, pp. 19).

Figure 2-10: The structure of partially hydrolysed polyacrylamide and its sodium salt [Sorbie, 1991].

Figure 2-11: Possible HPAM conformations in response to salinity [Sorbie, 1991].

High Salt Low Salt

Partially Hydrolysed Polyacrylamide and its corresponding sodium salt


48

2.6.3 Polymer Flow

The polymers used in EOR application exhibit non-Newtonian behaviour of their

viscosity. The polyacrylamide shows shear thinning (pseudoplastic) viscosity behaviour.

The apparent viscosity is dependent on polymer velocity in the porous medium (Lake,

1989). Degradation and hydrolysis of the polymer during the flood may cause changes

to its viscosity and thus its ability to maintain a favourable mobility ratio.

2.6.4 Polymer Stability

Polymer must be chemically and mechanically stable to ensure that the polymer will

retain its physical properties for successful mobility control. The degradation of

polymer could alter the viscosity off its targeted value. Degradation can be chemical or

mechanical. The chemical degradation can also be caused by thermal oxidation, free

radical substitution, hydrolysis or biological degradation (Lake, 1989, pp. 331). The

mechanical degradation involves break up of the polymer chain by mechanical

interactions such as high shear in pores. During the preparation of the polymer solution,

a prolonged stirring and strong rotating blades can chop the polymer chain into smaller

lengths (Beazley, 1985). When the polymer chain is chopped into smaller parts, the

ability of the polymer to sustain the target viscosity is undermined. High flow rates can

induce mechanical damage to the polymer chains. The polysaccharide is mechanically

more stable than the polyacrylamide polymer, yet, the polyacrylamide polymer is more

immune to biological degradation than the polysaccharide polymer (Lake, 1989, pp.

331).

2.6.5 Polymer Retention The polymer molecules interact with the pores as they are being transported through a

porous medium. This interaction may bring loss of the polymer to the porous medium

by a few retention mechanisms. There are at least three known polymer retention

mechanisms: adsorption, mechanical entrapment and hydrodynamic retention, Figure

2-12. Adsorption is the process where polymer molecules bound to the wall of the grain

by van der Waals forces and hydrogen bonding. Mechanical entrapment is the process

where the polymer molecule is partially clogging the pore throats and remains strained

out/hanged in the smaller pores. The hydrodynamic entrapment is not well defined but


49

can be described as the process where the polymer molecules halt flowing after getting

into a stagnant flow region within the pores.

Figure 2-12: Illustration of polymer retention mechanisms in porous media [Sorbie, 1991].

Ogunberu and Asghari (2005) proposed that polymer adsorption could be enhanced by

higher flow rates, thus, producing thicker adsorbed layer of polymer. As a result, the

relative permeability to water is further lowered. The type of polymer they used was not

reported.

2.6.6 Permeability Reduction and Relative Permeability Modification Polymer retention reduces the apparent permeability of the porous medium (Green and

Willhite, 1998, pp. 111). The polymer decreases the mobility ratio (M) by the viscosity

increase as well as permeability reduction (Lake, 1989, pp. 327). The polyacrylamide

polymer can change the relative permeability to water significantly to the extent it could

be used for water production control treatment in producing wells (White et al., 1973); a

process referred to as relative permeability modification (RPM). It is believed that

adsorbed polyacrylamide in the presence of oil causes larger relative permeability

change to water (Zheng et al., 2000).

Mechanically entrapped polymer in narrow pore throats

Hydrodynamically trapped polymer in stagnant zones

Flow path through porous medium

Grains

Adsorbed Polymer


50

2.6.7 Residual Resistance Factor

Polymer flooding causes changes to the permeability to water flow in the porous

medium as mentioned above. The residual resistance factor (RRF) is a measure to

evaluate this change and is defined as the following (Lake, 1989):

injectionpolymerAfter

w

w

injectionpolymerBefore

w

w

K

K

RRF

=

µ

µ

RRF is simply the ratio of the mobility of water before and after the polymer flooding.

There are other measures such as resistance factor and permeability reduction factor,

both are related to permeability reduction by polymer injection.

2.6.8 Inaccessible Pore Volume

The inaccessible pore volume, as the name implies, is the portion of pore(s) space that

cannot be entered by polymer molecules. The polymers, which are used in mobility

control flood, were observed to elute in the flooding liquids earlier than anticipated.

Early encounters of such experimental observation of polymer accelerated breakthrough

triggered some disagreement between researchers (Trushenski et al., 1974). IPV is not a

simple phenomenon and perhaps is a result of several mechanisms. According to Liauh

et al. (1982), the IPV phenomenon in reservoir flooding is due to the coupled effects of

hydrodynamic exclusion and thermodynamic equilibrium distribution of polymer

molecules.

Mobility control polymers are substantially large molecules. For example, the partially

hydrolysed polyacrylamide Flopaam 3630S has an approximate molecular molar mass

of 20 million Daltons. Thus, such large molecules like HPAM or polysaccharide may

not have the ability to invade all the space within pores (Lake, 1989, pp. 326). This

causes the polymer to experience the size exclusion effects as seen in liquid

chromatography. Separation of chemicals in size exclusion chromatography (SEC)

occurs exclusively due to differences in molecular size (Braithwaite and Smith, 1996,

2-29


51

pp. 340). This leads the large polymer molecules to elute from the porous medium

earlier than smaller size particles.

2.6.9 Polymer Impact on IFT

There is general agreement that polyacrylamide polymers do not have as much

influence on the IFT compared to the impact of surfactants. Austad and Taugbøl (1995)

showed that the presence of the polymer (xanthan), up to 500 ppm in surfactant/polymer

solution did not affect the ultra low IFT significantly. This is mainly because the

polyacrylamide is not an amphiphilic compound. Stepanow et al. (1996) produced

theoretical calculations showing that the polymers will tend to adsorb at interfaces when

there are potential differences between the phases. This may have more influence on

interface stability rather than on IFT reduction. In practice, although polymer is not

affecting the IFT, it is believed that it contributes to the stability of emulsions produced

in ASP floods (Deng et al., 2002).

2.6.10 Gelation Process The main purpose of gelation process is relative permeability modification (RPM) for

water production control. Gelation is the process where the straight chains of polymer

molecules crosslink with each other to form three dimensional structures (3D) or gels.

The process can take place in surface facilities or in-situ within porous medium. Cross-

linking chemical agents (trivalent cations: Al+++ or Cr+++) are used to form gels in-situ.

The consequences of the process could be the blockage of the flow within the porous

medium or a reduction of the relative permeability to water (Green and Willhite, 1998).

2.7 ASP Flooding An introduction to the ASP process has been covered in Chapter 1. In this section, a

more detailed discussion on the process is given.

2.7.1 Oil Recovery Mechanisms of ASP

The ASP flood combines the actions of the standalone floods of: alkali, surfactant and

polymer (Sheng, 2010). As a result, the ASP flooding inherits the recovery mechanisms


52

of these standalone floods. The action and the application of these stand alone chemical

floods were discussed earlier in this chapter. The main recovery mechanisms of ASP

flood are (Sheng, 2010; Green and Willhite, 1998):

Decreasing capillary forces -which trap the oil- by sharp decrease of the

interfacial tension between oil and water phase (ASP slug).

Generating in-situ surfactants (soaps) by the interaction of the injected alkali

with acidic components of the crude oil

Stabilising the flood using polymers

Synergic combined effects of alkali and surfactant on reducing the IFT

Generation of emulsion in situ which could improve sweep efficiency in heavy

oil recovery (Wang and Dong, 2010).

Combined change of IFT and wettability (Nasr-El-Din et al., 1992)

Viscoelastic drag of oil globules in pores by the flowing polymer solution

(Urbissinova et al., 2010; Wang et al., 2011).

2.7.2 Advantages of ASP Process

• Higher recoveries at lower cost because lower chemicals’ concentrations are

used in ASP compared to standalone chemical flooding (Wyatt et al., 2002;

Chang et al., 2006).

• Reduced surfactant loss by absorption because of the high pH nature of the

process as higher pH decreases the adsorption of anionic surfactants on silica

sands.

• Possible wettability alteration.

2.7.3 Drawbacks

• More intensive workload and designing of new surface facilities (Weatherill,

2009).

• Scale development and build up in producing tubes and pumps (Wang et al.,

2004; Cao et al., 2007).

• Significant number of interactions between the ASP slug/reservoir fluids, ASP

slug/reservoir rock and among the ASP chemicals making the process complex

to design and manage (Weatherill, 2009; Delshad et al., 2002). Alkali is part of


53

the ASP process and may have complex interactions with reservoir fluids and

rocks as described by Bhuyan (Bhuyan, 1989).

2.7.4 Injection Sequence of ASP Flood

The ASP flood is commonly applied as EOR application towards the end of the field

life (Weatherill, 2009, Sheng, 2010), an example of its application as a secondary mode

flooding in Cambridge oil field, Wyoming, USA (Wyatt et al., 2004). Pre-designed

amounts of alkali, surfactants and polymer are mixed in the flooding water in certain

steps. The ASP aqueous solution is then injected as one slug. The most common main

injection sequences are:

1) Pre-flush that could be fresh water or water with other additives to reduce the

concentration of divalent cation (mainly Ca++ and Mg++) to avoid the

precipitation of surfactants.

2) The injection of 0.3-0.5 PV of ASP slug

3) The injection of polymer solution or water as driving agent to push the ASP slug

through and reduce fingering.

2.8 Emulsion and Microemulsions

The emulsions and microemulsions of oil and water are dispersions of one of the phases

in form of droplets in the other and are of importance to oil recovery. Emulsion or

microemulsions (colloidal suspensions) may form upon mixing the oil and an aqueous

solution that contains surface active ingredients such as fine solids, soaps, synthetic

surfactant or asphaltenes (Schramm, 1992). Rosen defined an emulsion as:

“An emulsion is significantly stable suspension of particles of liquid of a certain size

within a second, immiscible liquid. The term significantly stable means relative to the

intended use and may range from a few minutes to a few years”, (Rosen, 2004, pp. 303).

However, there is another petroleum oriented definition that distinguishes between

emulsion and microemulsion based on thermodynamical stability. Healy and Reed

defined the microemulsion as “a stable, translucent micellar solution of oil, water that

may contain electrolytes and one or more amphiphilic compounds (surfactant, alcohol,

etc...)”, (Healy and Reed, 1974, pp.492).


54

The suspensions could be oil-in-water emulsion (o/w) in which the continuous phase

(external phase) is water and the dispersed phase (droplets) is the oil. It also could form

water-in-oil emulsion (w/o), in which the continuous phase (external phase) is the oil

and the dispersed phase (droplets) is the water, Figure 2-13. There is a third phase that

could occur which is described as being bicontinuous. The nature of o/w and w/o

emulsion is easy to conceptualise, however, there is a degree of uncertainty on the

nature of the middle phase.

Figure 2-13: Illustrations of basic emulsion types, gray colour represent water and black represents oil [Edited from Schramm, 2005].

Scriven (1976) proposed a bicontinuous structure to describe the middle phase. He

suggested that this phase has both the oil and water continuous, hence bicontinuous. His

principle view is that both liquids could be continuous in similar analogy to porous

medium filled with fluid. In this case the porous medium is continuous and the fluid is

also continuous. An illustrative sketch of the bicontinuous phase as suggested by Rosen

(2004) to show different possible structures of liquid crystals formed by emulsions is

depicted in Figure 2-14. Bourrel and Schechter (1988) argued that Scriven’s view

could not be rejected or totally accepted because thermal fluctuation of liquids will

prevent the development of persistent structure as long as both phases remain liquids.

As seen, the nature of the middle phase is till a matter of discussion.

Rosen (2004) classified emulsions based on droplets diameter size into macroemulsion,

miniemulsion, and microemulsion with respective sizes of greater than 0.4 µm, between

0.1-0.4 µm and less than 0.1 µm. He also reported another category called multiple

emulsions, in which the emulsion itself can be a dispersed phase of droplets in the

Oil-in-Water (o/w) Water continuous

Water-in-Oil (w/o) Oil continuous


55

droplets of another phase, Figure 2-15. However, in practice, emulsion droplet size in

petroleum could be between 0.2 µm and 50 µm or even larger (Schramm, 1992).

Figure 2-14: Bicontinuous structure of middle phase where both oil and water are continuous [Rosen, 2004].

Figure 2-15: Illustration of multiple emulsion structure of oil-in-water-in-oil and water-in-oil-in-water, [Edited from Schramm 2005].

The type of the microemulsion formed whether it is bicontinuous, water-in-oil or oil-in-

water is classically described by Winsor phase behaviour. This behaviour depends on

electrolyte concentration in the aqueous phase. The phase behaviour of microemulsion

is described further below.

2.8.1 Emulsion size and Chemical Concentration

McAuliffe (1973) prepared emulsions of slightly different droplet sizes. He found that

changes in alkali or synthetic surfactant concentrations could change the size of droplets

in emulsion. More sodium hydroxide or more synthetic surfactant produces smaller o/w

Water-in-Oil-in-Water (w/o/w)

Oil-in-Water-in-Oil (o/w/o)


56

droplets. More hydroxide interacts with oil acids and increases the amount of in-situ

surfactants (soaps) thus increases the surface activity and IFT reduction. Arriola et al.

(1983) showed that the reduction of IFT by surfactant facilitates the break up of large

oil droplets trapped at throat constriction into smaller drops that can flow through.

2.8.2 Permeability Reduction and Emulsion Flow in Porous Medium Emulsion flow in porous media is one of the phenomena which can take place in ASP

process (Lei et al., 2008). When the droplets average size overlaps with the pore throat

size it may block the flow. McAuliffe (1973) studied the flow of o/w emulsion in

porous media. He found that the emulsion reduces the permeability of the cores. He

suggested a blockage mechanism in which the droplets plug or clog fluid flow when it

passed in pores’ constrictions, in the form shown in Figure 2-16. The differential

pressure that is required to push the droplet through the pore throat could be estimated

from the following relation (Kokal et al., 1992);

−=

21

112

rrP σδ

where δP is the differential pressure, r1 and r2 are the radii of the leading front and the

rear of the droplet as depicted in Figure 2-16, and σ is the interfacial tension between

the aqueous solution and the oil.

Figure 2-16: Oil droplet enters pore constriction. [McAuliffe, 1973]

Oil Water

Water Flow

2-30

r1

r2


57

Soo and Radke (1986) proposed some mechanisms of emulsion trapping in the porous

media like straining and interception, Figure 2-17. They also found that the emulsion

decreases the permeability of the porous medium.

Figure 2-17: Droplet capture mechanisms in porous media [Edited from Soo and Radke, 1986]

2.9 Emulsion Winsor Phase Behaviour

Phase behaviour of emulsion (or microemulsion) is regarded as a key aspect of chemical

EOR processes (Green and Willhite, 1998). Emulsion phase behaviour of an

oil/surfactant system relates the type of emulsion (w/o, o/w or bicontinuous) to the

salinity of the surfactant solution, surfactant concentration, temperature or pressure. In

this thesis, salinity refers to the concentration of sodium chloride (NaCl) because it the

most abundant electrolyte found in formation waters of oil reservoirs.

2.9.1 Phase Behaviour Mechanisms

Winsor (1954) compiled a widely referenced textbook: Solvent Properties of

amphiphilic Compounds on emulsion phase behaviour. He reported that the phase

behaviour of oil/surfactant solutions is a function of electrolyte concentrations. He

classified the phase behaviour states of oil /ionic surfactant solution/emulsions into

three types. These three types are illustrated in Figure 2-18 using the terminology given

by Green and Willhite (1998):

Interception

Straining

Flow Direction

Grains Droplets


58

• Lower phase (phase –II also known as Winsor type I): oil droplets dispersed

in continuous water phase, this phase is in contact with oil phase.

• Upper phase (phase +II also known as Winsor type II): water droplets

dispersed in continues oil phase, this phase is in contact with water phase.

• Middle phase (phase III also know as Winsor III): bicontinuous phase, this

phase is in contact with both oil phase and water phase.

Figure 2-18: Typical Winsor phase behaviour as a function of salinity [Based on Healy et al., 1976; Bavière et al., 1997; Green and Willhite, 1998].

In systems obeying the Winsor phase behaviour, the system goes from –II phase to III

phase to +II phase as electrolyte concentration is increased. The most common increase

of electrolyte concentration comes in the form of salinity increase. Many oil/ionic

surfactant solution systems follow the Winsor phase behaviour. Usually such systems

have a blend of surfactants and co-surfactants to avoid the production of highly viscous

phases (Green and Willhite, 1998).

2.9.2 Phase Behaviour Salinity Scans

Salinity scans are preformed to find the phase behaviour of a target oil/surfactant system.

The scans are used to find the optimum salinity of the system. In these scans, several

solutions of the target surfactant(s) at a given surfactant concentration are made with

Oil phase Emulsion

Oil phase Emulsion Water Phase

Emulsion Water Phase

Lower Phase Middle Phase Upper Phase Phase -II Phase III Phase +II Winsor type I Winsor type III Winsor type II Salinity Increase

Under Optimum Optimum Salinity Over Optimum salinity salinity


59

variable salinity. The salinity is increased in increments over a salinity range (Green and

Willhite, 1998). These solutions are then mixed with the target oil at constant oil/water

ratio. Some time is allowed to the mixture to reach equilibrium. The amount of

produced emulsion, remaining oil phase and water phase are then monitored.

Equilibrium is reached when there are no further changes in the amounts of these phases.

The time for equilibrium could be hours, days or weeks (Rosen, 2004). However, the

stability of emulsions is not infinite and thus a time frame relevant to the intended

experiments needs to be defined (Green and Willhite, 1998; Winsor, 1954; Rosen,

2004).

2.9.3 Parameters Affecting the Phase Behaviour

There are several factors that can control or affect Winsor phase behaviour including oil

type, temperature, surfactant type and concentration, electrolyte concentration and

pressure (Green and Willhite, 1998). Electrolytes include all ions dissolved in the water:

monovalent (Na+, Cl-, ect…), divalent ions mainly Mg++, and Ca++. Also trivalent ions

could exist such as Al+++ etc. Higher ion charges have more impact on the stability of

emulsion, and thus, phase behaviour (Schramm and Marangoni, 2000). Non ionic

surfactants are less affected by salinity and more affected by temperature.

2.9.4 Solubilisation Parameters and IFT Correlation with Phase Behaviour Solubilisation parameters quantify the amount of solubilised oil phase and water phase

in the microemulsion and are dependent on salinity. Healy et al. (1976) showed that

there is a correlation between the phase behaviour type of the microemulsions and IFT.

They introduced the solubilisation parameters of petroleum microemulsion with

presence of surfactant. Bourrel and Schechter (1988) defined the solubilisation

parameters of Healy et al. (1976) for oil (SPo) and water (SPw) as the following:

s

oo

V

VSP =

s

ww

V

VSP =

2-31

2-32


60

where Vo is the volume of oil solubilised in the emulsion phase, Vw is the volume of

water solubilised in the emulsion phase and Vs is the volume of surfactant solubilised in

the emulsion phase. For a system that is at lower phase and follows the Winsor phase

behaviour, Vw/Vs decreases while Vo/Vs increases as salinity is increased (Figure 2-19).

Figure 2-19: Behaviour of solubilisation parameters and IFT against Salinity [from Healy et al., 1976].

The plots of solubilisation parameters against salinity intersect at a point where both

parameters are equal. This point of intersection lies in phase III and corresponds to the

minimum IFT. The salinity at which this point occurs is called optimum salinity

(Healy et al., 1976), Figure 2-19. Huh (1979) established the theoretical basis of the

IFT

(m

N/m

) V

o/V

s or

V w

/Vs

Salinity, % NaCl


61

relationship between the IFT and phase behaviour. He expressed the IFT (σ) in terms of

solubilisation parameters; a simplified form of this relation is as follows:

2

=

S

jV

V

cσ

where j = oil or water and c is a constant usually equal to 0.3 depending on the surfactant type.

2.9.5 Phase Behaviour and Maximum Oil Recovery Nelson and Pope (1978) confirmed that the same phase behaviour described by Healy et

al. (1976) can take place in chemical floods in porous media. Nelson and Pope (1978)

also proposed that other intermediate stages or phases may exist between the -II, III and

+II phases. As discussed above, optimum salinity that brings the oil/surfactant system

into phase III, also, corresponds to the minimum IFT. This optimum salinity also

corresponds to the maximum EOR. Higher oil recovery is related to higher capillary

numbers, which in turn are linked to IFT reduction (Austad and Milter, 2000). As a

result, the Winsor middle phase (III) is recommended to achieve maximum oil recovery

(Nelson and Pope, 1978; Flaaten et al., 2009; Liu et al., 2008). The solubilisation

parameters are equal at optimum salinity, therefore, once the middle phase is found, one

can calculate the IFT using Hus’s equation (Equation 2-33). Flaaten et al. (2009)

emphasized the importance of phase behaviour to simplify the screening process of

chemicals used to design effective ASP slug. Therefore, phase behaviour can be used to

estimate the IFT and is a quick method to design effective ASP slugs (Flaaten et al.

2009; Liu et al., 2008).

2.9.6 Emulsion Electrical Conductivity The electrical conductivity of an emulsion can be used to distinguish between o/w and

w/o emulsions. Electrical conductivity is the ability of a material to conduct electrical

charge. Resistivity is the reciprocal of conductivity and is easy to measurer.

2-33


62

In emulsions, the continuous phase of the emulsion governs the conductivity (Schramm,

1992). Oil has high resistivity, thus, w/o emulsions have high electrical resistivity

because the oil is the continuous phase. The o/w emulsions have lower resistivity

because the water usually contains emulsification agents as electrolytes that could

conduct charge (surfactant, sodium ions, chloride ions etc...). Healy et al. (1976)

showed that w/o emulsion has higher resistivity than o/w emulsion as shown in Figure

2-20 below.

Figure 2-20: Electrical resistivity of w/o (Phase +II) emulsion is bigger than the resistivity of o/w emulsion (Phase -II) [Edited from Healy et al., 1976)

2.9.7 Non-typical Winsor Phase Behaviour

Despite the importance of phase behaviour, some authors pointed out that not all

oil/surfactant solutions will exhibit this typical Winsor phase behaviour (Austad and

Milter, 2000, pp. 223; Bourrel and Schechter, 1988, pp. 159). Bourrel and Schechter

(1998) pointed out that the phase behaviour, which is called ”typical” or usual, is not

necessarily always observed. Masahiko (1997) indicated that the surfactant has to have

a co-surfactant to enable the formation of the middle phase microemulsion.

Res

istiv

ity

(O

hm-M

eter

)

NaCl %


63

2.10 Emulsion Droplet Size and Size Distribution Emulsions are by-products of ASP flooding which influence the EOR. In this project,

the emulsion size distribution was determined. This section gives the background

knowledge required to find emulsion droplets size distribution.

2.10.1 Techniques for the Determination of Emulsion Droplets Size Distribution

The determination of emulsion droplets size distribution (EDSD) is important to

interpret the performance of the ASP process. It is also important for the prediction of

pressure drops and the design of pipes (Nasr-El-Din, 1992). Coulter counters can be

used to find EDSD when the emulsion is dilute or further dilution is not expected to

affect the original size of the emulsion (McAuliffe, 1973). When the droplet size is

lower than the optical microscopy resolution limit (~0.5 um), then scanning electron

microscopy (SEM) can be used to determine EDSD after preparing the emulsion with a

cryogenic stage (Schramm, 1992; Masahiko, 1997). If the droplet size is above the

optical microscopy resolution limit, conventional optical microscope can be then used

to determine EDSD. However, a large number of images would be needed to provide a

representative droplet size distribution. Generally, the minimum number of droplets

required to produce a representative distribution is 500 (O’Rourke and MacLoughlin,

2005). This can be a difficult task when considering concentrated opaque emulsions

which is the case for the emulsions found in ASP flooding.

Nuclear magnetic resonance techniques can be also used to find EDSD (Packer and

Rees, 1972). The NMR techniques have several advantages over optical techniques.

Optical techniques require invasive sample preparation and are limited to transparent

diluted emulsions (Hollingsworth and Johns, 2003). In contrast, NMR techniques

involve non-invasive sampling and can work in opaque emulsions.

2.10.2 Determination of the Emulsion Size Distribution Using NMR The use of Nuclear Magnetic Resonance pulsed field gradients (NMR-PFG) techniques

for the determination of emulsion droplet size distribution has been established for

around 40 years (Packer and Rees, 1972; Tanner and Stejskal, 1968). The theory of


64

pulsed NMR can be found at textbook of Farrar and Becker: Pulse and Fourier

Transform NMR: Introduction to Theory and Methods (Farrar and Becker, 1971).

When a sample of liquid molecules are introduced into NMR spectrophotometer and

two separated pulses of gradient magnetic fields are applied at specific times, the

molecules will return back an echo of NMR signal with a measurable amplitude.

Depending on how this returned NMR signal was excited, it could be called spin-echo

or stimulated spin-echo. The degree of attenuation in the amplitude of the returned

NMR spin-echo is increased when the amplitude of gradient pulses is increased, which

can be controlled experimentally. This dependence between the amplitudes of both the

applied gradient magnetic fields and the returned NMR echoes can be used to study the

diffusion of molecules (Tanner and Stejskal, 1965). There are theoretical models which

can use diffusion to infer information on structures containing the liquids (Tanner and

Stejskal, 1968; Murday and Cott, 1968). For the NMR to be applied, the samples to be

analysed must contain molecules with nuclei having non-zero angular magnetic moment.

Isotopes with odd mass numbers offer net angular magnetic moment that is not equal to

zero, 1H and 13C are examples of such nuclei.

2.10.3 Molecular Diffusion

Diffusion is the random motion of molecules in a medium driven by thermal

fluctuations. Diffusion itself is not the aim of this investigation, however, its effect on

the echo-spin amplitude allowed further manipulation of this phenomenon. If there was

no diffusion and the magnetic field applied on the sample was perfectly homogenous,

the spin- echo will not lose magnitude. The diffusion will affect the amplitude of the

spin-echo (Farrar and Becker, 1971). A larger diffusion invokes more attenuation to the

spin-echo amplitude. The motion of the nuclei by diffusion reduces the amplitude of

the re-focused signal or spin-echo. When the motion of nuclei is restricted by a

boundary, the re-focus quality is improved and the spin-echo amplitude is less

attenuated. This classifies molecular diffusion from a NMR signal perspective as

restricted and unrestricted diffusion (Tanner and Stejskal, 1968).


65

2.10.4 Unrestricted Diffusion

The unrestricted diffusion involves the random motion of molecules in regions where it

hits no boundaries at least during the measurement time. The diffusion is measured

between the two field gradients pulses. Time depended gradient is a gradient that is only

switched on for some time during the NMR pulse sequence execution, a pulsed field

gradient (PFG). In contrast, a steady gradient is a gradient that is present throughout the

execution of the NMR pulse sequence. Tanner and Stejskal (1965) demonstrated and

proposed a model to measure unrestricted diffusion using pulsed field gradients. Their

method used the NMR pulse sequence of Carr Purcell Meiboom Gill (CPMG) (Farrar

and Pecker, 1971). In their model, the loss of the spin-echo signal (R) is an exponential

function of the diffusion coefficient (D), the amplitude of the pulsed field gradient (g),

the duration of gradient pulse (δ), and the time between the gradient pulses (∆). The

NMR signal sequence is displayed in Figure 2-21. The expression of this relation is

shown in Equation 2-34 (Tanner and Stejskal, 1968, 1965):

)3

()( 2 δδγ −∆−=

gD meR

where γm is the magnetogyric ratio (constant for each isotope), for hydrogen proton (H1)

γm = 2.675 x 108 (Hz/T) (Hollingsworth and Johns, 2003).

Figure 2-21: PFG- CPMG-NMR pulse sequence used for the measurements of unrestricted diffusion coefficients and emulsion droplet size distribution [based on Packer and Rees, 1972]

t = 0 t = τ t =2τ

90o

Pulse 180o

Pulse

t1

Gradient Pulse

δ

∆

Spin-Echo Signal Accusation

Gradient Pulse

δ

2-34


66

2.10.5 Restricted Diffusion and Emulsion Size Distribution

The restricted diffusion involves the random motion of molecules where it hits a

physical boundary during the measurement time (Neumann, 1974; Tanner and Stejskal,

1968). The restriction could come in the form of the interface boundary between two

immiscible liquids, like oil and water. The restriction affects the measured diffusion

coefficient compared to the unrestricted diffusion. This restriction effect on the

measured diffusion coefficients enabled the use of restricted diffusion in estimating the

dimensions of structures, which bound liquids such as emulsion. The diffusion of

emulsion droplets themselves during the measurement time is implicitly assumed zero.

Restricted diffusion models are theoretical formulae which aim to relate the observed

loss of spin-echo signal to the size of structure that restricts the diffusion. Tanner and

Stejskal (1968) developed simple models of restricted diffusion using NMR. They used

these models to estimate the thickness of simple structures such as the thickness of

water layer bounded between mica sheets. They, also, used octane-in-water emulsion as

an example of restricted diffusion but the model was treated as one dimensional

problem. Neumann (1974) developed mathematical models for restricted diffusion in

planar, cylindrical and spherical boundaries for steady field gradients. Specific to this

research, the diffusion in spherical cavity could be used to estimate the emulsion droplet

size distribution. Based on private communications, prior to the publication of Neumann

work on bounded diffusion, Murday and Cott extended his theoretical work in 1968 to

calculated diffusion in pulsed field gradients within spherical boundaries (Murday and

Cott, 1968). Packer and Rees (1972) then extended Murday and Cott (1986) work to

determine the size and size distribution of emulsions. They employed the pulse

sequence in Figure 2-21. The model of the spin-echo signal attenuation (RSP) due to

restricted diffusion in spheres (droplets) is as follows (Packer and Rees, 1972):

( )

Ψ−×−

−

=∆

∑∞

=1222222

22 2

)2(

12exp

),,,,(

m DPmDPmmmm

DPSP

DDrg

DgrR

ααδ

ααγ

δ

2-35


67

where ( ) ( )

( ) ( ))(expexp2

exp2)(exp222

22

δαα

δαδα

+∆−+∆−−

−−−∆−+=Ψ

DPmDPm

DPmDPm

DD

DD

∆ and δ have the same definition as described above, r is the radius of the emulsion

droplet, and DDP is the diffusion coefficient of the liquid bounded in the emulsion

droplets; water in case of w/o emulsion and oil in case of o/w emulsion. αm is given by

the mth positive root which satisfies the following equality of the following two Bessel

functions:

)()(1

25

23 rJrJ

rαα

α=

Emulsions usually consist of one or more distributions of droplet sizes rather than one

single size for all droplets. Rsp will thus need to be evaluated for each possible droplet

size. The following expression gives the overall observed spin-echo (RObs) of emulsion

with droplet size distribution of P(r):

∫

∫∞

∞

∆=∆

0

3

0

3

)(

),,,,()(

),,(

drrPr

drDgrRrPr

gRDPSP

Obs

δδ

The spin-echo signal from oil needs to be resolved from the water spin-echo signal for

this expression to be used. High field NMR machines could satisfy this requirement. If

low field NMR machines are used, then the oil and water NMR signals may overlap and

further processing of the signals might be required (Pena and Hirasaki, 2006; Aichele et

al., 2007). It is accepted that many emulsions have log-normal distribution (Schramm,

1992). Packer and Rees (1972) assumed a log-normal size distribution for the emulsion

to ease mathematical treatment of the analysis:

( )( )

−−=

2

2

21 2

)ln()2ln(exp

22

1)(

σπσavdr

rrP

where dav is the average droplet size, r is the radius of the emulsion droplet, σ is the

distribution width (variance) and π is a constant equal to 3.14.

2-36

2-37

2-39

2-38


68

More recently, Aichele et al. (2007) developed a technique that uses low field NMR to

find the emulsion droplet size distribution. This method can show binodal distributions

of the emulsions and it assumes no size distribution. The techniques requires that T1~T2

which is the case with low magnetic frequency NMR. Therefore, it is probably only

applicable in low field NMR instruments. Another limiting factor of this technique is

the large amount of emulsion required to produce strong enough NMR signals. The

technique needs long time to acquire the NMR signals, 5 to 7 hours to achieve

acceptable signal-to-noise ratio.

Another NMR technique was described by Hollingsworth and Johns (2003) that uses

NMR-PGF stimulated spin-echo (STE). STE was first introduced by Hahn (1950).

Tanner (1970) proposed the use of STE in conjunction with pulsed filed gradients for

diffusion studies to allow longer measurement times. STE as developed by Hahn (1950)

Show several spin-echoes after the leading echo. The spurious echoes are unwanted for

the emulsion droplet size determination. Van Den Enden et al. (1990) added a third

gradient pulse (Homospoil) to get rid of spurious spin-echoes. The Homospoil removes

the unwanted spurious echoes. The pulse sequence of the stimulated spin-echo NMR-

PFG –STE is shown in Figure 2-22.

Figure 2-22: Pulse sequence of NMR-PFG-STE [Adapted from Hollingsworth and Johns, 2003]

In the work of Hollingsworth and Johns (2003), an experiment can be done in less than

15 minutes with a 300 MHz NMR machine. Their treatment to extract EDSD involves

90o

Pulse 90o

Pulse

∆

Accusation

Homospoil gr

Gradient Pulse δ

Gradient Pulse δ

90o

Pulse


69

intense mathematical analysis and assumes no prior size distribution. However, the

mathematics involved needs special knowledge of inverse problem and regularisation

schemes. The use of the technique becomes much simpler if normal-log distribution is

assumed for the emulsion just like the assumption made by Packer and Rees. Recall that

it is widely accepted that many emulsions have log-normal distribution (Schramm,

1992).

Although, the use of regularisation schemes is desired to resolve possible binodal

emulsion distributions, it was satisfactory for the scope of this PhD work to adapt a pre-

assumed log-normal emulsion distribution.

2.10.6 Limitation of NMR for Droplet Size Distribution Determination

The limitation of the NMR usage for the droplet size distribution comes from the

physics of NMR and the diffusion. The NMR signal is lost after some time from the

time of the signal excitation, therefore, the allowed time between the pulses of the

pulsed field gradients is limited (Johns, 2009; Farrar and Becker, 1971). As a result,

there is a maximum droplet size, as a rule of thumb, that is measureable by available

NMR techniques to give sufficient restricted diffusion. This maximum radius is the

random mean square of molecular diffusion length given by (Johns, 2009):

rmax ≈ (2∆DDP)0.5

where DDP is the diffusion coefficient of the dispersed liquid molecules and ∆ is the

measurement time between the gradient pulses. When the droplet size is beyond a

certain size, the diffusing molecules may not hit a boundary. In this case, the molecule

will not see the boundary during the measurement time ∆ between the two gradient

pulses and the spin-echo attenuation will be of the unrestricted type. As a result, larger

diffusion coefficients will allow the measurement of larger droplet sizes. The

implication is that as the average droplet size is getting bigger, the restricted diffusion

model will approach the unrestricted model as it can be seen in Figure 2-23 and Figure

2-24. Consequently, the EDSD of water-in-oil emulsion is easier to measure than the

oil-in-water because generally oils have lower diffusion coefficients.

2-40


70

0 5 10 15 20 25 30 35 400.5

0.6

0.7

0.8

0.9

1

Pulsed Field Gradient (G/cm)

Sp

in-E

cho

Att

en

ua

tion

Curves of Restricted and Unrestricted Diffusion Modelswith Different Emulsion Sizes

UnrestrictedRestricted 0.1umRestricted 0.4umRestricted 1umRestricted 10um

Figure 2-23: NMR signal attenuation curves for restricted and unrestricted diffusion as function of field gradient magnitude for o/w emulsion with different average droplet sizes for ∆=400 ms, δ= 2 ms, D (diffusion coefficient of oil) =3.75 x 10-11 m2/s.

0 5 10 15 20 25 30 35 400

0.2

0.4

0.6

0.8

1


Sp

in-E

cho

Att

en

ua

tion

Curves of Restricted and Unrestricted Diffusion Models with Variable Emulsion Size

UnrestrictedRestricted 0.1umRestricted 1umRestricted 80um

Figure 2-24: The restricted and unrestricted curves of w/o emulsion with given sizes for ∆=400 ms, δ= 2 ms, D (diffusion coefficient of ASP water) =2.20 x 10-9 m2/s.

2.11 Analytical Determination of Surfactant and Pol ymer The introductory chapter (Chapter 1) and this chapter discussed the effectiveness of the

ASP process. Part of its success is attributed to the co-existence of the three ASP

chemicals in one slug. Chemical analyses of the produced fluids were preformed in

different studies on ASP floods (Wang et al., 2009; Li et al., 2009; Hou et al., 2005).

The study of the effluent could help to interpret the process happening during the oil


71

recovery by ASP flood. Consequently, the literature was searched to find analytical

methods to determine the concentrations of the surfactant, polymer and alkali.

A comprehensive and detailed literature review of surfactant and polymer determination

is beyond the scope of this PhD. Therefore, only those methods which were used, tested

or highly considered for application in this PhD will be reported.

2.11.1 Polyacrylamide Analytical Determination Review

The literature provided a range of chemical and instrumental analysis methods for the

quantitative determination of the polymers. Taylor and Nasr-El-Din (1994) have

reviewed several polyacrylamide determination methods and the limitations of each

method. They reviewed seventeen methods, including: size exclusion chromatography

(SEC), turbidimetry, N-bromination of amides, amide hydrolysis with ammonia

detection, fluorescence spectrophotometry, polarography, viscosity, infra red

spectroscopy and ultraviolet spectroscopy. They suggested that SEC and N-bromination

of amide groups were more suitable for oil field samples than other methods. Sorbie

(Sorbie 1991, pp. 26), also, reported these two methods in his textbook Polymer

Improved Oil Recovery; namely the N-bromination of the acrylamide groups and SEC

for the analysis of polyacrylamide concentration encountered in oil field applications.

One of the methods reported by Taylor and Nasr-El-Din (1994) above involved

determining the concentration of polyacrylamide in drilling mud. This method was

developed by Palma et al. (1984) and they reported a good accuracy in field trials. It is

based on the liberation of ammonium by strong base induced hydrolysis of the

amide/acrylamide groups in the polyacrylamide. An ammonium selective electrode is

used to detect the liberated ammonium. It could be assumed this method would work for

the ASP samples since it worked for heavily contaminated mud. However, this method

is not suitable for the ASP laboratory floods as it needs relatively large mass samples to

librate enough ammonium to be detected. This method is more applicable to field

samples were large masses can be secured, while in laboratory experiments undertaken

in this research, the size before dilution was 0-3 mL (depending on how much aqueous

phase in the sample), that is roughly 3 g. Moreover, they did not report the sensitivity of

the method to the degree of hydrolysis occurring on the polymer in the drilling process.

Although not stated in their paper, it seems that they simply assumed a constant degree

of hydrolysis.


72

2.11.2 Size Exclusion Chromatography for Polyacrylamide

Separation of substances in SEC occurs exclusively due to differences in molecular size

(Braithwaite and Smith, 1996, pp. 340). Lin has mentioned that it is hard to efficiently

separate high molecular weight polyacrylamide of more than 5 × 105 g/mol by

commercially available columns (Lin, 1995, pp. 270). In this PhD work, polyacrylamide

polymer(s) with molecular weight significantly higher than this value were used. Hagel

and Janson pointed out that overload effects were observed with SEC especially when

long chain polymers are used (Hagel and Janson 1992, pp. A278). For example, they

recommended a maximum of 5 mg/L for dextran. For our application which involves

quite long polyacrylamide chains, this limit may be even less.

Despite all the above mentioned challenges, SEC remains an effective option for the

determination of the polyacrylamide. Beazley (1985) showed that SEC worked well in

contaminated samples obtained from an oilfield. He reported a detailed methodology for

the application of SEC. He used a diol (glycol) column with a mobile phase of a mixture

of 0.1 M NaClO4 and 0.005 M pentanesulfonic acid solution. The relatively large size of

the polyacrylamide molecule compared to most potentially interfering species allows

the usage of the SEC with contaminated samples. Therefore, it has been used to find the

concentration of polyacrylamide polymer in soil waters (Lu et al., 2003). Hence it is

applicable for use with contaminated samples. In order to make the SEC method work,

the large molecules of the polyacrylamide are needed to be mechanically sheared and

filtered otherwise the column will be plugged. The usage of the hydrogen peroxide may

help to degrade the polyacrylamide into smaller fragments (Beazley, 1985). Recently

Wang et al., (2009) have used high performance liquid chromatography (HPLC)

including SEC for the analysis of ASP slug components. They used a diol column but

they did not report how they prepared the samples. However, the SEC method requires

a long preparation and test time and when a large number of samples are considered for

analysis -which is the case in this PhD research- the time required becomes prohibitive.

Therefore, SEC was not used in this work.

2.11.3 The N-Bromination of the Amide Group- Starch Iodide Method

The N-bromination of the amide group in the polyacrylamide was more attractive in

terms of number of samples able to be processed per hour and all the materials required


73

for the method, were available. Although, the method has been automated by Taylor

(1993) and further improved by Taylor et al., (1998), it was more convenient to use the

manual method described by Scoggins and Miller (1975 and 1979). The manual method

is reasonably accurate as long as the degree of hydrolysis of the polyacrylamide is

known and the interferences are eliminated. The automated method may suffer from

sulphonate interference (Taylor et al., 1998), but this was not mentioned as a possible

interference for the manual method. Therefore, the calibration line between intensity

and concentration should take account of the existing interference sources and degree of

hydrolysis. It is important to mention that the SEC is also sensitive to the hydrolysis

degree in the polyacrylamide (Taylor et al., 1998). Therefore, using the SEC to

determine the polyacrylamide concentration will not resolve this shortcoming of the N-

bromination method. The hydrolysis of the polyacrylamide increases with temperature

and alkalinity.

2.11.4 The Step and Mechanism of the N-Bromination Process

The reaction mechanism of the N-bromination process as suggested originally by

Scoggins and Miller which was slightly improved by Taylor (Scoggins and Miller, 1979;

Taylor, 1993) is shown below:

R C

O

NH2 + Excess Br2 R C

O

NH

Br+ HBr

Essentially complete1)

Moderately fast

H CO-

O

Na+Br2 +fast

IrreverableNa+ + H+ + 2Br- + CO22)

BrOH + Na+ + H+ + Br- + CO23)

Slow-minimal interference

H CO-

O

Na+

R C

O

NH

Br4) + H2O BrOH+

Rapid Equilibrium

R C

O

NH2


74

BrOH5) + H2O+2I- + 2H+ HBrI2 +fast

6) I2 I3-fast+ Starch .... Starch (blue complex)+I-

The N-bromination method involves the addition of excess amount of saturated bromine

water (excess bromine relative to amide or acrylamide groups) to a diluted sample of

polyacrylamide solution (in water), reaction (1). The bromine starts bromination of the

acrylamide group in the polymer. This means the bromine replaces one hydrogen atom

on the amide groups of the polymer. Some of the bromine will not react because of its

excess over the amide groups. Then, an excess amount of sodium formate solution

(excess formate relative to bromine) is added to destroy the excess bromine, reaction (2).

After some time a solution of cadmium iodide- starch is added. Then, the bromide on

the polymer’s amide groups oxides the iodide to iodine, reaction (5). The presence of

iodide/iodine leads to the formation of triiodide and with the presence of starch, it gives

the known blue colour of starch-triiodide complex, reaction 6, (Lambert, 1951A). The

intensity of the blue colour is used to quantify the polymer concentration. The quality of

the starch is very important in this method to get a stable colour (Lambert, 1951B;

Scoggins and Miller, 1979).

2.11.5 Surfactant Determination

2.11.6 ISO 2271 The concentration of alkoxylated alcohol sulphate and alpha olefin sulphonate

surfactant types can be determined by a very well established method and recognised as

an international standard method ISO 2271. The method is described in the textbooks of

Surfactants Analysis (Schmitt, 2001, pp. 491), Handbook of Detergents (Spilker, 2005,

pp. 255) and the Analysis of Detergents and Detergent Products (Longman, 1975).

The method is based on the two phase titration developed initially by Epton (1948) with

methylene blue as the indicator (single indicator). One of the phases is the aqueous

phase and the other is the organic phase, a chloroform or dichloromethane. Like other

titration methods, an endpoint must be reached to determine the surfactant concentration


75

in the samples against a known concentration and volume of titrant. The ISO 2271

method, however, used a mixed indicator of bromide/disulphine blue that was proven to

have the sharpest endpoint of all previously tested indicators (Spilker, 2005, pp. 256).

The endpoint can be realised visually by a colour change as used in the original ISO

2271 or potentiometrically. Potentiometric detection of the endpoint will require a

special electrode that is selective to surfactant ions (Spilker, 2005, pp. 263).

The two phase titration- dimidium bromide/disulphine blue involves titrating a cationic

surfactant (benzethonium chloride also has a commercial name known as Hyman 1622)

and the determination of surfactant concentration with dimidium bromide and

disulphine blue. This method would be a good choice if there was a limited number of

samples to be analysed. The fact that it is a titration method and in each step there is

mixing and inspection of the colour change make the method labour intensive and time

consuming when a large number of samples are needed to be analysed. There have been

reports on automating the ISO 2271 (Spilker, 2005, pp. 258), but this was not an

accessible option for this PhD project. This ISO 2271 method is good and reliable, and

it would have been used in this project if the samples number was small.

2.11.7 HLPC for Surfactant Determination

High performance liquid chromatography (HPLC) was also considered for application

in this research. HPLC separates the species in a liquid sample by injecting the liquid

and specific solvent through specially designed porous column. The separation occurs

as a result of physical and chemical differences between analyte species such as polarity

and hydrodynamic size. At the end of the column, these species could be detected by

different detection methods such as mass spectrometry, infrared (IR) or ultraviolet (UV)

spectrophotometry. Although different detection methods exist, the most commonly

employed are UV detectors for quantification. There are other physical bases for the

separation of species which will not be discussed here. The detailed explanation of the

chromatography process and its fundamentals and applications can be found in

Chromatography: fundamentals and applications of chromatography and related

differential migration methods, Part A. Fundamentals and techniques, in a chapter by

Snyder and edited by Heftmann (Snyder, 1992). The literature also provided details on

column and mobile phase selection for many surfactants among which is the alpha

olefin sulphonate surfactants (Schmitt, 2001, pp. 238).


76

One limitation of the use of the HPLC lies within the detection of the surfactant UV

absorbance. The problem comes when the species do not have strong UV peak or have a

peak shared with other species. This is the case with the surfactants used in this PhD

project, namely the alkoxylated alcohol sulphate and alpha olefin sulphonate. The

sulphonate and sulphate surfactants used in this PhD have polar sulphate or sulphonate

groups, non polar branches and straight chains of hydrocarbons respectively (aliphatic

chains). The absorbance of such aliphatic surfactants comes within the range of 190-210

nm, the same absorbance range of solvents and common impurities encountered in

HPLC, resulting in poor noise to signal ratio (Schmitt, 2001, pp. 196). To avoid this

problem, non direct detection methods can be used in which a substance with known

absorbance is employed. Despite the fact that Wang et al. (2009) used HPLC for the

analysis of surfactant in ASP effluents and reported the type of column and the mobile

phase, their surfactant was of the alkyl benzene sulfonate type. This surfactant has a

benzene ring which has a maximum absorbance at 225 nm. Therefore, it can be detected

directly by the UV detectors of the HLPC system. The benzene ring on their surfactant

have clear absorbance UV peak, while, such a ring does not exist in our surfactants.

Pois and Agterof (1985) described the use of HLPC for the determination of ortho/para

linear alkylxylene sulphonate, alkyltoluene sulphonates, and linear alkylbenzene

sulphonate obtained from samples containing some oil and polymer. Note that these

surfactants all have benzene ring(s). Therefore, in this case, it is possible to use direct

UV detection method. To remove the polymer and unwanted oil, they precipitated the

polymer in water/alcohol solution and used solid phase separation filters (silica packed

filters with C18 hydrocarbon chain to make it hydrophobic) to remove any remaining

oil traces as well as the precipitated polymer. This filter captures the oil traces and the

precipitated polymer but allows the surfactant to pass through. The filtered solution was

then injected into the HPCL column. They also specified the columns types and the

mobile phases.

2.11.8 Spectrophotometric Methods

From a technical standpoint of view, using the HPLC method would involve

considerable time. Such high molecular weight molecules like polyacrylamide polymer

are difficult to handle with chromatographic methods for concentration analysis. The

potential large time required to develop the HPLC method and the financial expenses


77

associated with columns cost as well as the time to analyse about 200 samples has

motivated the need for a simpler method. Spectrophotometric methods, were found to

be the quickest to adopt and the lowest in cost to setup especially as the essential

instruments, cuvettes, dyes and related chemical were already available for both the

surfactant and the polymer. Consequently, spectrophotometric methods were preferred.

Brilliant green dye was used to determine the surfactant and the N-bromination was

selected for the polymer determination. Further discussion about the methods employed

is presented in Chapter 3.

2.12 Impact of ASP Chemicals on Environment

Although the environmental impact of the ASP slug is not part of this PhD project, it is

beneficial to briefly discuss some of the possible environmental side effects of ASP

chemicals because the environment is becoming a shared global concern. The ASP slug

in this project contains HPAM, sulphate surfactant and NaOH as alkali. The alkali will

probably be consumed in short time by the rock and soil, thus, will not have severe or

long term adverse effect on the environment.

Generally, the surfactant may not be considered as a threat to the environment nor is the

polyacrylamide. This may not be true for all surfactants of which some may cause

negative effects on the echo-system especially if they find their way to surface water

(Blasco et al., 2003). In one hand surfactants are generally required to be stable for

applications in industry. On the other hand, environment welfare requires ultimately

biodegradable surfactants (Steber and Berger, 1995). In regard to chemical EOR the risk

is not substantial for humans, however, it could present a threat to the environment

(Britton, 2000). The propoxylated surfactants used in this PhD research are of the

alcohol ether sulphate family. This family show stability and more resistance to

biodegradation as the branching is increased which is required for EOR application, but

is ultimately biodegradable (Steber and Berger, 1995). This probably satisfies the

environmental concerns.

The acrylamide monomer has been found to posse some toxicity to organisms

(Takigami et al., 1998). The acrylamide monomer could be harmful, but polyacrylamide

polymer itself is not harmful, it is biodegradable and finds applications in agriculture

(Kay-Shoemake et al., 1998). However, under irradiation with ultraviolet light or high


78

temperatures (95 oC) the polymer may suffers degradation and small amounts of

acrylamide monomer could be released (Caulfield et al., 2003). Acrylamide was

recently placed under the spotlight for its possible relation to cancer in humans as

acrylamide can form in fried foods (Mucci et al., 2003). Polyacrylamide used in an oil

field may not be easily exposed to UV light and generally reservoirs with high

temperature may not be suitable for ASP floods. Also, Wen et al. (2010) have reported

that biodegradation of the polyacrylamide does not release acrylamide monomers. Thus,

the production of acrylamide monomer in the ASP flooding is probably not an issue.

However, more studies are needed to prove that any accidental leakage of

polyacrylamide into fresh water aquifers will not result in acrylamide monomer being

released into the fresh water. Usually, very large amounts of polymer (tonnes) are

injected in chemical EOR projects which magnify the impact of any potential

environmental damage.

According to the available literature, it seems that the ASP chemicals possess some

degree of environmental hazard. Perhaps, it is fruitful to conduct environmental studies

in relation to any ASP flooding prospective project within the local area of a targeted oil

field. A study should parallel the technical assessment of the ASP flood feasibility. In

particular, if a candidate field for the ASP flooding is near or close to a substantial

underground fresh water reservoir, more care is required to avoid surface spills of ASP

slug. It is also important to ensure that the well casings integrity is in good condition

and will not allow any leakage of the ASP slug near or close to fresh water reservoirs. It

is evident from field experience that some old or badly segmented casings may allow

injected fluids to leak between the casing’s cement and rock formation up to the surface.

This leaked water which was intended to displace the oil may reach the surface or leak

into other shallow non-producing layers that could contain fresh water or be

hydraulically connected to fresh water aquifers. At shallow depths, some geological

layers may be a major fresh water aquifer which could be the main source of fresh water

for local communities. If this happens, some of the polyacrylamide may reach such

aquifer and may contaminate the aquifer with acrylamide or surfactants.

79

3 Chemical Analysis of the ASP Slug Components

This chapter presents the analytical and instrumental methods which were adapted and

developed in this research to measure the concentrations of ASP components in the

samples recovered from ASP floods. Some literature on the analytical methods was

already discussed in Chapter 2. The preparation of ASP slug is described in detail in

Chapter 4 and the actual ASP floods are described in Chapter 5. The concentration

profiles of the alkali, polyacrylamide and surfactant could be used to understand the

effect of heterogeneity on the ASP slug integrity and relate it to EOR. In this work, the

alkali/hydroxide concentration was determined by the simple measurement of the pH

value. The polymer concentration was determined by N-bromination of amide groups

which is based on the classical starch-triiodide method. The surfactant concentration

was determined by manipulating the transformation of a colourless leuco-base of

triphenylmethane dye (brilliant green) into its coloured form by the addition of

surfactant. This later method was further improved and significant changes were made

to the method. The use of Fourier Transform Infra Red- Attenuation Total Reflection

for simultaneous determination of the surfactant and polymer was trailed on a zinc

selenide crystal, but was not fully successful.

3.1 Background and Motivation

The technical motivation to consider the determination of the concentration profiles of

ASP components is that it could aid the interpretation the effects porous medium

heterogeneity on the enhanced oil recovery. This PhD was not intended to be committed

to develop analytical methods for the determination of the ASP chemical. However, it

has proven to be a challenging process to analyse the ASP chemicals and academic

motivation was aroused. The academic motivation was the hope to compile a paper that

describes the analytical methods to analysis the ASP components in one place, it was

rather scattered in different references. The anticipated total number of samples was

Chapter 3: Chemical Analysis of ASP Slug Components

80

large for the determination of the three ASP components. Thus, the methods needed to

be simple and low in cost. Innovative ideas were tested to determine the surfactant and

polymer simultaneously by the use of FTIR-ATR which was not successful. Eventually,

based on literature in Chapter 2, spectrophotometric methods were found easier and

lower in cost to implement. Another advantage is that at the start of this investigation all

the materials and instruments for these spectrophotometric methods were found already

available in the laboratories within UWA which helped to save time.

3.2 Description of the Samples

The injected ASP slug in the sand pack floods has 0.5 % (w/v) alkali (NaOH), 1% (w/v)

surfactant (Alfoterra 145-S4), and 1550 ppm polymer (Flopaam 3630 S). The effluents

from the ASP floods are multiphase and multicomponent by nature making it a

challenging task to measure the concentration of each component, Figure 5-10 in

Chapter 5 shows some such samples. A sample may consist of oil, an

emulsion/microemulsion and an aqueous phase. The aqueous phase contains polymer,

surfactant and alkali and some emulsified oil. Some solids may also be present from

microscopic debris from the sand grains, but the water flooding during the secondary

recovery is expected to remove all loose solids. The sand was washed by deionised

water and dried before use as described in detail in Chapter 5.

Some of the ASP chemicals may move and partition into the oil or emulsion because of

the nature of the multiphase fluids, it becomes increasingly difficult when the

concentrations of these samples in the oleic phase are considered for determination. The

surfactants are well known to partition between the oil and the water phases. The

polyacrylamide molecules are large and highly water soluble, therefore, it is usually

assumed that they reside entirely in the water phase. The alkali is ionic and is assumed

to reside largely in the water phase.

The aqueous component was the only part of the effluent considered for analysis in this

investigation; otherwise, the task would be overwhelmingly difficult. The approach

taken was to use existing techniques and make improvements were necessary.


81

3.3 Representative Sample and Sampling Uncertainty

A representative sample is a smaller sample that is taken from a bulk substance and that

has similar physical prosperities and chemical composition resembling its larger bulk

substance. Longman (1975) gave a good explanation of this concept by using tomato

paste as an example, he pointed out that there is a minimum size of the sample where its

composition is representative of the average composition of the bulk tomato paste.

The alkali, surfactant and polymer may not be distributed homogenously/evenly within

each sample, as only part of the aqueous phase in each sample is taken for further

analysis, this could impose some uncertainty in the representativeness of the sampling.

However, the water taken for analysis at least is the third, if not the whole, aqueous

phase present in the sample, note that the total size of each sample collected from floods

is 3 mL and can go down to 0.5 mL when the flow is impaired. In the case where the

sample is almost entirely filled with aqueous phase, an amount of 1 mL was taken for

analysis. When the sample was smaller than 1 mL the entire water part was taken. As

the maximum size of each sample collected from floods was only 3 mL. It was assumed

that the samples were effectively representative of the actual concentrations of the ASP

chemicals in the produced water.

3.4 Beer’s Law and Spectrophotometry

Beer’s Law is fundamental to spectrophotometry and will be briefly explained here.

Beer’s Law is also known as Beer-Bouguer-Lambert Law (Christian, 1994, pp. 414).

Other texts also call it Beer-Lambert Law (Braithwaite and Smith, 1996, pp. 292).This

law relates the concentration of chemical species in a solution to the absorbance of an

incident monochromatic beam of light passing through the solution. It simply states that

the absorbance of a light beam is directly proportional to the concentration and path

length (solution thickness) of the sample. The law Beer-Lambert is defined as:

clI

ILogAbsorbanceA

o

ε=

−==

3-1


82

where Io is the intensity of incident light beam hitting the sample, I is the intensity of

light beam coming out of the sample, l is the length travelled by light in the sample in

(cm), c is the concentration in (mol per L) and ε is the molar absorptivity in (mol-1 cm-1

L). Although, these are the common units used in Beer’s Law, it is still possible to use

other concentration units. The Beer’s Law usually holds for monochromatic light at low

analyte concentration (Christian, 1994, pp. 416).

In spectrophotometric methods several standard samples of known concentrations of an

analyte are used to make an analytical calibration curve. The absorbance of each

solution sample is plotted against the concentration of each solution. Then an empirical

fitted curve, known as analytical calibration curve, is made between the absorbance and

concentration. This curve could be used to determine the concentration of the substance

in a sample with unknown concentration by measuring the absorbance of the sample

(Christian, 1994, pp. 9).

3.5 The Spectrophotometer Model and Detector Linearity

In this research a double UV/Vis beam spectrophotometer (PerkinElmer Lambda 25)

was used for the analytical determination. The linearity of the spectrophotometer’s

detector was checked and found highly linear to ensure the spectrophotometer accuracy.

As a measure to check the light detector linearity several diluted samples of Brilliant

Green (BG) dye were made. A stock solution of Brilliant Green was made without

adding sodium sulphite to preserve the solution colour. An amount of 0.005 g of BG

powder was dissolved in 25 mL of methanol and solution was diluted with DW to 400

mL. Seven samples of different dilutions of this stock solution were made and its

absorbance was measured. A linear fitting curve was then made using Excel®

(Microsoft Corporation). The results are shown in Figure 3-1.

The linearity of the line and the detector were judged based on the obtained correlation

factor of the absorbance data and the fitting line. The correlation factor was 0.9995

indicating high linearity of the detector. The wavelength calibration had been done on

the account of other workers in the laboratory. This account was supported by the fact

that the BG found to have a peak at 624 nm, a very close value to the reported value in

the literature (Duxbury, 1993; Karukstis and Gulledge, 1998).


83

Spectrophotometer Light Detector Linearity

y = 1635.6489x - 0.0256

R2 = 0.9995

0.00

0.50

1.00

1.50

2.00

2.50

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014

Brilliant Green Dye Concentration %(w/v)

Abs

orba

nce

Figure 3-1: Linearity check of the spectrophotometer light detector.

3.6 Sampling of ASP Floods Effluents

The raw samples which are collected from the ASP flooding contain the recovered oil,

the ASP slug and may contain emulsion. Gravity will segregate the phases by density

difference, and the aqueous phase resides at the bottom of the sample. The following

procedure is followed for sampling and dilution in preparation for the concentration

determination:

1. The ASP effluent are sampled directly from the sand pack effluent and collected

in cylindrical glass vials (3.5 mL). A fraction collector was used to automat the

collection at a constant interval time of 42.86 min to collect 3 mL per sample.

2. The volume of the aqueous, oil and microemulsion in the raw samples was

measured first to establish the oil and water production rates.

3. The aqueous phase was extracted using a pipette and the exact mass of the

extracted aqueous phase was determined.

4. The samples were diluted 6-15 times. The dilution of each sample was recorded.

5. A volume of 2 mL from step 4 was taken for the surfactant analysis using the

brilliant green method.

6. A volume of 1-2 mL from step 4 was diluted a further 8-10 times. The dilution

was recorded.


84

7. A volume of 6 mL from step 6 is taken for pH measurements to calculate

hydroxide ion concentration.

8. The same 6 mL from step 7 are used for the determination of polymer by the N-

bromination method.

The polyacrylamide polymer showed nonlinear absorbance above concentration of 30

ppm using the N-bromination method. Therefore, the ASP slug samples collected from

the floods were diluted by a factor of 50-150 times to bring the concentration down to

the linear range. The initial polymer concentration is 1550 ppm. The produced ASP slug

should have concentration equal or less than 1550 ppm but not higher than this value,

thus, diluting with 60-100 times brings the polymer concentration below 25 ppm.

3.7 Surfactant Determination

Concentration determination of surfactant was required in this research and it is

reasonably well described in the literature. Some of the literature has been already cited

in Chapter 2. In this research, anionic surfactants of the alkoxylated alcohol sulphate

were used, Figure 3-2. Earlier in the research alpha olefin sulphonate type were also

used. The determination of the surfactant concentration in liquids will depend on its

type: nonionic, anionic, cationic or zwitterionic. It will also depend on the nature of the

liquid phase that caries the surfactant and any other ions existing in the liquid matrix.

OO

OO

OSO

OO

Figure 3-2: The structure of the propoxylated alcohol sulphate that was used in the ASP slug, commercially known as Alfoterra® 145-S4.

3.7.1 Spectrophotometric Method Based on Brilliant Green

A search was conducted for a simple and quick method to determine the concentration

of the surfactant in the large number of samples. This led us to consider the use of a


85

method using BG, a green dye which has a colourless leuco-base. Pobiner and Hoffman

Jr (1982) described the application of the BG dye for the determination of sulphate and

sulphonate surfactants concentration in aqueous solutions. They showed that this dye

has a linear calibration curve for concentration range between 60-400 ppm.

Figure 3-3: The photo shows the brilliant green (green-blue) on the left and its colourless leuco-base on the right. The real colour is green-blue but the camera captured it as blue. The leuco-base solution of this dye is essentially colourless with low greenish hint,

Figure 3-3. When the sulphate or sulphonate surfactants are added to this solution,

some of its original colour is restored and it becomes green. The intensity of the

recovered colour depends on the concentration of surfactant concentration. This

intensity of the colour can be measured in a spectrophotometer.

3.7.2 Spectrophotometric Properties of Brilliant Green The visible spectrum of the BG and many other triphenylmethane dyes are well studied

(Duxbury, 1993; Karukstis and Gulledge, 1998). It comes in its powder form under

different names; Brilliant Green, Basic Green 1, diamond green and others more,

regardless of the name it has Chemical Abstract Service code (CAS number) of 633-03-

4. It is from the Triphenylmethane branch of dyes, it is soluble in water and alcohols

(Duxbury, 1993). It has a molecular weight of 482.63 g and chemical formula is

C27H34N2O4S.

The BG spectrum has two peaks close to 430 and 630 nm, both are clear and sharp

(Karukstis and Gulledge, 1998). A typical absorbance scan of the BG in water is shown


86

in Figure 3-4. In this PhD work, the main peak was found at wavelength of 625 nm in

deionised water in agreement with literature. When the sulphate surfactant is present in

solution the peak shifts towards 634 nm. It was also found that for low surfactant

concentration the peak is at or close to 625 nm and for higher concentration is at 634 nm.

This may present a challenge for accurate determination of the surfactants. This was

overcome by scanning in wavelength range that includes both 625 and 634 nm, the

absorbance of the peak in a scan whether is at 625 nm or 634 nm or even between both

is easily found by using the MAXIMUM function of Excel® (Microsoft Corporation).

Brilliant Green Spectrum in DW

0

0.02

0.04

0.06

0.08

0.1

0.12

340 380 420 460 500 540 580 620 660 700 740 780 820

Wavelength (nm)

Ab

sorb

ance

Figure 3-4: The absorbance spectrum of brilliant green in water. Note at 490 nm, there is a spectral flat zone.

3.7.3 Brilliant Green Leuco-Base Reaction

When the powder of BG is dissolved in water or methanol it gives green-blue colour

solution as shown in the image of Figure 3-3. The solution becomes colourless when its

leuco-base is formed. This can be achieved by adding sodium sulphite at pH of 9. The

green colour is restored by adding sulphate or sulphonate surfactants as mentioned

above. The colour recovery is not limited to these two types of chemicals/ surfactants,

but these two are under study in this research. According to Longman (Longman, 1975,

pp. 217), the surfactant micelles act as solubilisers for the dye but are impervious to


87

sulphite ions. So, the surfactant micelles separate the dye stuff and the sodium sulphite

which re-generate the coloured form of the dye, as a result some degree of colour is

restored.

Longman (Longman, 1975, pp. 217) referred to this process in his book as the

Abramovich reaction were he cited two different references in two languages: French

and English, these are printed here for interested readers Zutrauen H.A. and L.T.

Minassian-Saraga- Comptes Rend., 240, 869, (1955); chem. Abstr. , 49, 7977 (1955).

Also, Pobiner and Hoffman Jr (Pobiner and Hoffman Jr, 1982) cited a reference in

Russian which was not obtained in this research and it is printed here E.S. Abramovich,

U.S.S.R Patent No. 122,336.

Pobiner and Hoffman Jr (1982) proposed the following as a possible reaction in Figure

3-5. Though they showed the reaction of BG (basic green 1) with chloride ion in their

original work, while, this one shows the reaction of BG with hydrogen sulphate counter

ion:

N(C2H5)2

N(C2H5)2

C

N+(C2H5)2

N(C2H5)2

Na2SO3 at pH 9

Anioinc Surfactant

+

HO S

O

O

O-

H

Leuco base"colourless"

Quinoid form"Blue-Green"

HO S

O

O

O-

Figure 3-5: Proposed reaction of colour restoration of BG leuco base upon addition of

surfactant.

Perhaps, this proposed reaction is a very simple representation of the actual reaction;

this is because the dye molecules may exist as monomers as well as dimmers or other

higher order structures (Duxbury, 1993). However, this simple representation is

adequate for this work.


88

3.7.4 Essential Modifications to the Brilliant Green Method

Pobiner and Hoffman Jr (1982) used samples which contain no polymer, no alkali and

more importantly no oil traces in the form of emulsion or microemulsion. Their

solutions were simply pure solutions of surfactants. They used sodium borate solution

as a buffer to maintain a constant pH value of 9.0. Their buffer was of low capacity, but

it was satisfactory for their work. In contrast, in this PhD research, samples did have all

the contaminants mentioned above. In particular, the presence of sodium hydroxide in

the surfactant solution was more profound. For a given surfactant concentration the

NaOH significantly reduced the intensity of the green colour compared to a pure

surfactant solution, Figure 3-8. Moreover, the restored colour by surfactant addition

becomes colourless in few hours indicating slow side reaction. Therefore, increasing the

strength of the buffer was essential to overcome this problem.

A sum up of the modifications which were made in this research to the reported BG

method are:

1. Increase the buffer capacity by increasing the amount of dissolved borax close to

its solubility limit (~5 g per 100 g of water).

2. Omit the use of the H2SO4 for tuning the pH to 9.0, instead just used the NaOH

and HCl. Note, the use of NaOH can be omitted when the correct amount of HCl

required to tip the pH just below the 9.0 pH value, but since both were used in

the first BG solution, then it was preferred to keep the electrolyte concentration

in the BG solution the same all the way in this research.

3. The pH was adjusted to just below 9.0, close to 8.9, because the NaOH in the

samples does increase the pH slightly even after dilution.

4. Scan after 4 minute rather than after 1 minute.

3.7.5 Material Used in the Preparation of BGS

• Brilliant green powder: Sigma-Aldrich with ~90% dye content.

• Sodium sulphite: UNIVAR analytical reagent of Ajax chemicals, with minimum

assay of 98%.

• Sodium Tetraborate decahydrate: UNIVAR analytical reagent of Ajax chemicals,

with minimum assay of 99.0%.


89

• Hydrochloric acid (HCl) solution: UNIVAR analytical reagent of Ajax Finchem.

Minimum assay is 31.5% and maximum assay is 32% (w).

• Sodium hydroxide (NaOH) pellets: UNIVAR analytical reagent of Ajax

Finchem, minimum assay of 97%.

• Methanol was of a reagent grade.

3.7.6 Preparation of Brilliant Green Mother Solution

These steps of preparing the Brilliant Green Mother Solution (BGMS) account for the modifications mentioned one page earlier:

1- Solution 1: Dissolve 20.900 g of sodium borate (borax) in the 400 mL of DW.

The solution should be prepared in a closable glass bottle with lid. Shake

rigorously until all the borax is dissolved, if necessary, the solution may be

warmed a little bit above room temperature to ease the dissolution of the borax,

note that this mixture is close to the solubility limit of borax in water at room

temperature of about 22 oC (~5 g per 100 g of water).

2- Dissolve 0.052 g of BG powder in a 35 mL of methanol in a 50 mL beaker. This

solution becomes green-blue.

3- Pour this BG/methanol solution to Solution 1 which after the addition becomes

deep green.

4- Rinse the 50 mL beaker that was used in step 2 with a further aliquot of 35 mL

methanol. Three rinses (each about10 mL) should be enough to remove

remaining BG, this rinsing solution should be added to Solution 1, at this stage

Solution 1 takes deep green colour and total methanol added is 75 mL.

5- Dissolve 6.000±0.005 g of sodium sulphite into Solution 1, Shake rigorously

until all the sodium sulphite is dissolved, some foam may develop, but it is not

stable and disappears in few minutes.

6- It is important to shake long enough to ensure all the borax and sodium sulphite

are totally dissolved. Solution 1 can be heated above room temperature but

below 35 oC to expedite the dissolution of borax and sodium sulphite.

7- Add 5.585 g of hydrochloric acid solution (concentration of 32% (w)), this

effectively adds 1.787 g of hydrochloric acid and 3.798 g of water.

8- Add 0.855 g of sodium hydroxide pellets.

9- Steps 7 and 8 will ensure that the pH is at or just below 9.0.


90

10- Then, filter the solution by using filter paper that can retain medium size

crystalline matter, Whatman filter paper number 541 was used for this purpose.

11- Close the bottle properly to prevent evaporation and leave undisturbed and use

after two weeks. Experience found that this storage time allowed some scale to

build up on the glass bottle’s wall and it reduces scatter at lower wavelengths.

With time some borax may scale on the bottle glass wall which may reduce the

buffer capacity and change the pH slightly but the reagent was found suitable to use.

A new analytical calibration curve was required to be performed every few days for

new measurements.

3.7.7 Preparation of BG Reagent Samples

The BGMS prepared above is used to make Brilliant Green Reagent Samples

(BGRS) ready for use for surfactant determination. The following procedure

describes the process:

1. Place 3.0 mL of the BGMS in a 15 mL test tube that can be closed.

2. Add 5.0 mL of DW to the BGMS, total size by now is 8.0 mL.

3. The BGRS are ready for use.

3.7.8 Scanning Procedure

After the BGRS’s are ready, the following steps are followed to make the

spectrophotometric scanning:

1. Take 2.0 mL of solution of the surfactant samples which have been diluted by 6-

15 times as described in section 3.6.

2. Add the 2.0 mL to the 8.0 mL BGRS, the total volume is now 10 mL.

3. Mix gently and avoid making bubbles, especially for higher surfactant

concentrations.

4. Wait 4 minutes and start scan in the range 340-850 nm.

5. Find the peak by Excel® function MAX (Microsoft Corporation)


91

3.7.9 Analytical Calibration Curve Elementary work on the relationship between the absorbance and the concentration of

the surfactant in the BGRS samples showed that the absorbance is linear with respect to

concentration at low concentrations of propoxy sulphate surfactant but starts to lose

linearity at higher concentrations as shown in Figure 3-6. This can be handled by fitting

a nonlinear function to the analytical calibration curve. It was found that diluting the

original samples from the flood by a factor of 6 to 15 brings the surfactant concentration

down to the linear region of its absorbance, Figure 3-7.

Calibration Curve of Al-145-S4

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

surfactant concentration (%w)

abso

rban

ce

Figure 3-6: Analytical Calibration Curve of BGR with sulphate surfactant.

Calibration Curve of Al-145-S4 within the linear Ab sorbance Range

y = 3.7312x + 0.0042

R2 = 0.9928

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 0.02 0.04 0.06 0.08 0.1 0.12

Surfactant Concentration (%w)

Abs

orba

nce

Figure 3-7: Analytical Calibration Curve of BGRS with sulphate surfactant within linear absorbance region.


92

3.7.10 Elimination of the Effect of NaOH Concentration

It was important to increase the borax buffer capacity to eliminate the NaOH effects.

The original buffer as proposed by Pobiner and Hoffman Jr (1982) did not have enough

capacity to resist pH change by the NaOH of the ASP flood samples.

The desired pH of the BG solution is 9.0 to maintain a colourless leuco-base of the BG.

The borate buffer readily does maintain the pH close to this value and with proper

adjustment it can hold the pH at 9.0. It has dissociation constant of 9.23 (Holtzhauer,

2006). There are other buffers which may replace the sodium borate but since it was

already used and described by Pobiner and Hoffman Jr (1982), it was continued to be

used in this project. Some examples of substances which can be used as buffers are:

acetate, phosphate, carbonate, and other more.

Buffer Capacity effect on Colour Stability Against Alkaline

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6

NaOH concentration (% w/v)

Abs

orba

nce

Low capacity buffer High cabacity buffer

Figure 3-8: The sodium hydroxide reduced the absorbance of 0.4% surfactant when low capacity borate buffer is used (solid squares), Higher capacity borate dropped the absorbance and effectively sustained the colour intensity (empty squares), the colour was maintained for weeks indicting the elimination of any possible slow side reaction. The sodium hydroxide effect was eliminated by increasing the capacity of the sodium

tetraborate (borax) buffer. Figure 3-8 shows that the buffer with higher capacity

resisted the increase of the NaOH and the absorbance remained stable.


93

The highest possible buffer capacity is limited by its solubility. The amount of the borax

in its buffer was increased to its solubility limit at room temperature. The borax

solubility was found to be 5.3 % (w/w) at roughly 22 oC, In agreement with the

solubility of 6.3 g per 100 g of water at room temperature reported in literature (Dean,

1992).

3.7.11 Time Effects and Aging of the BGRS The absorbance of several samples with different surfactant concentrations was

measured with respect to time to decide optimum waiting time for scanning. The

absorbance was recorded for 11 minutes after adding the surfactant to the BGR, Figure

3-9. It is clear that the absorbance is stable from minute one. Pobiner and Hoffman Jr

used a time of 1 minute to start scanning after the mixing of the BGRS with the

surfactant samples (Pobiner and Hoffman Jr, 1982). However, a closer look to Figure

3-10 will show that there are some small fluctuations in the first three minutes, thus, it’s

preferred to perform scanning after 4 minutes from mixing the surfactant with the

BGRS.

Stability of Absorbance in the BGRS for different S urfactant Concentrations

0.00

0.10

0.20

0.30

0.40

0 1 2 3 4 5 6 7 8 9 10 11 12

Time (minutes)

Abs

orba

nce

0.15%

0.15% ( in ASP slug)

0.07%

0.05%

0.005%

Figure 3-9: The behaviour of BGS absorbance with different surfactant concentrations for 11 minutes. The blue line is of a sample that also contain polymer.


94

The absorbance at 625 nm (exactly at the peak of 0.005% of surfactant concentration)

showed some oscillations, whereas, at 634 nm (slightly off peak for the 0.005%

concentration) those oscillation disappeared as shown in Figure 3-10. These oscillations

were not further studied, but it is believed they may reveal some of the kinetics of the

reaction which could be of interest.

Small Scale Oscillations of Absorbance of Surfactant Concentration of 0.005% (wt)

0.04

0.0402

0.0404

0.0406

0.0408

0.041

0.0412

0.0414

0 2 4 6 8 10 12Time (minutes)

Abs

orba

nce

at 6

34

nm

0.019

0.0195

0.02

0.0205

0.021

0.0215

0.022

Ab

sorb

ance

at 6

25

nm

625 nm 634 nm

Figure 3-10: The absorbance of 0.005% sulphate. One scan was made at 634 nm and the other at 634 nm of the same sample. For the calibration curve the maximum absorbance value around the region of 634 nm was used.

3.7.12 Optimisation of the Volumes of BGMS and DW in BGRS

The total volume of the BGRS which contains 3 mL BGMS, 5 mL DW and 2 mL of the

analyte was selected based on experimental optimisation study which is discussed here.

The total volume of the BGRS is set to 10 mL, including the 2 mL analyte. This total

volume of the BGRS was chosen for ease of handling. The analyte volume was also set

to 2 mL for ease of handling. The optimisation was merely aimed to decide the

optimum proportions of the BGMS and DW in the BGRS. The optimisation is

constrained with the total size of BGRS of 10 mL.


95

Sizes of 2 mL BGMS+6 mL DW, 3 mL BGMS+5 mL DW, 4 BGMS mL+4 mL DW

and entirely BGMS (8 mL + 0 mL DW) were tested. Four sets of these samples were

made. Then, 2 mL from surfactant samples of known concentration were added to the

samples in each set. Four surfactant concentrations were tested. Figure 3-11 shows the

results of this work.

Absorbance Dependence on the Amount BGMSin BGRS

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 1 2 3 4 5 6 7 8BGMS volume in 8 mL of BGRS (mL)

Abs

orba

nce

1% 0.1% 0.4% 0.7%

Figure 3-11: The effect of adding more BGMS on the absorbance of BGRS with different surfactant concentration, 1%, 0.1%, 0.4% and 0.7%.

Experiment findings showed that the stock BGMS solution needs to be diluted further

by DW to increase sensitivity to surfactant concentration. When the BGRS was entirely

filled with BGMS, the width of the absorbance was narrow and the different surfactant

concentrations produced overlapping peaks. The widest width in the absorbance was

observed when the BGMS was 2 mL and the DW 6 mL and at these volumes the

spacing between the corresponding absorbances of the different surfactant

concentrations was bigger.

The lower sizes of the BGMS in the BGRS may increase the absorbance, however, it

also weakens the buffering capacity by dilution. Higher dilution of the BGMS depletes

the buffer capacity which is meant to have enough ion reserve to absorb the NaOH.

Another limitation comes from the amount of BG dye available for reaction. Higher


96

dilution factors means that the amount of BG dye stuff available for reaction and

restoration of colour is lowered, thus, a pre-mature plateau appears in the absorbance-

concentration curve. Therefore, volumes of 3 mL of BGMS and 5 mL of DW were

selected to from the 8 mL of the BGRS. It was thought this proportion is better than

other proportions to preserve good response to absorbance and at the same time reduces

the buffer dilution.

3.7.13 95% Confidence Level and Error Determination

Pobiner and Hoffman Jr (1982) did not report the minimum detectable concentration on

which the method was giving reasonable results. Furthermore, the essential changes had

been made to the original methods as discussed earlier. Therefore, it is important to

know the minimum concentration of the surfactant on which this modified method can

be used. Consequently, statistical calculations were preformed to get the 95%

confidence interval to decide the minimum measurable surfactant concentration.

Several samples with different surfactant concentrations were determined. The number

of samples per one concentration ranged from 6 to 8. Data are shown in numerical

format in Table 9-1 through to Table 9-4 in Appendix A1. The resultant 95%

confidence range from these tables is shown in Figure 3-12 as percentage of the mean

value of absorbance/ concentration. Note that, some of the samples contained sodium

hydroxide and polymer besides the surfactant. Some samples just contained the

surfactant alone. The samples which have polymer are marked in Figure 3-12 by the

empty squares and those containing only the surfactant are marked by the crosses. This

figure shows that the ±95% confidence range is below 6% of the concentration’s mean

for concentration from 0.1 % down to concentration of 0.005%. Below a concentration

of 0.005%, the confidence range is about 14% of the mean. See Table 9-1 through to

Table 9-4 for numerical details.

Thus at a concentration of 0.005% or below, the confidence range goes quite high, up to

14% which means a concentration of 0.005% could give a measured value between

0.0043% up to 0.0057%, and as the real samples are diluted, for a further dilution of ten

times, the concentration range becomes 0.043% to 0.057%.


97

When the samples have higher surfactant concentrations of more than 0.005%, the 95%

confidence range is close to or below 5% which means a reading of 0.05 could be

determined with a value between 0.0525 and 0. 0475%. If the original samples were

subjected to 10 times of dilution, these translate to 0.525 and 0.475%. Therefore, based

on Figure 3-12 the minimum recommended concentration to be determined for

samples is 0.005%.

± 95% Confidence Limit as a Percentage of the Conentration Vs Surfactant Concentration

0

2

4

6

8

10

12

14

16

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11

Surfactant Concentration (% w/v)

± 9

5%

Co

nfid

ence

Lim

it as

a

perc

enta

ge

of th

e m

ean

valu

e

(%)

contains ASP surfactant only

Figure 3-12: The ±95% confidence range as a percentage of the mean. A power plot is used to approximate interpolation of the 95% confidence range of remaining concentrations. The errors in the N- bromination method were not investigated in depth as much as was

the case with the BG method. This is because the N-bromination method was adopted in

the same manner described by its original references without significant change and the

minimum limits of the methods were reported to be around 2 ppm. While, the BG

method was modified in this work and a statistical check became necessary.

3.7.14 Emulsion Interference

Some samples of the ASP floods were found to be slightly contaminated with emulsions

and that caused increased absorbance due scattering. This could give false results and

over estimate the actual surfactant concentration. The main contaminant was the


98

emulsions which naturally occur in the ASP floods. When a glass pipette is inserted to

extract aqueous phase during the sampling procedures described in Section 3.6, it

penetrates and disturbs the oil and emulsion layers. Some of the emulsion may stick to

the pipette glass and get into the extracted water phase. The contamination resulted in a

higher absorbance and caused scatter. In order to address emulsion contamination, a

number of control samples with known surfactant concentration in contact with

emulsion were subjected to deliberate disturbance to induce contamination. These

samples were compared to uncontaminated samples. Figure 3-13 shows three scans of

these samples for comparison; one uncontaminated and two contaminated. More

samples were made to have further insight into the absorbances at 340 and 850 nm and

its relation to the peak at 634 nm, Figure 3-14.

Scanes of Three Samples of Same Conecteration (0.075%) and Different Degree of Contimination

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

340 390 440 490 540 590 640 690 740 790 840

Wavelength (nm)

Abs

orba

nce

Higher contimination

Lower contimination

Figure 3-13: The scans of three samples one uncontaminated and two contaminated with emulsion, note the absorbance at 850 and 340 (nm). It was observed that the emulsion contamination did not change the shape of the peaks

but did shift up the height of the whole scan profile, see Figure 3-13. The lower

wavelengths far from the peak showed higher increase in absorbance, indicating

Rayleigh type of scattering which is inversely proportional to the fourth power of the

average emulsion diameter. Furthermore, the peak’s height also has increased more than

expected from the scatter. When the scatter was subtracted the beak height remained


99

higher than expected for the known surfactant concentration. Perhaps, the emulsion

helps to restore some of the colour and work to enhance the BG reaction described in

Figure 3-5 besides its contribution to the scatter.

It was found that both the absorbances at 340 and 850 nm have a degree of correlation

with the absorbance at the peak of 634 nm, Figure 3-14. The observed correlation

suggested that mathematical model for correction could restore the actual concentration

and extract the contamination influence on the absorbance. This model is discussed in

Section 3.7.16.

Cross-blot of Absrobance at 634 and 340 nm of Sulphate Surfactant

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.2 0.4 0.6 0.8 1 1.2 1.4Absorbance at 634 (nm)

Ab

sorb

an

ce a

t 3

40

or

85

0

(nm

)

Absorbance at 340 nmAborbance at 850 nm

uncontiminated

contiminated

Highly contiminated

Figure 3-14: The peak absorbance (at 634 nm) of contaminated and uncontaminated samples is influenced by the degree of contamination which is reflected with increase in absorbance at 850 and 340 nm.

3.7.15 Polymer Interference

The existence of the polymer did not affect the intensity of colour, Figure 3-15, except

for low concentrations of polymer in which small fluctuations were observed. The

reason of this is not understood and was not investigated further.

The actual samples collected from the floods are diluted 6-15 times for the surfactant

determination and the polymer concentration (initially 1550 ppm) should be more than

30 ppm after dilution. Thus, the may have minimal impact on the uncertainty in the


100

determination of surfactant. This is acceptable because the method is not intended for

high accuracy, rather, indicative of the ASP flood effluent profile.

Polymer Effect on BGR Absorbance with Presence of Surfactant at Different Concentrations

0

0.05

0.1

0.15

0.2

0.25

0.3

0 50 100 150 200 250polymer concentration (ppm)

Abs

orba

nce

0.1 0.075 0.05 0.03 0.012 0.003 0.005

Figure 3-15: The polymer effect on the absorbance of BG at different surfactant

concentrations, the legend above is %w concentration of sulphate surfactant.

3.7.16 Mathematical Model to Correct for Contamination

The scattering from emulsion discussed above caused higher absorbance and resulted in

over determination of the surfactant concentration. The emulsion interference has to be

eliminated either by destroying the emulsion or by subtracting the contribution of

emulsion in the absorbance. This emulsion in the extracted water is hardly visible to the

eye by its cloudy transparent texture but, its influence is clear in the scans. In order to

keep the method technically simple, mathematical correction approaches were first

investigated before looking to the more complex chemical separation methods like

using chloroform or other organic solvents to eliminate the emulsion contamination.

Several approaches were tried but only the one method which worked the best will be

discussed.

One of the mathematical approaches which were tried was checking the relation

between the peaks at 634 nm and 430 nm in uncontaminated and contaminated samples.


101

Also, relating the area under the peak curve to the contamination was tried. Another

trivial approach was to subtract the scattering contribution from the peak by calculating

an average absorbance based on the shoulders of the peak; that is the average

absorbance of both wavelengths 750 and 490 nm. These approaches did not work.

One more approach that was tried involved introducing a correction term to the Beer-

Lambert’s Law and at least it showed some encouraging results. This approach involves

the introduction of a corrective term into Beer-Lambert’s Law. The approach has four

assumptions:

1) The emulsion contributes to the restoration of the colour beside the sulphate

surfactant.

2) The colour restoration by emulsion is proportional to the amount of emulsion

present.

3) The emulsion also causes more absorbance by scattering.

4) The degree of contamination by emulsion could be estimated by following the

absorbances at 340 and 850 nm. Smaller droplets contribute bigger scattering in

accordance with Rayleigh scattering, thus, smaller wavelengths like 340 nm will

result more scatter.

Assumption 1 and 2 stems from the observation that for a known fixed surfactant

concentration, larger peaks with respect to their baseline were observed as the

contamination was increased as shown in Figure 3-13. The more the contamination the

larger the peak, perhaps, more dye stuff gets promoted to involve in restoring the colour

when there is some emulsion. There are studies indicating the effect of

microenvironment on the BG absorbance (Duxbury, 1993; Karukstis and Gulledge,

1998), however, such reactions are out of the scope of this PhD and no further

investigations were taken.

Assumption 3 was an observation rather than an assumption, the emulsion does

contribute more absorbance by Rayleigh scattering as shown in Figure 3-13 and

Figure 3-14.

Assumption 4 is supported by the experimental observation that the absorbance of

uncontaminated samples at wavelength 340 nm is independent of surfactant


102

concentration, Figure 3-14. When emulsion is present the scattering from emulsion at

340 nm becomes dependent on the amount of emulsion. The absorbance is larger for

higher degree of emulsion contamination. Therefore, the amount of emulsion could be

implicitly estimated from monitoring the absorbance at 340 nm.

The absorbance at 340 nm of uncontaminated samples was found to be independent of

surfactant concentration and was almost constant with an average value of 0.0316 and

standard deviation of 0.0025, Figure 3-14. Note that this value is dependent on the age

of the BGMS, thus, for each run this value could be calculated from the uncontaminated

samples which are used to make analytical calibration curve. On the contrary, this value

goes higher for the contaminated samples. In the contaminated samples, it varied from

sample to sample depending on the degree of contamination and reached a value close

to 0. 3. It was also observed, as it can be seen in Figure 3-14 that the contamination

influences the absorbance at 850 nm.

All these observations suggested to relate the absorbance at 340 and 850 (nm) to the

extra absorbance observed at the 634 nm (main peak used for surfactant determination).

The average value obtained from uncontaminated samples by subtracting absorbance at

850 (nm) from the absorbance at 340 nm was used as a reference. Note that for this

reference value, only uncontaminated samples are used. The Beer’s-Lambert Law with

the correction factor is as follows;

( ) ( )( )

clSA reference

referenceSample

AA

AAAA

ε

−

−−−

= 850340

850340850340

where, A634, A850 and A340 are the absorbances at 634, 850 and 340 (nm) respectively.

The other terms of Beer-Lambert’s Law are described in Section 3.4. The subscript

“sample” refers to the absorbances of the sample being measured and the subscript

“reference” refers to the average absorbances obtained from uncontaminated samples.

The constant S is given different values until the slope of the calculated points are close

to unity. The correlation factor between the fitting line and the point is also preferred to

be close to unity. For the control samples in this graph, the constant S gave best fit when

3-2


103

it has a numerical value of 1.25, it corrected the observed concentration of the

contaminated samples to approximately the actual values.

The observed absorbance at maximum peak (634 nm) is divided by this correction

factor to correct for the interference from emulsion. When the sample is clean, the

term( ) ( )referenceSample

AAAA 850340850340 −−− is close to zero or too small, thus, the correction

factor

( ) ( )( )

−

−−−

reference

referenceSample

AA

AAAA

S 850340

850340850340

is close to unity which in turn means no correction

is applied. When there is a high concentration, the factor increases above unity, thus, the

calculated concentration will be reduced.

The following few paragraphs are intended to explain the concept of the correction

factor. Several aqueous samples were made with known surfactant concentrations. Then,

very small amounts of oil were added to these to induce the formation of emulsion. The

amount of the oil is very small and it was safe to assume that the surfactant

concentration in the aqueous phase did not change. Following this, some of the aqueous

phase was diluted and the surfactant concentration was determined in the procedures

described in Section 3.7.7 and Section 3.7.8.

A comparison plot between the present concentration of the samples and the found

concentrations was made; this comparison plot is shown in Figure 3-16. In this plot the

y-axis is the found (measured) concentration and the x-axis of the graph is the present

(known) concentrations of the surfactants. If the emulsion contamination has no effects,

all the points should lay on a line with a slope of unity which means the present

concentration is equal to the found concentration. The empty polygons in the graph are

the found concentrations before the correction. The found surfactant concentrations

were in most cases higher than the present concentration indicating the interference

from emulsion by scattering (assumption 3) and enhancing the absorbance of the BG

(assumption 1 and 2). In the same plot, the black solid points are the representation of

the found concentrations after applying the correction factor. Several uncorrected

measurements (empty polygons) were brought on or closer to the line which now

represented by black solid points after the correction was applied. Few points were

displaced further from the line. The common nature of these deflected samples is that all

contain polymer besides the surfactant.


104

In summary, the correction factor showed limited success. In the control samples, those

containing polymer showed negative response to the correction factor and went further

from their real concentration as it can be seen in Figure 3-16. While those containing

only surfactants were brought closer to their real concentration by the correction factor.

Despite its partial success, it helped to reduce the contribution of scattering on the

apparent absorbance, thus, will be used for the surfactant determination. This factor is

not to be used in the polymer determination. Further investigation into destroying the

emulsion was not pursued due to time limitations.

Application of Correction Factor to Contaminated Sample by Brilliant Green Modified Method

y = 0.9236x - 7E-05

R2 = 0.9322

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 0.02 0.04 0.06 0.08 0.1 0.12Present concentration (% w/v)

Fou

nd c

once

ntra

tion

(% w

/v) Before correction After correction

Figure 3-16: Comparison plot between real concentrations and observed concentration before and after the application of correction factor. The trend is the best fit of the corrected points (solid circles). (For S=1.25, (A340-A850) reference =0.0316)

3.8 Polymer Quantitative Determination

In Chapter 2 of this thesis several methods of polymer determination were mentioned

and two were discussed further, namely SEC and N-bromination of amide groups. The

procedure of the N-bromination method here is mainly based on the procedure

developed by Scoggins and Miller (Scoggins and Miller, 1975 and 1979) with some

Samples contain Polymer


105

modifications adapted from Taylor (Taylor et al., 1998). The materials and steps to

prepare the reagents and the procedure to determine the polymer concentration are in

Appendix A2.

3.8.1 The Analytical Calibration Curve for Polymer Several standard samples of polyacrylamide of different predetermined concentrations

were prepared from polyacrylamide solution in DW and ASP slug. The concentrations

of the polymer in these samples were determined by the N-bromination. The materials

and steps to prepare the reagents and the procedure to determine the polymer

concentration are in Appendix A2. The calibration curve of the polymer from the ASP

slug is shown in Figure 3-17. The resulted calibration curve of the polymer alone is

shown in Figure 3-18.

The calibration curves show that the polymer in the DW has higher absorbances for

same concentrations present in the standards from the ASP slug. This disagreement

between the two calibration curves is perhaps due to the hydrolysis. The polymer in the

ASP slug experiences high alkalinity because of the presence of NaOH, whereas, the

polymer in the DW is subjected to less pH. It is well established that high pH

environment increases the hydrolysis of the amide groups in the polyacrylamide (Levitt

et al., 2011).

Analytical Calibration Line of Polyacrylamide in ASP of SP23

y = 0.0400x - 0.0095

R2 = 0.9992

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 5 10 15 20 25 30 35

Polyacrylamide concentration (ppm)

Abs

orba

nce

Figure 3-17: Analytical calibration curve of polyacrylamide by N-bromination method with standards diluted from ASP slug of SP 23 (1550 ppm).


106

Polyacrylamide in DW Calibration Curve

y = -0.0024x2 + 0.1459x + 0.0023

R2 = 0.9999

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30 35

Polyacrylamide Concentration (ppm)

Abs

orba

nce

Figure 3-18: Analytical calibration curve of polyacrylamide by N-bromination method with standards diluted from 1550 ppm polyacrylamide in DW.

3.8.2 Interferences on Polymer Determination by N-Bromination Method

Inductive coupled plasma- atomic emission spectroscopy (ICP-AES) is useful for

elemental analysis (Taylor, 2001). Some of the samples from ASP floods were analysed

by ICP-AES and elements such as Na, Ca, Mg, Fe, Al and Cu were detected (Table 9-5

in Appendix A3). These elements would be present as ions and have the potential to

interfere with the N-bromination method. Taylor (1993) reported that NaOH and several

divalent ions like Mg2+ and Ca2+ have no influence on n-bromination method. The

possible interference of trivalent ions could be saturated by adding aluminum sulphate

(Scoggins and Miller, 1979). The sodium acetate/acetic acid buffer is designed to

provide a pH of 3.5 to eliminated chloride ions interference (Scoggins and Miller, 1979).

The chloride ion is not expected to be present in the ASP floods; nevertheless, this

buffer was used. The buffer also contains the aluminum sulphate to eliminate

interference from trivalent ions.

3.9 Measurement of the Alkali Concentration

The alkali concentration is easy to find by using simple strong acid-strong base titration

or simple determination of pH value. The pH could be measured by simple pH meter


107

which is simpler than the titration. The pH could be related to the hydroxide

concentration because the hydroxide is a strong base and will dominate the pH reading.

The pH of 1% w/v surfactant in DW is only 8.5 and that of 1550 ppm of polymer in

DW is 7 compared to pH of 12.6 for 0.5% (w/v) of NaOH in DW. Recall that the raw

samples are to be diluted at least 60-80 times for polymer and alkali determination.

Figure 3-19 shows that despite of the high dilution factors of ASP slug that contains

NaOH concentration of 0.5% (w/v), the pH reading remains high up to dilutions of 100

(NaOH concentration 0.005%). This graph demonstrates the domination of the NaOH

over the pH reading. The domination of the NaOH on pH reading allowed the pH

reading to be used as a measure of the NaOH concentration. This is an advantage of

using the NaOH as the alkali in the ASP slug; it allowed easy determination of the

NaOH concentration in the ASP floods.

pH Value of ASP for Several Dilution Factors

y = 12.79x-0.028

R2 = 0.9815

11.011.211.411.611.812.012.212.412.612.813.0

0 20 40 60 80 100 120

Number of ASP dilutions

pH

Figure 3-19: Dilution of ASP slug and the pH reading.

Further investigation on the possible effects of polymer and surfactant on the pH

reading is discussed below. Another possible, concern is the presence of the emulsion in

the aqueous solution. It could possibly affect the pH reading, and this concern should be

investigated in the after next section.


108

3.9.1 Surfactant and Polymer Presence Interference on pH

One may suspect that the pH reading in ASP solution -which contain sodium hydroxide

as the alkali - could be affected by the presence of polymer and surfactant. In order to

examine this point, ASP solutions with corresponding alkali concentration were also

prepared by diluting the main ASP slug and some NaOH solutions in water (no

surfactant or polymer) with known concentration were prepared and their pH reading

was measured (Figure 3-20).This figure has logarithmic fitting function with good

correlation factor. There are slight differences between the curves.

pH Reading in ASP and DW

y = 0.4651Ln(x) + 13.502

R2 = 0.9926

y = 0.6829Ln(x) + 14.322

R2 = 0.9643

y = 0.4002Ln(x) + 12.954

R2 = 0.956

10.00

10.50

11.00

11.50

12.00

12.50

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

NaOH concentration (% w/v)

pH

Rea

din

g

NaOH in DW NaOH in ASP NaOH in ASP contaminated with emulsion

Figure 3-20: The pH reading as function of Sodium hydroxide concentration in water.

The presence of the synthetic surfactant and polymer were found to cause some changes

to the pH reading, Figure 3-20. Note that the samples of ASP solution needed to be

gently mixed to re-distribute equally the chemicals in the sample even though no phase

separation was observed. The measurement of each reading was repeated at least 3

times in all samples.


109

3.9.2 Crude Oil and Emulsion Presence

The crude oil may have some acidic components which may diffuse to the aqueous

phase. The ASP slug is run in the EOR mode, therefore, any fast diffusing components

will have been already flushed out off the core/ sand back during the water flooding

stage. When the ASP slug is injected the alkali is believed to react with these

components and neutralizing them into in-situ surfactants. The concentration of these

acids is low because the TAN of Oil 3 which is used in the floods is only 0.07 mg

KOH/g oil. Therefore, the pH reading is highly indicative on the hydroxide

concentration. One sample with known NaOH concentration (0.1% wt, 12 mL) and one

more with similar concentration (0.1% wt, 12 mL, 0.5 mL crude oil) but contaminated

with crude oil were papered. The pH was measured five times for each of the samples,

Table 3-1. The result were not changed to any significant degree, therefore, the oil

contaminates are not affecting the pH reading especially in the EOR mode.

Table 3-1: pH reading from pure and oil contaminated samples

Contaminated with oil No Contamination NaOH concentration

(%wt) 0.1 0.1 Reading 1 12.00 12.05 Reading 2 12.03 12.04 Reading 3 12.03 12.04 Reading 4 12.05 12.01 Reading 5 12.06 11.99

mean pH 12.03 12.03

3.9.3 The pH Meter, Buffers, Electrode and Calibration Procedure

In general it is a recommended practice to calibrate the pH measurement system (the

electrode and the meter) at least on daily basis. In this work, the pH measurement

system was calibrated every 3-4 hours. A common procedure to calibrate the pH meter

is to adjust it against the pH of known standard solution. These standards should cover

the anticipated pH range. Three solutions were used at pH values of 4.0, 6.8 and 10.0.

Meter Brand: Orion, of the model: Expandable ion Analyzer EA 940. The pH probe was

a glass electrode of Metrohm brand and model (Metrohm 6.0262.100) which can

operate in the pH range from 0 to 13 and in temperature range 0 to 80 oC. This electrode


110

uses potassium chloride (KCl) as the reference electrolyte at concentration of 3M.

Buffers for pH meter calibration all from Chem-Supply: pH 4.0 red coded pH 6.8

colourless and pH 10.0 blue coded.

3.10 Fourier Transform Infra Red- Attenuation Total Reflection In the course of this PhD, FTIR-ATR was tested for the simultaneous determination of

the polymer and surfactant. The FTIR-ATR method can detect different functional

groups simultaneously (Scheuing, 1991). It is mainly used to study the structural

properties and vibration modes of molecules and compounds. It is also used to identify

functional groups for qualitative studies. Taylor and Nasr-El-Den (1994), in their review

of methods to determine polyacrylamide, reported the use of FTIR-ATR for the

determination of the degree of hydrolysis in the polyacrylamide. It is possible to use

FTIR-ATR to distinguish simultaneously between the amide groups of the

polyacrylamide and the sulphate groups of the sulphate/sulphonate surfactants in one

infrared (IR) scan.

The amide sub-groups of the polyacrylamide have a known IR spectrum with clear

peaks at several wave numbers. Two examples of these peaks are at 3198 and 1660 cm-1

(Murugan et al., 1998). The sulphate and sulphonate groups of surfactants also have

clear absorption peaks particularly at wave numbers 1065 and 1250-1200 cm-1 (Weers

and Scheuing, 1991). These peaks were indeed found in this investigation and were

used to make an analytical calibration curve, assuming the intensity of the peak

depended on the concentration as given by Beer’s Law, Figure 3-21. Although, the

methylene groups of the hydrocarbon chains have sharp peaks at 2952 and 2580 cm-1

they are shared by both the surfactant and polymer, thus, can not be used.

A zinc selenide crystal was used to find the IR spectrum of standard solutions

containing the polymer and the surfactant. Several samples were made of surfactants

and polymer as well as mixtures of both. The results are shown in Figure 3-21, Figure

3-22 and Figure 3-23.


111

FTIR-ATR Spectrum of sulphonate Surfactant (Bioterge-As 40)

-1

0

1

2

3

4

5

1000105011001150120012501300

Wavenumnber (cm-1)

Tra

nsm

ittan

ce (

%)

(Bac

kgro

und

Sub

tract

ed)

FT9_0.01%

FT2_1%

FT14_0.5%+750ppm HPAMFT3_0.5%

FT6_0.1%

FT8_0.02%

FT7_0.04%

FT4_0.2%

S-O of SO4 (Symetric)

S-O of SO4 (assymetric)

Figure 3-21: The surfactant was easily detected with FTIR-ATR, note the characteristic peaks of sulphonate at 1050 cm-1.

Polyacrylamide FTIR-ATR Spectra at Different Concentrations After Background Spectra Subtraction

-2024681012141618

8001300180023002800330038004300Wavenumber (cm-1)

Tra

nsi

mit

an

ce a

fte

r su

btr

act

ion

of

ba

ckg

rou

nd

(%

)

10000 ppm 5000 ppm 800 ppm

NH2C-N

Methlyne

NH2

Figure 3-22: FTIR-ATR spectrum of polyacrylamide in water, after subtracting the background spectrum. The N-H band was detected ~1640 cm-1 but at very high concentrations.


112

The method detected the surfactant (sulphonate) down to 0.04% w/v concentration and

it was possible to make analytical calibration curve with a reasonable correlation factor,

this curve is shown in Figure 3-23.

FTIR-ATR Analytical Calibration Curve for Surfactan t

y = 1.930x + 0.386

R2 = 0.948

0

0.5

1

1.5

2

2.5

3

0 0.2 0.4 0.6 0.8 1 1.2

Bio-Terge AS 40 concentration (% w/v)

Abs

orba

nce

Figure 3-23: Analytical calibration curve obtained from sulphonate surfactant concentration and absorbance of the sulphonate groups in the FTIR-ATN spectrum.

The method did not detect the polyacrylamide at the low concentration used in ASP

process. The polyacrylamide is produced in different molecular weights. Higher

molecular weights require lower concentrations to achieve the desired viscosities.

Generally, ASP floods may use concentrations roughly in the range of 400-2500 ppm.

The FTIR-ATR did not detect the polymer with a molecular mass of 20 million Dalton

at 1560 ppm. It did detect the polymer at 5000 ppm or above, Figure 3-22, but the

peaks in the spectrum corresponding to the acrylamide groups were hardly detected.

Figure 3-22 shows shaded areas of some of the expected frequencies of the amide

groups. The methylene groups are clear and in these particular samples there was no

surfactant, therefore, these belong to the polymer backbone chain. The acrylamide

groups almost show no signal in the expected vibration frequencies of the amide groups,

except of very small hump close to 1660 nm which could belong to the amide group.

This could be due to the change of degree of hydrolysis of the polymer chain where


113

acrylamide groups are hydrolysed. The adsorption of surfactant or polymer on the

surface of the zinc selenide crystal may influence the measurement as well as the

possibility of overlap between the polymer and surfactant peaks.

In addition, there was the concern of possible corrosion of the zinc selenide crystal by

the sodium hydroxide part of the ASP slug. NaOH is a strong base that could corrode

the surface of the zinc selenide crystal. If the method was able to detect the polymer and

surfactant simultaneously, then it will be easy to resolve the potential corrosion

beforehand by neutralizing the strong base by adding controlled amounts of acid or

mitigate its effects by a buffer solution. Moreover, the zinc selenide crystal is brittle

which increases the precautions required during the measurement to avoid cracking the

crystal or initiating scratches to its surface.

In summary, the FTIR-ATR did not meet the requirement of this investigation and did

not detect the polymer in the anticipated concentration from ASP floods. The method

seems more applicable to higher concentration. Other methods were subsequently tested

and used as already reported above.

3.11 Conclusion The analytical analysis of the ASP effluent is proven very challenging because of the

co-existence of the three substances which could interfere with the determination of

each other. The best available methods for the quantitative determination of the

polyacrylamide like SEC and N-bromination are sensitive to the amide groups which

can change by hydrolysis. Therefore, even with no interference present, the polymer

concentration could be under estimated. The BG showed high sensitivity to the

surfactant concentration. However, the emulsion/microemulsion had high impact on the

absorbance. A mathematical model developed and was used to correct for emulsion

interference. The model was only partially successful. It was effective to correct the

emulsion interference with control samples that contain no polymer, but, failed with

those contain polymer. The alkali determination by pH measurement was the simplest.

The presence of oil, polymer and surfactant did have some minor influence on the pH

reading. All the three methods which were used to determine the alkali, surfactant and

polymer have limitations. The interferences in all the methods used, even after applying

improvements, will undermine the accuracy of the measured concentrations of the ASP


114

chemicals, but are still sufficiently good to reveal the chemical profiles. The

concentrations of the components in the aqueous phase are representative of the relative

and general concentration trend of each component in the effluent. Therefore, they

should reflect the effect of rock heterogeneity on the ASP EOR recovery.

In the timeframe, it was not possible to work more to add further improvements to the

existing techniques, so, the modified BG method for surfactant determination, alkali

determination by pH and the N-bromination of polyacrylamide were used in the actual

floods in Chapter 5 but their limitations should be noted.

115

4 The Physicochemical Properties of ASP Slug and Oil

This chapter presents physicochemical properties of the ASP and the oil used in this

project. It reports the chemicals and oils used in the project and the experimental work

associated with interfacial tension measurement (IFT), and determination of emulsion

phase behaviour. The chapter is divided into three sections: oil and chemicals, IFT

determination and Winsor phase behaviour determination. The IFT was measured using

a simple in-house-made sessile drop cell. The cell is based on a captive drop design

capable of estimating IFT down to 0.002 mN/m.

4.1 ASP Slug Properties ASP slugs should possess certain physical characteristics to achieve successful EOR.

All ASP chemicals should actively engage in the oil recovery process to qualify as an

ASP process. The ASP slug should produce ultra low IFT with the target oil to increase

the capillary number. The slug also needs to resist phase separation. The viscosity

should increase to improve the mobility ratio. However, it should not be too high to

avoid blocking the flow in the porous medium. The emulsion produced by the ASP slug

should not have high viscosity and should possess fluidity. A simple fluidity test could

be done by tilting the tubes containing the emulsion. If the emulsion easily moves in the

tubes then, its viscosity is not going to be detrimentally high.

The preferred phase behaviour is III phase for maximum oil recovery. For this

investigation, it is important that the phase behaviour is kept at one phase behaviour

during the floods. The target oil to be recovered should contain naphthenic acids to

enable the generation of in-situ soaps by the action of the alkali in the ASP slug.

Chemical stability of the slug is also important.

Chapter 4: Physicochemical Properties of ASP Slug and Oil

116

4.2 Oils and Chemicals

4.2.1 Chemicals Selection and ASP Slug Design In this project, the selected chemicals were not targeted to specific reservoir conditions.

This relaxed the screening procedure and facilitated the chemical selection process. In

general, very low concentration may not be effective to recover oil and high

concentrations could be limited by economics or technicalities. For example, higher

surfactant concentration will help recover more oil, but this will add to the cost of the

project. Increasing the polymer concentrations lead to produce high viscosities which

can undermine injectivity and adding small amounts will not increase viscosity enough

for mobility control. The increase of alkali concentration may trigger high

dissolution/precipitation reactions with the rock minerals. This could cause scaling in

the production wells and flow problems (Wang et al., 2004). Therefore, an optimal

composition of the ASP chemical is usually sought. Phase behaviour scans, IFT

measurements and adsorption studies are needed to design an effective ASP slug for

specific oil and reservoir conditions (Flaaten et al., 2009; Liu et al., 2010; Green and

Willhite, 1998). The screening process to find chemicals which give optimum properties

to meet the conditions of a specific reservoir requires finding the ASP slug formulation

that gives ultra low IFT and minimises chemical loss at reservoir salinity, pH and

temperature. This process could be time consuming and difficult, thus, when an

effective ASP slug composition is found, the design usually moves from design to

flooding experiments.

In this study, the chemicals were selected based on literature review of relevant

chemicals that have been used in prior work. The selected alkali was NaOH, the

surfactant was branched propoxy sulphate and the polymer was partially hydrolysed

polyacrylamide (HPAM). More details about these chemicals are given in the next

section. The HPAM was selected as the polymer component of the ASP slug because it

is the most commonly used polymer in EOR processes (Lake, 1989; Sorbie, 1991;

Sheng, 2010). The propoxy sulphate surfactants were chosen because they are known to

have the ability to reduce IFT to ultra low values (Hirasaki et al., 2006). Sodium

hydroxide was selected because it reacts quickly with the acids in the oil to produce in-

situ surfactants (Sun et al., 2008). This ensures the alkali is engaged in the chemical

flooding process. It is also easier to deal with NaOH to determine its concentration by


117

simple pH measurement. In addition, the oil has to contain natural crude oil acids to

enable the NaOH reacting with these acids and produce in-situ surfactants (soaps). A

mixture (called Oil 3) of aliphatic mineral oil and Stag Crude oil was made to ensure

that crude oil acids exist in the oil. The preparation of this oil is discussed in more

details in this chapter.

Since the ASP slug design was not constrained with specific reservoir conditions,

arbitrary ASP slug compositions were made and their ability to reduce the IFT against

Oil 3 was tested. A composition of 1% surfactant (Alfoterra 145-S4), 0.5% sodium

hydroxide and 1550 ppm polyacrylamide (Flopaam 3630 S) was found very effective in

reducing the IFT between the ASP slug and Oil 3 without the addition of sodium

chloride. The polymer concentration of 1550 ppm increased the viscosity to about 5.5

cP. These concentrations of this ASP slug are reasonable and fall within the

concentration ranges reported by several works on the ASP floods (Liu et al. 2008;

Mohammadi et al., 2009; Arihara et al., 1999; Change et al., 2006).

4.2.2 Materials Oil 3 is a mixture of two oils: 15.5% (w/w) Stag Crude Oil and 84.5% (w/w) Ondina 15.

This oil mixture has a TAN (Total Acid Number) value of 0.07.

Stag Crude Oil: Stag Crude is produced from the Stag Field (North West Shelf,

Western Australia) operated by a venture of Apache Northwest Pty Ltd and Santos

offshore Pty Ltd (Department of Industry and Resources, State of Western Australia,

2008). The producer made assay data of the Stage Crude available online (Santos, 2011).

Mineral Oil : Ondina 15 (Shell) is highly purified paraffinic oil marketed by Shell

Company of Australia Ltd.

Dodecane Oil: this oil is 99% pure and marketed by VWR PROLABO.

Polymers: Are partially hydrolysed (25-30%) polyacrylamide (HPAM) supplied by

SNF under the commercial names FLOPAAM 3630S and FLOPAAM 3430S with

approximate molecular weights of 20 and 12 million Dalton, respectively. A plot of the

Flopaam 3630S viscosity as function of its concentration in DW can be found in Figure

4-1.

Surfactant: Is a monoalkyl propoxy sulfate surfactant supplied by Sasol North America

under the current commercial name AFOTERRA 145-S4.

Alkali : Sodium hydroxide (NaOH) of reagent grade with minimum purity of 97%.


118

Sodium Chloride (NaCl): Fulka Analytical grad, assay 99%.

Partially Hydrolysied Polyacrylamide (Flopaam 3630 S) Viscosity as a Function of its Concentration in Deionised Water

Viscosityy = 6.35E+01e-3.75E-02x

R2 = 9.95E-01

0

2

4

6

8

10

12

14

16

0 500 1000 1500 2000 2500 3000 3500

Polymer Concentration (ppm)

Vis

cosi

ty (

cP)

Figure 4-1: Polyacrylamide (Flopaam 3630 S) viscosity as a function of its

concentration in deionised water with exponential fitting and correlation factor (R2).

The viscosities of the fluids used in this experiment were measured using an oscillating

piston viscometer, Cambridge viscometer model VISCOlab 4000 supplied with a

temperature control system. The densities of some of the liquids were measured using a

Mettler Toledo DE40 Density Meter. A rotational viscometer which could be used to

measure viscosity as a function of shear rate was not available in this project.

An optical microscope was used to take digital images of some of the emulsion. The

microscope model was Olympus® Provis AX70 equipped with a digital camera model

Olympus DP71. The objective lenses were Olympus UMPlanFI.

4.2.3 Mixing the Stag Crude and Ondina Oil 15

It was desired to use Stag Crude Oil in the ASP sand packs floods (6 floods) because it

was available and has TAN of 0.45 (mg KOH/ g oil), however, there was no enough


119

crude for all the runs. Furthermore, a mixture of Stag Crude and Ondina 15 oil was

promoted for the following operational reasons:

1. At the start of the sand packs flooding experiment, there was not enough crude

oil for all planned flooding runs, and securing more crude oil was not granted

within the timeframe of the PhD, mixing of the crude oil with Ondina 15 was

used to produce enough oil quantities with required properties (TAN, viscosity)

for the application of the ASP process.

2. Viscosity of the Stag Crude is about 95 cP at room temperature. This means that

the injection rate needs to be very low to avoid pressure build-up above the

breaking pressure of the glass tubes (~385 psi) which were used to pack the sand,

meaning it will take significantly increased time per flooding run. Mixing with

percentage 15.5% (w) Stag and 84.5% (w) Ondina 15 would drop the viscosity

to about 30 cP at room temperature. This reduces the required injection pressure

and shortens the time per flooding run, Figure 4-2 shows the viscosity and

density of Oil 3 as a function of temperature.

Viscosity and Density of Oil 3 (Mix:15.5% wt Stag+8 4.5% Ondina Oil 15)

Viscosityy = 6.35E+01e-3.75E-02x

R2 = 9.95E-01

Densityy = -6.44E-04x + 8.71E-01

R2 = 1.00E+00

0

5

10

15

20

25

30

35

20 25 30 35 40 45 50 55 60

Temperature (oC)

Vis

cosi

ty (

cP)

0.830

0.835

0.840

0.845

0.850

0.855

0.860D

ensi

ty (

g/cm

3 )Viscosity Density

Figure 4-2: Viscosity and density of the mixed oil (Oil 3) used in all of the sand pack floods.


120

3. The alkali in the ASP slug is mainly added to react with the acids in the oil to

generate in-situ surfactants. Ondina Oil 15 does not have acids while Stag Crude

assay data reported TAN of 0.45 mg KOH/ g oil (Santos, 2011). Acids in the oil

are needed to react with the alkali to enhance interfacial tension reduction.

Mixing with percentage 15.5% (w/w) Stag Crude and 84.5% (w/w) Ondina 15

gives TAN of 0.07 (mg KOH/ g) oil based on simple dilution calculations.

4. The home-made ultra low IFT cell -Section 4.4- only works with transparent to

semi-transparent oils. There was no readily accessible ultra low cell like

spinning drop. Because the Stag Crude is not transparent, it is not possible to

evaluate the IFT between this crude and the ASP slug or its components. While

for the mixed oil (Oil 3), it is slightly transparent and with good illumination it is

possible to estimate IFT with this cell.

4.2.4 Preparation of the ASP Slug

In preparation for each ASP flooding, 500 mL of ASP slug was made just about 20-28

hours before the injection. The target composition of the ASP slug was: 1550 ppm of

polymer (equivalent to 0.155 % (w/v)), 1% (w/v) surfactant (active based) and 0.5%

(w/v) of the alkali. The steps followed to prepare the slugs were:

1- A mass of 2.500±0.005 g of NaOH is first dissolved in 30 mL of DW in 50 mL

beaker.

2- 18.450 g of the surfactant slurry is diluted in 50 mL of DW in 80 mL beaker.

3- The surfactant and the alkali are then added to 70 mL of DW and mixed in 250

mL beaker using a magnetic stirrer.

4- The stirring rate is adjusted to make a 3-5 cm deep vortex.

5- An amount of 0.778 g of the polymer is then slowly added on the shoulder of the

vortex over the duration of one minute.

6- The stirring continues for about 75 minutes.

7- The slug is then transferred to 500 mL volumetric flask and the 250 mL beaker

is rinsed to remove all chemicals into the flask, before being diluted to 500 mL.

8- The stirring continue for additional 20 minutes in the flask.


121

9- The slug is transparent and could be visually inspected for any undissolved

polymer or gels. If any were observed the process would be repeated from 1 to 9.

10- The slug is then transferred to a 500 mL Scott glass bottle and stored with the

led closed until ASP injection commences. Furthermore, the ASP injection line

has an inline 7 µm filter, thus, any gels which were not noticed by the eye

inspection would be broken in the filter and should not plug the sand pack.

This ASP slug was transparent with viscosity of about 5.5 (cP), pH of 12.0 and density

of 1.003 (g/mL) at room temperature. A sample of this slug showed no phase separation

for over five month storage time. This long storage time is long compared to the age of

the ASP slugs prepared for ASP flood of one day. New slug was made for each ASP

flood and its viscosity was measured at the start of the flood, as will be described in

Chapter 5.

4.3 Winsor Phase Behaviour of Oil 3/Surfactant Syst em

Winsor phase behaviour is important for chemical EOR (Green and Willhite, 1998). As

discussed in Chapter 2, type III Winsor phase behaviour is generally preferred to the

phases +II and –II for achieving higher EOR recoveries (Nelson, and Pope, 1978).

Characterisation of the phase behaviour type and size of emulsions produced from the

ASP floods are important for the interpretation of the ASP flood results. Phase

behaviour scans were conducted to understand Oil 3/Alfoterra 145-S4 system phase

behaviour and find its optimum salinity.

4.3.1 Salinity Scan for Winsor Phase Behaviour

In order to learn the possible phase behaviour of Oil 3/ASP system, phase behaviour

scans of Oil 3/ Alfoterra 145-S4 were conducted. There was no specific surfactant

concentration to start with except that the literature tends to report the use of surfactant

concentration in the range of 0.1-1%. Consequently, a surfactant concentration of 0.2%

was picked as a starting concentration. Salinity scans were made for salinity from 0 to

15 % NaCl (w/v). The surfactant concentration was kept at 0.2 % in all the samples and

the NaCl concentration was changed in increments of 1% NaCl.


122

In each salinity scan the following steps were taken:

1. Pour 6 mL of the surfactant solutions with preset salinity into 15 mL plastic tube.

2. Add 6 mL of Oil 3.

3. Firmly close the tubes cape.

4. Shake rigorously using hand for 5 minutes.

5. Leave tubes vertical undisturbed.

Results showed that as the salinity was increased, there was no gradual change in phase

behaviour observed between 0 and 5% NaCl. Slight change in texture was seen and the

system showed lower phase behaviour (phase –II). There is a sudden change between

8% and 9% NaCl. At salinity of 9% NaCl, the system showed upper Winsor phase

behaviour, Figure 4-3, with a greyish emulsion. The image was taken after 4 months of

emulsion formation. Another sample of this system was made -not included in the

image in Figure 4-3 at salinity of 15% and it also showed upper Winsor phase

behaviour.

Figure 4-3: Salinity scan of Oil 3 with 0.2% (w/v) Alfoterra 145-S4 and variable salinity, the salinity is shown in the textboxes as % NaCl (w/v). The emulsion in the tubes is 4 months old.

0% 4% 5% 7% 8% 9%


123

Figure 4-4: Closer image of the two emulsions, brown and white, formed in the 7% NaCl, 0.2% (w/v) Alfoterra 145-S4.

Note that at 7% NaCl, two types of emulsions were observed, Figure 4-3. One part of

the emulsion was white and the upper part was brown. Figure 4-4 shows an

enlargement of the region of these two coloured emulsions. The emulsions which

occurred at 7% NaCl persisted for few months, showing that they have some degree of

thermodynamical stability. At salinity of 8%, there was only the white emulsion

observed and below 5% there was brownish emulsion with a hint of white emulsion as

can be seen in Figure 4-3. The production of two coloured emulsions in one test tube is

not typical; no prior work describes such observations. Therefore, it was not certain that

this white coloured material was an emulsion. Consequently, a small sample of the

white emulsion was extracted using a syringe, and poured into microscopic slide.

Subsequently several images were taken under an optical microscope (Olympus) to

check the nature of this emulsion. Figure 4-5-a confirmed that this white material was

indeed an emulsion. Figure 4-5-b shows oil fluorescence image of the image in Figure

4-5-a, through which it was found that this emulsion was water continuous. This makes

this emulsion of the lower Winsor phase behaviour (oil-in-water) with oil (blue)

dispersed in water (black). In the actual ASP floods (Chapter 5), only brownish

emulsions were produced. Finding explanations for the observed two coloured emulsion

was not attempted due to time restrains, and thus, the investigation of such emulsion is

suggested for future work.

Excess Oil Brownish Emulsion White Emulsion Water 0.2% surfactant,

7% (w /v) NaCl

Unidentified suspensions


124

Figure 4-5 : a) Microscopic photograph of the white emulsion seen at 7% NaCl (w/v) and 0.2% (w/v) surfactant. b) Oil fluorescence (blue) shows that oil is surrounded by water (black) constituting oil-in-water emulsion.

Some suspensions were observed and remained unidentified (Figure 4-4). The source

of these suspensions could be the Stag Crude which constitutes about 15% w/w of Oil 3.

No further experimental work was done to investigate this suspensions. In a typical

Winsor phase behaviour the system should go from lower phase –II to +II phase

through the middle phase III as the salinity is increased.

These observations suggest that, below 7% NaCl the system has lower Winsor phase

behaviour, and above 8% the system has upper Winsor phase behaviour. Between 7 to

8% there was a unclear behaviour because of the co-existence of the two emulsions.

This phase behaviour did not fit to Winsor phase behaviour described in Chapter 2.

Perhaps, the optimum salinity of this particular system lies within a very narrow salinity

window. In summary, this Oil 3/Alfotera®-145-S4 system did not follow the typical

Winsor phase behaviour and these experiments were not continued due to time

constraints. Therefore, the design of the ASP slug was not based on Winsor phase

behaviour, but was based on its ability to reduce the IFT as described in Section 4.4.

4.3.2 Electrical Resistivity Test for Emulsion Type The actual ASP slug that was used in the ASP sand pack floods consisted of 1% (w/v)

Alfoterra 145-S4, 0.5% (w/v) NaOH and 1550 ppm (equivalent to 0.155 % (w/v))

partially hydrolysed polyacrylamide. The phase behaviour of this ASP slug with the oil

100 µm 100 µm

(a) (b)


125

used in the floods was checked by electrical resistivity. The oil electrical resistivity is

known to be high compared to the resistivity of aqueous solutions containing ions. The

oil-in-water emulsion of ASP/oil system will have low resistivity because the

continuous phase is the water and it has sodium hydroxide and ionic surfactant from the

ASP slug which can conduct electrical charge. The water-in-oil emulsion is oil

continuous and the oil acts as an insulator leading to high resistivity measurements. A

simple resistivity test was applied to check the type of emulsion produced in phase

behaviour tubes as well as samples obtained from actual ASP floods, which will be

described in Chapter 5. The test instruments and procedure are described below.

4.3.3 Emulsion Resistance Measurement Procedure.

A digital multimeter (JayTech: QM1340) was used to measure the resistivity of the

emulsion, the water (ASP slug) and the oil. Two thin (~ 1 mm diameter) metallic probes

were attached to the positive and negative alligator clips of the multimeter’s leads. The

multimeter was set to measure resistivity in the range of 20 MΩ. The samples were

collected in glass vials as described in Section 3.6. It is possible to check visually the

location of the probes to ensure no short circuit exists. The probes were inserted into the

target phase. The probes were held in position for 10 to 20 seconds, to take a reading of

the target phase, see Figure 4-6 for illustration.

Figure 4-6: Simple setup to measure resistivities of oil, ASP slug and emulsion.

In this work, the oil resistivity was measured and found beyond the range of the

multimeter of 20 MΩ. It can thus be considered as essentially infinite, with respect to

Oil phase

Microemulsion

Water Phase

Multimeter Metallic probes

Emulsion resistivity measurement

Water resistivity measurement

Oil resistivity measurement


126

the voltmeter measurable resistivity range. The ASP slug resistivity was found to

fluctuate in the range 0.001- 0.03 MΩ. Therefore, oil-in-water (water continuous)

emulsion will have a resistivity comparable to that of the ASP slug or larger but not

infinite. On the other hand, the water-in-oil (oil continuous) will have infinite resistivity.

The oil layer, emulsion layer and water (ASP) layer behaves as parallel resistors with

respect to the immersed multimeter’s metallic probes. When resistors are in parallel

configuration, the overall resistivity is dominated by the layer that has the lowest

resistivity. When the resistance of the emulsion is measured, both emulsion and oil are

in parallel. The oil acts as the insulator and the resulting resistance is dominated by the

emulsion. When the probes are immersed in the ASP (water phase), the resistance is

dominated by the water phase.

Phase behaviour of ASP slug in test tube showed lower phase behaviour. The samples

from the ASP floods (Chapter 5) were tested and found of the oil-in-water type, that is

Winsor lower phase –II. NMR could also be used to find the emulsion type as well as

the droplets size distribution which are discussed in Chapter 6.

4.4 Interfacial Tension Measurement

IFT estimation or measurement is essential to design and predict the efficiency of

chemical EOR process. Capillary number can be increased by orders of magnitude by

reducing IFT, as discussed in Chapter 2. There appears to be no standard classification

of IFT into regions of high, low, or ultra low. However, the ‘ultra low’ term typically

would refer to IFT below 0.01 mN/m which corresponds to the region of interest of this

study.

4.4.1 Interfacial Tension Measurement Methods

There are several methods reported in the literature to measure the IFT like: spinning

drop, pendant drop, captive drop, capillary height, drop weight, maximum bubble

pressure, the Wilhelmy plate and the Du Noüy ring (Padday, 1969; Schramm and

Marangoni, 2000; de Gennes, Brochard-Wyard, and Quere, 2004). Even lasers can be

used to measure IFT; Mitani and Sakai (2002) described an elegant methodology to use


127

a laser to measure ultra low IFT through laser stimulated deformation of the

liquid/liquid interface. No apparatus for any of these methods was available to this PhD

except of the pendant drop, however, the measurement of ultra low IFT using the

pendant drop technique was found to be not viable. A simplified captive/sessile drop

cell was developed to estimate the ultra low IFT. The ability of this cell to estimate IFT

was checked against the IFT results from the spinning drop technique reported in other

works. The limitation of the pendant drop technique to measure ultra low IFT, the

development of in-house captive/sessile drop cell and the validation of the cell using

spinning drop results are below.

4.4.2 Pendant Drop

There was a pendant drop apparatus at CSIRO, accessible for this project which was

considered for use. Figure 4-7 shows a sketch of pendant drop and the dimensions

required to calculate the IFT.

Figure 4-7: Pendant drop profile and input diameters for IFT calculations (adapted from Song and Springer, 1996A).

Padday (1969) detailed the derivation of the pendant drop equation, which can be re-

arranged in the form expressed by Tadros in Equation 4-1 (Tadros, 2005, pp. 81).

H

dg eE2ρσ ∆

=

where σ is the IFT (mN/m), ∆ρ (g/mL) is the density difference between the two fluids,

gE (cm/s2) is the local Earth gravitational acceleration, de is the maximum horizontal

de de

ds

4-1


128

diameter, ds is the horizontal diameter at distance ds from the drop apex. H is a

dimensionless shape factor could be obtained from ds/de tables, Padday (1969) provided

the original tables of Niederhauser, Bartell and Fordham for finding H as a function of

the ratio ds/de.

There is no literature value of the minimum measurable IFT value using the pendant

drop technique. However, as the PhD research was progressing it was realised that most

of the papers reported high IFT using the pendant drop method with almost no paper

reporting ultra low IFT. Moreover, the elementary physics of capillary length (defined

in Equation 2-10) implies that the drops will be small when the IFT is ultra low which

means it could be experimentally difficult to form a stable pendant drop.

Despite the fact that Guo and Schechter (1997) as well as Lin and Hwang (1994)

reported their success to measure ultra low IFT down to 0.01 and 0.0025 mN/m

respectively using the pendant drop technique, the method becomes operationally

difficult to use below 0.1 mN/m. Haq (2010) confirmed, based on laboratory

experiments of the pendant drop method, it is difficult to form stable drops of

surfactant/oil systems when the anticipated IFT is ultra low. Schramm and Marangoni

noted this experimental difficulty with the pendant drop technique when the IFT is close

to the range 10-1 mN/m (Schramm and Marangoni, 2000, pp. 18). Consequently,

another method was sought. The next most convenient technique is described in Chapter

2; the estimation of IFT using Winsor phase behaviour as suggested by Healy et al.

(1976) and Huh (1979).

4.4.3 Estimation of IFT Using Winsor Phase Behaviour

Estimations of IFT’s are sometimes satisfactory for EOR and negate the need for high

accuracy measurements in the initial screening for suitable surfactants. For example,

Gary Pope used the Winsor phase behaviour to estimate IFT in the design of ASP slug

(Flaaten, Nguyen, Pope and Zhang, 2009). This approach is valid because low IFT is

proven to correlate with middle phase behaviour (phase III) both experimentally and

theoretically (Healy et al., 1976; Huh, 1979). If a system of oil/surfactant follows

typical Winsor phase behaviour and reaches phase III as salinity is increased, then

Huh’s equation (Equation 2-33) could be used to estimate IFT.


129

Attempts to use the Winsor phase behaviour to estimate the IFT between the oil that

was used in this PhD work but as the Oil 3/Alfotera®-145-S4 did not follow the typical

Winsor phase behaviour, this approach was unsuccessful. The phase transition in typical

Winsor phase behaviour should ideally go from phase –II through phase III to phase +II

as salinity is increased. The non-typical phase behaviour exhibited by the surfactant

used in the ASP slug and Oil 3 is reported in Section 4.3. With the limitation of the

pendant drop technique and the observed non-typical Winsor phase behaviour, the IFT

measurement or even estimation became a challenge, and consideration was given to

purchasing a new spinning drop IFT apparatus.

4.4.4 Motivation to Build In-House IFT Cell

No instrument was readily available for this project to measure the anticipated ultra low

IFT values between the ASP slug and the oils. The purchase cost of a new ultra low IFT

instruments like (spinning drop) was far beyond the budget allocated to this PhD project.

A typical instrument cost in excess of $40,000. No nearby instrument was known to be

readily available. Because this research would require several evaluations of different

ASP compositions at different times, sending samples overseas or to other Australian

States was not a convenient option. An alternative in-house solution was investigated.

4.4.5 Captive Drop

A method known as the captive drop that can measure ultra low IFT has been reported.

Note that the captive drop is essentially a sessile drop (Padday, 1969, pp. 85; Malcolm

and Elliot, 1980). Sessile drop is an established technique to measure IFT (Padday,

1969). When the drop is floating against a solid structure in a denser fluid then, it is

called captive drop, and when the drop is sunk on a solid structure in a lighter fluid, is

called sessile drop. Figure 4-8 show a sketch of sessile drop with the important

dimension to calculate the IFT.

Figure 4-8: Illustrative Sketch of Sessile drop

h

d

Solid platform


130

Schramm et al. (1995) developed a captive drop cell to measure IFT for a wide range of

pressure and temperatures. They used an equation derived by Malcolm and Elliot (1980)

for the calculations of IFT using the sessile drop technique (or captive drop). The

method was developed for a special case of sessile/captive drop where the contact angle

is 180o (Malcolm and Elliot, 1980). The special case of sessile drop, as described by

Malcolm and Elliot (1980) corresponds to:

( )2

∆=

dhG

hgEρσ

where σ is the IFT (mN/m), ∆ρ (g/mL) is the density difference between the two fluids,

gE (cm/s2) is the local Earth gravitational acceleration constant, h (cm) is the height of

the drop from its base to the apex, and d (cm) is the median diameter of the drop, these

dimensions are illustrated in Figure 4-8. G (h/d) is a fourth order polynomial to

calculate the shape factor (Malcolm and Elliot, 1980):

( ) ( ) ( )( ) ( ) 43

2

660622.3669726.8

430927.9807066.286519.1

dhdh

dhdhdhG

−+

−+=

The behaviour of this polynomial is displayed in Figure 4-9 for h/d in the range 0 to 1.

The equations which were used by Schramm et al. (1995) and developed originally by

Malcolm and Elliot (1980) make the following two conditions assumptions:

1- The contact angle is 180o.

2- The drop is separated from the platform by a thin film of the surrounding fluid.

When the surrounding liquid is oil and the platform surface is lipophilic, the oil

molecules adsorb on the surface and form a stagnant thin layer. When a water drop is

placed gently on the surface, the stagnant layer acts as a barrier or cushion between the

water and platform and no direct contact between water droplet and platform should

occur. If the platform surface is smooth, the thin layer will be smooth. As a result, the

drop resting on this thin layer cushion will have its lower surface area parallel to the

platform surface, that is, a contact angle equal or close to 180 degrees. Furthermore, in

practice when the cell is slightly tilted with the water drop placed on the platform and

4-2

4-3


131

surrounded by water or ASP slug, the drop tends to move off the platform with no traces

left behind. This shows that there was a very thin oil film separating water/ASP drop

from the Teflon. This should validate the two stated assumptions.

Shape Facor of the IFT Equation of Special Case Ses sile Drop

G(h/d)= -3.660622(h/d)4 + 8.669726(h/d)3 - 9.430927(h/d)2

+ 2.807066(h/d) + 1.865190

0

0.5

1

1.5

2

2.5

0 0.2 0.4 0.6 0.8 1

(h/d) (fraction)

G(h

/d)

Figure 4-9: The curve of the polynomial function that describes the shape factor of the

sessile drop as a function of the ratio of its height to its diameter.

4.4.6 Failure of Original Cell Duplication

An attempt for academic purposes was made to duplicate the captive drop IFT cell

reported by Schramm et al. (1995). The method requires capturing clear images of the

drop and finding the diameter and height of the drop. The method also requires a special

sulphonated tetrafluoroethylene polymer coating. Nafion is a commercial sulphonated

tetrafluoroethylene polymer that is available as sheets or solutions. The Nafion solutions

have been used to make solution-cast coatings (Moore and Martin, 1986).

Our attempt was not successful because of the difficulty in producing an even and stable

Nafion coating (sulphonated tetrafluoroethylene) on the Teflon substrate. This coating is

required in the original Schramm’s method to make a thermally stable, chemically-inert

and water-wet surface. The water-wet surface is required to build a thin surface of water

between the drop and the platform, which is a pre-condition for the captive drop method.

It also allows for the water to be the surrounding fluid and the oil to be the drop. It is

thus possible to achieve good visibility and capture good images with well defined

drops.


132

Although, several Nafion coatings were made under vacuum following the procedure

described by Moore and Martin (1986), the coatings were not smooth and pealed off

from the Teflon platform. Consequently, a simplification was proposed using the Teflon

without the Nafion coating.

4.4.7 Simplification to Make the Method Work

A straight forward simplification to Schramm’s method is to use the Teflon without the

Nafion coating. Malcolm and Elliot (1980) suggested using Teflon with hydrocarbon

oils like benzene and n-hexene to produce the required thin film between the drop and

the platform. This simplification means that oil would need to become the surrounding

fluid and the water would be the drop. Visibility of the drop would then be limited to

transparent or semi transparent oils which is the penalty for this simplification. However,

the technique is straightforward and significantly simplifies the design of the IFT cell

for the sessile (captive) drop. Researchers working with transparent oils and requiring

IFT estimation in their work may benefit from the proposed measurement technique.

4.4.8 Modified Captive Drop Method

The proposed captive/sessile drop method does not use the Nafion coating used in the

work of Schramm et al. (1995) to provide a hydrophilic surface. Instead, it uses the

hydrophobic nature of the Teflon to make a lipophilic surface.

In this method the droplet is resting on the platform through gravity and not captured by

floating forces as in the original method. The surrounding fluid is oil rather than

water/aqueous phase in the original method. The governing Equations 4-2 and 4-3 are

the same as the equations of Malcolm and Elliot (1980).

4.4.9 Camera and Optics

Microphotography was used to capture the resting droplets. The setup of the sessile

drop apparatus is shown in Figure 4-10 and Figure 4-11.


133

Figure 4-10: Side view schematic of the sessile drop IFT cell with the drop resting on the Teflon platform. A micro-lens (TAMRON: sp 60 mm: F/2 MACRO) was mounded on three tube

extensions (KENKO: 12 mm, 20 mm and 36 mm tubes). The extensions were mounted

on the camera. The camera model was 60D Canon (digital sensor 18 mega pixels,

electro-optical system (EOS)) which can be remotely controlled by computer via a cable

and USB connections. The camera is equipped with a Liquid Crystal Display (LCD)

screen that shows live display of the target view. The focus of the lens could be

changed from 1:1 to infinity by rotating the lens.

Figure 4-11: Photograph of the sessile drop IFT cell apparatus.

Sidewise sliding knob

Forward/Backward Sliding Knob

Camera extension tubes

Tri-axial platform attached to tri-pod

Light Source

Teflon platform Resting Droplet

Transparent Glass Window

Lens

Macro sliding head on tri-pod

Lockable Rotating Head

2

3 4

1

6 7

5

10 1: Light 2: Droplet Chamber 3: Macro lens 4: Tube Extension 5: Camera 6: Macro sliding head 7:Tri-axial camera base fixed to tri-pod 8: Lockable rotating head 9: Tri- pod hold the sliding head 10: Computer for camera remote control

9

8


134

4.4.10 Sliding Head

A bi-directional sliding head commonly used in microphotography to move cameras

forward and backward or sidewise with respect to a target object was used to move the

object (the cell containing the drop) forward/closer to the lens or backward/away from

the lens. The cell is slowly moved by rotating the knobs of the sliding head until the

drop is in focus in the camera screen.

4.4.11 Droplet Chamber

The droplet chamber/cell was made from aluminium with a rectangular shape and 33.6

mL capacity (depth=3 cm, width = 2.8 cm and length = 4 cm). The front and back walls

had square glass windows each with a side length of close to 2 cm. The glass windows

were made of microscope slides to enhance optical transparency. An aluminium lid was

used to cover the top of the chamber. A bubble level was installed on the lid to allow

proper horizontal alignment of the chamber.

4.4.12 Illumination

Illumination was provided from the back of the drop to the lens. Two different light

sources were used: one for highly transparent oils and the other for semi-transparent

ones. A light-emitting-diode (LED) torch was used with transparent oils. A yellow or

white paper was used to spread the light evenly in the background. When semi-

transparent oil was tested, a stronger light source was used: a 21 Watt tungsten filament

lamp operating at 24 volts (car tail indicator lamp, 21 watt/ 12 V).

4.4.13 Procedure

The following procedure was followed to capture images of sessile drops:

1. The lens focus was adjusted to infinity or 1:1 and kept at this focus during the

following procedure.

2. Bubble levels were used to accurately level the camera and the cell horizontally

by adjusting the tri-pods, tri-axial camera base, and the lockable rotating head.


135

3. The Teflon platform was placed close to the front glass window.

4. The sliding head was used to move the cell bi-directionally by rotating its knobs

until the Teflon platform was in focus in the camera screen.

5. About 25 mL of oil was poured into the cell chamber, and a time of 10-15

minutes was given for the oil to settle down.

6. A syringe was used to eject a drop of the aqueous solution on the platform and

time count was started.

7. The focus on the drop was refined by rotating the knobs of the sliding head until

the drop was in focus in the centre of the camera view.

8. The drop was photographed at different times and the time of each photograph

was recorded. The photographs/images were saved to be processed at later time

to find the dimensions of the drop.

9. When measurement was finished, a plastic pipette was used to suck the droplet.

10. Steps 6-9 in this procedure were repeated for IFT measurement with new drop.

4.4.14 Distance Scale Calibration

The droplet height and median diameter are required in real distance units to calculate

the IFT using the sessile drop. The image processing only gives these dimensions in

pixels. The distance in pixels has to be converted to distance in centimetres at a given

lens focus, say 1:1, the plane of focus should be at a given distance X from the lens, as

illustrated in Figure 4-12. The lens that was used has a very narrow focal depth, so

whenever the focus is adjusted to the focus of 1:1, the plane of focus should be always

at distance X. Therefore, the distance per pixel within the plane of focus should be the

same whenever focus of the lens is adjusted to 1:1.

Figure 4-12: Illustration of the plane of focus of a lens.

X

Z

Y

lens Plane of focus at distance X


136

Objects located before or after this focal plane will appear blurry. Only those on the

focal plane will appear sharp. In this setting, the object is moved to position it in the

plane of focus in front of the lens. The lens focus is always adjusted to 1:1. If the

distance per pixel in the focal plane of 1:1 focus is known, then the size of the object

could be calculated. Other focuses like planes could also be used. The 1:1 or infinity

focuses are preferred because it is easier to adjust the lens to these values.

The focal plane is a 2D surface at distance X from the lens, therefore, the distance per

pixel should be calibrated for the vertical and horizontal distance scale. The horizontal

(Y) and the vertical (Z) distances of the plane of focus are illustrated in Figure 4-12.

The distance per pixel in the plane of focus was calibrated by a focused image of a

vernier scale as can be seen in Figure 4-13. One image of the vernier was taken

horizontal and another was taken vertical to calibrate the distance per pixel in Y and Z

directions of the plane of focus. The images are 5184 by 3456 pixels and the scale is 1

mm per division. The distance per pixel was found equal to 1.847 x 10-4 cm/pixel and

1.838 x 10-4 cm/pixel for the horizontal and vertical distance respectively.

Figure 4-13: Calibration images of vernier scale for 1:1 lens focus, each division is 1

mm.

4.4.15 Refractive Index

In the IFT cell/chamber, the light travels through water drop (or the ASP drop), the

surrounding oil, the glass window of the cell and air to the camera lens. The distance

per pixel in the plane of focus was calibrated in the air, as described in Section 4.4.14,

because it was practically difficult to make calibration in the oil. Therefore, the


137

difference in the refractive indices of the oil and glass may produce an apparent size of

the drop that could be different to its actual size. The IFT of an aqueous droplet

surrounded by oil is determined based on its dimensions. Consequently, it is important

to check for the possible changes of the apparent droplet size induced by differing

refractive indices. The approach was employed to check the change in drop size:

1) A new bearing ball was imaged in air (outside the cell) following the same

procedure as in Section 4.4.13. The bearing ball was suspended by a thin

magnetic bar fitted into the chamber lid.

2) Oil was poured into the cell and the bearing ball was immersed in the oil and

imaged again, Figure 4-14.

3) The height and width of the ball in the oil were measured in pixels.

4) The height and width of the ball in the oil were compared to its height and width

when in air.

Figure 4-14: Bearing ball image in air and oil used to check possible optical size change

Results are shown in Table 4-1, each reading is the average of a doublet. The changes

in height and width were small and negligible. Oil 3 is darker than Ondina 15 which

makes it more difficult to define the drop dimensions. Given the small differences

observed, no statistical work was done to define the confidence intervals or calculate

errors and the effects if the refractive index was ignored in subsequent measurements.

Table 4-1:Change in apparent drop width and height in oil compared to air as seen by the camera lens

width (pixels)

height (pixels) woil/wair hoil/hair

air 3257.5 3261.5 1.000 1.000 Ondina 15 3252 3255.5 0.998 0.998

Oil 3 3253.5 3243.5 0.999 0.994

Air Ondina 15 Oil 3


138

4.4.16 Image Processing and IFT calculations Focused images of droplets are processed using image processing softwares ImageJ

(version 1.42q, National Institutes of Health, USA) and GIMP (Version 2.6.7, The

GIMP Development Team) to measure the droplets’ height and the diameter in pixels.

An Excel® template worksheet was setup to convert these dimensions to real distances

and to directly calculate the IFT using Equations 4-2 and 4-3. The inputs to this

template are the droplet height and maximum horizontal diameter in real distance, the

oil density and the aqueous phase density. The output from this template is the IFT.

Examples of the obtained image are shown in Figure 4-17-a for high IFTs, Figure 4-15

for ultra low IFTs and Figure 4-16 for low IFTs.

Ultra low IFT= 0.002 (mN/m)

Figure 4-15: Droplet age: 5 minutes. System: Dodecane against a solution of 0.05% Alfoterra 145-S4, 5.14% NaCl. Height =27 (pixel) = 50 (µm) diameter =2763 (pixel) = 5103 (µm). Oil density: 0.746 (g/mL) and surfactant solutions density: 1.032 (g/mL), temperature = 25oC.

Low IFT = 0.264 (mN/m)

Figure 4-16: Droplet age: 0.3 minutes. System: Dodecane against a solution of 0.025% Alfoterra 145-S4, 5.14% NaCl. Height =352 (pixel) = 647 (µm), Diameter =1511 (pixel) = 2791 (µm). Oil density: (0.746 g/mL) and surfactant solutions density: assumed 1.032 g/mL, temperature =25oC.

4.4.17 Teflon Platform Lipophilicity and Contact Angle

The Teflon platform was used to provide a lipophilic surface. Teflon has both

hydrophobic and hydrophilic nature but the lipophilic nature is more dominant

Thin Flat Sessile Drop Teflon Platform


139

(Chessick et al., 1956). This gives it its well known oil wetting nature. The top surface

was machined in a concave shape with a radius of 1 foot similar to Schramm’s method

(Schramm et al., 1995). The concave surface centres the drop in the platform. The top

surface of the Teflon platform was then smoothed by abrasive paper.

Figure 4-17: a) Image of a resting water drop on Teflon platform surface surrounded by oil in the sessile drop. b) Close up look of the contact angle of the Teflon surface showing that the contact angle is close to 180o. c) A processed image of image in (b) to aid visual observation of the contact angle between the black and red lines.

The smoothing process could increase the micro-roughness of the Teflon surface, which

could enhance the Teflon surface’s hydrophobicity from a hydrophobic state to a

Teflon Platform

Water drop

Oil

Contact angle at The base surface is close to180o

Surrounding Oil

Water drop

Teflon Platform

(a)

(b)

Contact angle at The base surface is close to180o

(c)


140

superhydrophobic state (Guo et al., 2009). However, it is emphasised that the surface of

the Teflon platform used is only superhydrophobic in the presence of oil. Gao and

McCarthy (2008) argued that the usage of the word “hydrophobic” to describe Teflon

surface should not be taken for granted as its hydrophobicity or hydrophilicity can be

changed by processing the surface with different techniques (Gao and McCarthy, 2008).

The surface of the Teflon platform is superhydrophobic to water in the presence of

surrounding oil, as can be seen in Figure 4-17-a where the contact angle is very close to

180o.

It could be argued that this angle is not 180o by judging on the more apparent contact

angle of the drop in Figure 4-17-b. The limitation of the optics did not enable more

precise measurement of the contact angle. However, when the image is treated with

image processing softwares to sharply distinguish between the boundaries of the drop

and the Teflon platform, the contact angle becomes easier to measure and appears to be

180o or very close to 180o, Figure 4-17-c.

GIMP was used to find the angle between the black and red lines bounding the contact

angle in Figure 4-17-c and found to be 179.5o. However, when the IFT is ultra low, of

the droplets become flat and the images are difficult to process to check the contact

angle. Huh and Reed (1982) realised that the use of sessile drop technique to measure

ultra low IFT may introduce significant errors because of the uncertainty in the contact

angle, especially when the sessile drop is flat (indicative of ultra low IFT) as can be

seen in Figure 4-15. They used the word ‘estimation’ for the IFT measurement using

sessile drop. In this thesis, we also do not claim that the proposed cell measures accurate

ultra low IFT values but the method does provide indicative values and a flat sessile

drop correlates with ultra low IFT. Experimental work done in this PhD to compare

IFT measurements obtained by this cell with the results of spinning drop technique

supported that this cell gives a good estimation of ultra low IFT values.

4.4.18 Cross-Check with Spinning Drop Method

Schramm et al. (1995) showed that the captive drop method gives IFT values

comparable to that of the spinning drop method. However, in this current work, some

modifications were introduced to the captive/sessile drop method and thus a cross-check

with other methods became necessary. The spinning drop is possibly the most used


141

technique for the determination of ultra low IFT, in petroleum related literature

(Schramm and Marangoni, 2000, pp. 18). It involves the spinning of an oil drop in a

sealed capillary tube (2 mm I.D. or a larger) filled with aqueous phase (Taylor and

Hawkins, 1992). The aqueous phase could be the ASP slug, the surfactant or the

alkaline solutions. The tube is placed horizontally and spun around its horizontal axis at

a high rotation rate (high angular speed). The aqueous phase is denser and the centrifuge

force resulting from the spinning will drive the oil drop to the tube centre. The

centrifuge force stretches the oil drop into cylindrical shape. There is a relationship

between the square of the angular velocity of the tube, the drop’s width and IFT. The

drop is assumed to have the same angular speed of the tube after some equilibration

time (rigid body rotation). Figure 4-18 shows an illustration of the spinning drop

method. The governing equation is known as the Vonnegut’s formula for the IFT

measurement using spinning drop technique (Taylor and Hawkins, 1992; Tadros, 2005,

pp. 83).

ρωσ ∆= 32

4

1R

where σ and ∆ρ are as defined above, R is the radius of the cylindrically stretched drop

(half of the stretched drop width), and ω is the angular frequency (radian/s).

The surrounding fluid has to be denser for the method to work. Therefore, the method

requires an aqueous solution to be the surrounding liquid and the oil to be the drop as

compared to the simplified sessile drop method described in this thesis in which the

surrounding fluid is the oil.

Figure 4-18: Illustration of spinning drop at angular frequency ω [adapted from Tadros, 2005]

Hammond (2011) kindly provided data of spinning drop IFT measurement for dodecane

against Alfoterra 145-S4 solutions with fixed sodium chloride salinity. Identical

4-4

R


142

solutions to those Hammond used were prepared and measured here using our apparatus.

Surfactant solutions of Alfoterra 145-S4 were made with variable surfactant

concentrations and a fixed NaCl concentration of 5.14 % (w/v) (Table 4-2).

The measured densities of the different surfactant solutions in this table were all 1.032

g/mL. The small variations of surfactant concentration did not affect the measured

density. The density of dodecane was 0.7460 g/mL. The densities were measured by

Mettler Toledo DE40 Density Meter at temperatures of 20.0 and 30.0 oC, the densities

at 25oC were then calculated as the average of the densities at the two temperatures,

using linear interpolation, which is a valid assumption given the small temperature

range interval between the measurements.

As can be seen in Table 4-2, there is a good agreement between the two IFT methods at

higher surfactant concentrations, but a clear divergence occurs at lower concentrations.

No further work was done to investigate this divergence because the anticipated

surfactant concentration in the actual ASP flood experiments was much higher than

0.01% which was the concentration below which the differences between the spinning

and sessile drop occur.

*The densities: solutions of surfactant is 1.032 (g/mL), dodecane is 0.7460 (g/mL).

4.4.19 Measurements and Results

Following the successful test of the cell more solutions were made including: alkaline,

surfactant, combination of surfactant and alkaline, water, combination of surfactant/

Table 4-2: Sessile drop IFT results compared to spinning drop IFT measurements of dodecane against Alfoterra 145-S4 solutions at temperature of 25 oC* and NaCl concentration of 5.14 % (w/v)

Surfactant Conc.

(% active w)

Spinning Drop

(mN/m) (Hammond 2011)

No. of tests

Sessile Drop

(mN/m)

No. of tests comment

0.05 0.0028 2 0.0026 3 Perfect agreement 0.025 0.0016 2 0.0015 3 Perfect agreement

0.0125 0.0048 2 0.0040 3 Perfect agreement 0.010 0.0033 2 0.0108 3 some difference

0.00625 0.0052 2 0.1480 1 significant difference


143

alkaline/polymer. The concentration of these solutions and the measurements are shown

in Table 4-3. This table confirms that the ASP slug is more effective at providing an

ultra low value of IFT than the individual ASP slug chemicals. Therefore, the process

taking place is in fact an ASP process.

The alkali and the surfactant solution reaches very low IFT within five minutes of the

drop formation. Shortly after that, no vertical height of the drop is observable by the

camera. The ASP combination showed less dynamic IFT, most probably because the

polymer damped and reduced the IFT reduction rate.

Table 4-3: IFT between different combinations of ASP chemicals and Oil 3 or Ondina 15

Aqueous Solution

Concentration (w/v) or ppm

Density (g/mL)

Oil Density (g/mL)

Th (oC)

Average IFT

(mN/m)

Droplet Age

(min)

No. of

tests

ASP 0.5% ,

1% ,1550 ppm 1.003 Oil 3 0.856 25 0.004 20 3

ASP 0.5% ,

1% ,1550 ppm 1.005 Ondina 15 0.844 20 0.487 20 4

NaOH 0.5% 1.003 Oil 3 0.856 23 0.122 20 3 NaOH +

Surfactant 0.5%, 1% 1.006 Oil 3 0.856 20 0.005 ~1 4

NaOH + Surfactant

0.5% , 1% 1.006 Oil 3 0.857 20 0.002 4 1

surfactant 1% 0.998 Oil 3 0.998 23 0.992 20 3 Deionised

water N.A. 0.997 Oil 3 0.856 23 12.339 20 3

Deionised water N.A. 0.998 Ondina

15 0.844 20 43.316 15 1

Figure 4-10 shows this dynamic IFT behaviour. Note that the surfactant (1%) alone

produces IFT of about 1 mN/m and the alkali (0.5%) about 0.1 mN/m. The combination

of these two solutions, at the same concentration brings the IFT down to 0.005 in 1

minute. It becomes difficult to follow the droplet height because it disintegrates. The

longest time for the combination of the alkali and the surfactant was 4 minutes and the

IFT reached 0.002 mN/m. ASP slug needs about 10 minutes or more to reach 0.004

mN/m and stays untacked for longer time.


144

IFT of ASP Combination with Oil 3

0.001

0.01

0.1

1

10

0 5 10 15 20 25

time (min)

IFT

(m

N/m

)ASP NaOH surf surf+NaOH

Figure 4-19: Dynamic IFT for different combinations of alkali, surfactant and polymer against Oil 3.

4.4.20 Limitation of the Method

This simplified cell introduced in this PhD project may be suitable for IFT estimation. It

fulfilled the accuracy requirements for this research and it may also fulfil other proposes

in petroleum research. The proposed method exhibits a number of practical limitations,

including:

The method does not give high accuracy IFT measurements.

The method is limited to transparent or semi-transparent oils.

The method is not recommended for high IFT.

The method is not recommended for low surfactant concentrations below

0.02% (w/v).

4.4.21 Discussion of IFT Measurements The cell showed good agreement with the spinning drop technique applied to surfactant

concentrations above 0.02% (w/v). The estimated IFT between Oil 3 and ASP slug was

found to be about 0.004 mN/m compared to that of DW with Oil 3 of 12.3 mN/m.

Generally, non-contaminated hydrocarbon oil show IFT in the range 30-50 mN/m with

DW at room temperature. Perhaps, Oil 3 contains some components originating from

the Stag Crude oil which may have contributed to the reduction of the IFT between Oil


145

3 and DW from 30-40 to 12.3 mN/m. The IFT values between DW/Oil 3 and ASP/Oil

3 were used to estimate the change of capillary number using Equation 2-13 with the

results shown in Table 4-4. This indicates that this ASP slug could be used for chemical

EOR and could be effective to recover trapped oil after water flooding.

Table 4-4: Comparison of capillary number (Nc) in Sand pack floods

Water Flooding ASP Flooding Injection Rate (mL/min)* 0.07 0.07

Sand pack cross-section (cm2)* 0.739 0.739 Approximated porosity* 0.37 0.37

Interstitial Velocity (m/s)* 4.8 4.8 Viscosity (cP)* 0.98 5.58

Interfacial Tension (mN/m) 12.339 0.004 Capillary Number 3.5 x 10-6 5.9 x 10-2

NcASP /Ncwater ~ 16800 *These values were obtained from work in Chapter 5, porosity is the average porosity of six sand packs and ASP viscosity is the average viscosity of the six ASP slugs used in the ASP floods.

4.5 Conclusion for the Chapter

A mixture of oil (Oil 3) was prepared from Stag Crude and Ondina 15 to ensure

appropriate oil properties for the experiments of the ASP floods. Oil 3 has TAN of 0.07

(mg KOH/ g oil) because of adding Stag Crude. The natural acids in Oil 3 were required

to engage the alkali in the generation of in-situ soaps which is a main characteristic of

the ASP process.

The ASP slug designed in this work was found to be stable and effective in reducing the

IFT with the Oil 3. The IFT of the ASP slug with Oil 3 was estimated using an in-

house-made sessile drop cell. A modified and simplified instrument based on the

equations of Malcolm and Elliot (1980) and work of Schramm et al. (1995) for a special

case of sessile drop was made for IFT estimation because there was no ultra low IFT

apparatus readily available for this project. The cell was tested and showed good

agreement with the measurements of the spinning drop technique for surfactant

(Alfoterra-145-S4) concentrations above 0.02% (w/v). Divergence between the two

methods occurred at lower concentrations. This cell is able to estimated IFT down to


146

0.002 mN/m. This setup is recommended for IFT estimation rather than accurate

measurements.

The IFT cell was used to measure IFT between Oil 3 and the ASP slug which were used

in the ASP sand pack floods, reported in Chapter 5. The IFT between Oil 3 and the

ASP slug was found to have a value of 0.004 mN/m compared to an estimated IFT of

12.3 mN/m between Oil 3 and deionised water. This big change in the IFT by a factor of

more than 3000 is significant and increased the capillary number high enough for the

ASP flood to recover residual oil left behind the water flooding. This low IFT confirms

the efficiency of this ASP slug consisting of 1% (w/v) Alfoterra 145-S4 surfactant,

0.5% (w/v) NaOH as the alkali and 1550 ppm of Flopaam 3360 S as the polymer.

The IFT of the surfactant with Oil 3 was found to be 1 mN/m and the alkali with Oil 3

was found to be 0.12 mN/m. The combination of both surfactant/alkali produced an

ultra low IFT with Oil 3 of 0.005 mN/m. This confirms that both the surfactant and

alkali are engaged in synergic IFT reduction with Oil 3. In the ASP slug, the polymer

addition did not increase the IFT, however, the IFT needed a longer time to reach the

same ultra low IFT value produced by the slug containing only the surfactant and the

alkali.

The ASP slug was made to produce lower phase behaviour with Oil 3 to rule out the

effect of phase behaviour on EOR and to relate exclusively the change in oil recovery to

heterogeneity alone. The ASP slug was found to be stable against phase separation by

aging and found to be stable over a period of several months, compared to the short time

required for its stability in the actual flooding experiments. The emulsion produced by

the ASP slug and Oil 3 was found to be stable for several months after its formation in

both test tubes and actual floods, indicating that Oil 3 and the ASP slug produced

thermodynamically stable emulsion. The type of Winsor phase behaviour of the

ASP/Oil 3 system was determined by electrical resistance and it was confirmed that the

continuous phase is the aqueous phase, thus, the emulsion is oil-in-water (lower Winsor

phase behaviour).

147

5 ASP Floods in Homogenous and Heterogeneous Sand Packs

This chapter reports the experimental study of the impact of longitudinal heterogeneity

of permeability on the Alkaline Surfactant Polymer process. It is valuable to know

which flooding direction will maximise EOR and minimise the impact of heterogeneity.

Several controlled runs of ASP floods, which followed secondary recovery floods, were

applied in macroscopically homogenous and heterogeneous silica sand packs.

Heterogeneity was treated in this context as change in the permeability with respect to

the flooding direction. All experimental control parameters were kept the same in all

runs, only the longitudinal heterogeneity in terms of permeability variation was changed.

The most important observed parameter that was measured to evaluate the impact of

heterogeneity was the oil recovery which was determined by oil and water saturations

based on the measurements of sand packs mass. Other evaluated parameters were

production rate, emulsion production, oil cut and injection pressure.

5.1 Experimental Approach to Study Heterogeneity Im pact As discussed earlier in Chapter 2, the ultimate recovery in an oil recovery process,

whether primary or secondary, generally, is affected by the reservoir heterogeneity. The

most important variables in ASP flooding experiments are: ASP slug size and

composition, phase behaviour type, IFT, flow rate, salinity, oil properties and

composition, sand type and adsorption/retention of ASP chemicals in the porous

medium (Sheng, 2010; Ahmed, 2001; Green and Willhite, 1998; Lake, 1989).

The aim of this experimental investigation was to test the impact of heterogeneity on the

ASP flood process by separating out the contribution of other factors which could affect

the ASP process. In order to achieve this and treat heterogeneity as the only variable

responsible for the change in oil recovery, all variables in the experiments were kept

constant in several ASP floods except the heterogeneity which was changed in a

controlled manner. Consequently, six different sand packs of the same size dimensions

were made in pairs with different heterogeneity configurations as shown in Figure 5-1.

Chapter 5: ASP Floods In Homogenous and Heterogeneous Sand Packs

148

Four of these six sand packs were made to be identical. Heterogeneity was treated in

this context as a change in the permeability with respect to the flooding direction as

described below. Sand packing was done, to the best of efforts, using similar sand

increments and sand pressing procedure to ensure the repeatability of permeability and

porosity. The quality of the repeatability was checked by the mass gradient (mass in

grams per centimetre) of the sand in the pack, the porosity and the permeability of the

sand pack.

Figure 5-1: Heterogeneity configurations of the sand packs with sudden permeability

change.

5.2 Target Permeabilities for Chemical Flooding

Candidate reservoir formations, considered for chemical flooding involving polymers

such as ASP flooding or polymer flooding, are preferred to have permeabilities higher

than 0.5 D. Examples of such fields include the Daqing Oilfield (China) with a targeted

reservoir formations having permeability of ~0.5 D (Wang et al., 1997), the Gudong

Oilfield (China) with a targeted formation having permeability of 2.6 D (Qu et al., 1998)

and the Alkhlata Formation in Marmul Oilfield (Oman) with an average permeability

of about 15 D (Koning et al., 1989). Therefore, the permeabilities used in the sand pack

experiments were chosen to be within the range found in potential reservoirs for the

application of chemical EOR. Emphasis was placed on the permeability contrast along

the flow path of the ASP slug because this study is aimed to investigate the influence of

longitudinal heterogeneity of permeability on the ASP process within one layer.

Flow direction

Legend: Low Permeability High Permeability

Lower-to-Higher Permeability Higher-to-Lower Permeability Higher Permeability Lower Permeability

Heterogeneous Heterogeneous Homogenous Homogenous

Symmetry axis


149

5.3 Experimental Work Flow Secondary recovery and EOR experiments were conducted in the following manner. In

each run, a sand pack was first prepared with some heterogeneity initiation and its mass

and exact dimensions were recorded. Next, the sand pack was saturated with water.

Subsequently, oil was then injected to displace the saturation water in order to saturate

the sand pack with initial oil. Afterwards, water flood and ASP flood were started to

recover oil in the secondary and EOR modes respectively. The injection pressure was

recorded during both flooding stages. The fluids produced during the floods were

collected to measure the production rate, oil cut, emulsion production and the chemical

profiles. Thereafter, the impact of the longitudinal heterogeneity was evaluated by the

changes in oil recovery, oil cut, flow rate, chemical profile of produced ASP and

injection pressure response. The methods on how the control parameters were kept

constant in all runs and how the observed parameters were evaluated are discussed

below.

5.4 Sand Pack Preparation

5.4.1 Materials of the Sand Packs Glass tubes used were high pressure-heavy wall gauge glass tubes each cut to 150 cm

long and 0.97 cm internal diameter of SCHOTT DURAN. The O.D. is ~ 1.45 cm.

Flow plugs were made of Teflon and were specially manufactured at CSIRO workshop

to exactly fit the internal diameter of the glass tubes. Each plug has two O-rings to

prevent leakage. Production and injection tubings were passed though its centre and

fixed to the Teflon by Swagelok® stainless steel fittings.

-300 µm silica sand was supplied by Cooks Industrial Minerals and washed with

deionised water (DW) before use and then dried at 90 oC. The negative sign placed in

front of the sand grain size is used in this thesis to indicate that the largest grain size of

this sand is 300 µm. The grain size distribution as provided by the manufacturer, is

shown in Figure 5-2. This distribution had probably changed after the DW wash. This

sand was used to construct the higher permeability sections of the sand packs.


150

Grain Size Distribution of the -300 µm Silica Sand

0102030

405060

6005004253002121501067553

Grain Size Range (µm)-Histogram

Per

cent

Ret

aine

d (%

)

0

20

40

60

80

100

0 100 200 300 400 500 600

Grain Size (µm)-Cumulative Passing Curve

Cum

ulat

ive

Pas

sing

(%

)

Figure 5-2: Grain size distribution of the -300 µm silica sand before sand washing, note that the primary and secondary x-axes are not equally scaled.

-75 µm silica sand (silica flour): supplied by UNIMIN (now part of Sibelco Group,

Australia) and was washed with DW before use and then dried at 90 oC. The negative

sign is to indicate that the largest grain size is 75 µm. The grain size distribution as

provided by the manufacturer is shown in Figure 5-3. The distribution of this sand had

most likely changed significantly after the DW wash given the fine size of the grains.

Note that, the exact grain size distributions of these sands are not important for this

project.

Sand mixture consisted of a mixture of the two above sands with the following

percentages: 92% (w/w) is -300 µm and 8% (w/w) is -75 µm. This sand was used to

construct the lower permeability sections of the sand packs.

Grain Size Distribution of the -75 µm Silica Sand

0

10

20

30

40

7553382010642

Grain Size Range (µm)-Histogram

Per

cent

Ret

aine

d (%

)

0

20

40

60

80

100

0 15 30 45 60 75

Grain Size (µm)-Cumulative Passing Curve

Cum

ulat

ive

Pas

sing

(%

)

Figure 5-3: Grain size distribution of the -75 µm silica sand before sand washing, note that the primary and secondary x-axes are not equally scaled.


151

5.4.2 Sand Packs Dimensions The sand packs were intended to represent 1D experiment, Figure 5-4. The diameter

was deliberately selected to be narrow compared to its length. The plugs took some

length at both ends of the tube and this made the sand column length to be

approximately 147.5 cm. In order to retain the sand inside the sand packs, small circles

were cut of a thin scouring plastic pad (mesh) to the size of the glass tube internal

diameter and were placed between the Teflon plugs and the packed sand. These were

successful in preventing sand leaching from the sand pack to the production/injection

ports. Also, the pads were slightly springy and helped packing the contents. The mesh

thickness is very small (~0.5 cm) compared to the total length of the packed sand

column (~148 cm), therefore, their contribution to the storage and oil recovery is

negligible. Moreover, meshes of the same size were used in all the runs, thus, their

contribution (if have any significance) should be equal in all the runs.

Figure 5-4: Diagram showing dimensions and configuration of the heterogeneous and the homogenous Sand Packs.

5.4.3 Heterogeneity Construction and Configuration

In the experiments of water and ASP floods the heterogeneity was introduced, as

mentioned earlier, in terms of permeability change. The permeability variations were

configured, in this investigation, to provide two cases: 1) heterogeneous; increasing or

decreasing permeability with respect to flow direction and 2) homogenous; same

permeability in the whole sand pack. The heterogeneous sand packs could have, with

Section 1

~74 cm ~74 cm

~148 cm

Section 2 I.D. ~ 0.97 cm

I.D. ~ 0.97 cm

Mesh Teflon plug

Tubing to Injection or Production

Homogenous sand pack

Heterogeneous sand pack


152

respect to the flow direction, decreasing or increasing permeability sequence/transition:

either lower-to-higher or higher-to-lower permeability transition. In the heterogeneous

sand packs, the permeability transition is not gradual rather a sudden change of

permeability in the direction of flow.

The homogenous sand packs were packed with a single sand for the whole tube length.

The heterogeneous sand packs were packed into halves each with different sand to

provide permeability variations along the path of the flowing fluids. One sand (-300 µm)

was selected to construct the higher permeability zones, while, a mixture of sands was

used to construct the lower permeability zone. Mixing the sands is described latter in

this chapter. There is a clear boundary between both sands as can be seen in Figure 5-5.

Figure 5-5: Image shows the boundary between the lower and higher permeability sections. The lower permeability section is to the left of the dark mark (on glass tube wall), while the higher permeability is to the right of the mark.

5.4.4 Sand Washing

Both fine and coarse sands were cleaned separately in deep buckets with DW: 3 kg of -

300 µm and 1 kg of -75 µm. DW was allowed to flow slowly from the bottom of the

bucket upwards. Some of the very fine sand grains and organic material were observed

to float out of the bucket. This meant that the potential problems, which are usually

associated with fine migration, such as plugging the production thin tubes or changing

the absolute permeability should not arise in the actual sand packs floods because most

of the fines would have been removed during the wash. Then both sands were dried

separately in oven at 90 oC in different porcelain trays. Sands were left in the oven until

they were completely dry.

An acid wash was proposed to clean the sands of the organic material but was avoided

to reduce the operational and OHSE challenges as such sand would require the acid

Boundary of heterogeneous sand pack


153

wash to be performed in a dust extraction cabinet. The available cabinet was not

equipped for sand wash. As the same sands were used in all runs, possible

contamination by organics would have affected all experiments equally and therefore

would not complicate the results.

5.4.5 Sand Mixing and Permeability Control

Several trial sand packs were initially made to study the effect of mixing the -75 µm

with the -300 µm sands on the observed absolute permeability. Mixing these sands in

different proportions (by mass) changed the permeability. See Figure 5-6 for the

resultant permeability of the packs containing mixtures of the two sands. The

permeability was determined by single phase flow at a known rate and measuring the

corresponding pressure drop across the pack. Darcy’s Law was used to determine the

permeability for the pressure drop. All of these permeabilities in this figure were

calculated based on water flow except for the highest permeability in the curve (at 100%

-300 µm sand) which was calculated based on oil flow.

5.4.6 Construction of Lower and Higher Permeability Sections

In sand mixing, it was found that a percentage of 8% (w/w) of -75 µm and 92% (w/w)

of -300 µm gave a permeability of ~1.5 D. This mix was used to construct the lower

permeability sections in the composite sand packs. The -300 µm sand was used to

construct the higher permeability section~ 6 D. The sand pack configurations are shown

in Figure 5-4.

Note these permeabilities of both the higher and lower permeability sections in the sand

packs are considered high permeabilities from a reservoir engineering point of view

(Ahmed, 2001; Dandekar, 2006). As discussed earlier, although these permeabilities are

relatively high, they are within range of permeabilities encountered in real reservoirs

(Wang et al., 1997; Koning et al., 1989; Qu et al., 1998). The use of the terms ‘low

permeability’ or ‘lower permeability’ to describe the permeability of sand packs used in

this study is meant to be “relatively low” compared to the permeability of the other

section.


154

Permeability of the Sand Mixture of -300 µm and -75 µm in Different Propotions

6

0.170.431.51

0.2301234567

0 20 40 60 80 100

Percentage of -75 µm sand in the mixture

Per

mea

bilit

y (D

)020406080100

Percentage of -300 µm sand in the mixture

Figure 5-6: Permeability of the 150 cm long sand packs as a function of the ratio of the amount of -75 µm and -300 µm sand.

5.4.7 Sand Packing Procedure

The sand mixture (92% is -300 µm sand + 8% -75 µm sand) was dry packed in

increments of about 1.5 g to construct the lower permeability section. Each increment

filled slightly more than 1 cm height of the glass tube. The 1.5 g sand increments were

poured vertically into the narrow glass tube through a plastic funnel. Then a 1.8 m long

wooden stick with a diameter of 9 mm (the glass tube I.D is 9.7 mm) was inserted and

used to hand-tamp the sand. On average 10 vertical hits were applied by the stick using

hand force. For each strike, the stick was raised about 10 cm above the sand level and

thrust down. In order to ensure consistently, this procedure was performed after adding

each increment of 1.5 g of sand. The same procedure was employed with the -300 µm

sand to construct the higher permeability section. The -75 µm sand may contain free

crystalline quartz which could harm the lungs and eyes. Therefore, the packing process

of the sand mixture needed to take place in an area that has dust extraction facility. A

dust mask and safety glasses were also worn for safety and health considerations as well

as to comply with the safety regulations.

5.4.8 Sand Pack Pairs

In total, apart from the initial trials, six sand packs were made and were grouped in pairs.

Four out of these six were made heterogeneous and practically identical. The only


155

difference applied to these four packs in the flooding experiments was the direction of

the flooding with respect to the permeability transition as shown in Figure 5-1. The

remaining two were a homogenous pair of sand packs. The first one of these

homogenous sand packs had high permeability, and the second had a relatively lower

permeability. The heterogeneous packs had higher and lower permeability sections.

Care was taken to reproduce the higher permeability sections with close permeability

value in the four heterogeneous sand packs as well as to reproduce lower permeability

section with the same permeability value by using the exact packing procedure. The

sand pack pairs are below:

Pair 1:

SP15: homogenous sand pack with lower permeability sand (the sand mixture).

SP18: homogenous sand pack with higher permeability sand (the -300 µm).

Pair 2:

SP16: heterogeneous sand pack with higher-to-lower permeability transition.

SP17: heterogeneous sand pack with lower-to-higher permeability transition.

These two were tested with ASP slug containing polymer 3630 S.

Pair 3:

SP19: heterogeneous sand pack with higher-to-lower permeability transition

SP23: heterogeneous sand pack with lower-to-higher permeability transition.

These two were tested with ASP slug containing polymer 3430 S.

Pair 3 was a duplicate of Pair 2 in terms of heterogeneity. In other words, SP19 is a

repeat of SP16 and SP 23 is a repeat of SP17. The only classifying difference between

these two pairs comes from difference in the molecular weights of the polymers which

were used to make the ASP slug. The ASP slug used for the last pair had a polymer with

lower molecular weight (Flopaam 3430 S) which was used as a measure to reduce flow

impairment in the water drive stage. This change was not effective to eliminate the flow

impairment. More on this will be discussed later.

There were other sand packs which were made and used in different experiments e.g.

SP22, SP21 and SP11. In total about 23 sand packs were made and used in different


156

trials and experiments. The above six sand packs were specifically prepared for the

heterogeneity experiments.

5.4.9 Air Removal from Sand Packs

CO2 was injected into the sand pack to remove air. The pre-saturation with CO2 and

saturating with DW at low injection rate were preformed to ensure the removal of air

bubbles which could affect the experiment outcome. Visual inspection was done

regularly through the transparent wall of the glass tube to ensure no air bubbles are

trapped inside. During the water saturation stage the sand pack was held slightly off the

vertical, so any mobile bubbles will reside close to the upper side of the wall. Large

bubbles should merge close to the wall because of the thin nature of the glass tube.

Although, any bubbles trapped inside the sand pack away from the wall cannot be seen,

the fact that no bubbles were seen near the wall should indicate that the sand pack had

negligible air. Given that the dimensions of the sand packs, the sand type and the

injection rates were all the same, one can assume that the heterogeneous sand packs

would have similar trapped air volumes. The fact that the heterogeneous sand packs

yielded similar pore volume sizes and porosities after saturating with DW confirms this

assumption (Table 5-2). Therefore, the effects of any possible trapped air are equalized

in the heterogeneous sand packs. This assumption does not hold for the homogenous

sand packs (Pair 1) because one of these sand packs has entirely the sand mixture (92%

is -300 µm sand + 8% -75 µm) whereas the other has entirely the -300 µm sand.

5.5 Water and ASP Floods

Once the sand pack was prepared for the flooding experiments as described above the

injection and production plugs were installed. Then the flooding stages are started and

the ASP slug is injected into the sand packs. The same process was repeated in several

sand packs. The details of the facility that was used in the flood runs are described

below followed by detailed experimental procedure conducted in each run.

5.5.1 Experimental Parameters

These parameters were kept the same in all the experimental runs:


157

Injection rate: 0.07 mL/min (Interstitial speed of ~12 ft/day)

Water Flood duration and size: 3.5 PV of injection

ASP slug size and injection rate: 0.07 mL/min size of ~0.4PV of injection.

ASP slug Composition: 1% (w/v) Surfactant – Alfoterra 145-S4 (active), 1550

ppm polymer and 0.5% (w/v) NaOH.

IFT between Oil 3 and ASP slug: found to be dynamic and reached about 0.004

mN/m in 20 minutes at 25 oC.

IFT between DW and Oil 3: found to have a value of ~12.3 mN/m.

Salinity: no NaCl was added and DW was used to prepare the ASP slug as well

as to saturate the sand packs and conduct the water floods.

Phase Behaviour: Lower Winsor phase behaviour.

Oil 3 viscosity is 30.00 cP @ 20oC and 19.90 cP @ 30oC

Water viscosity is 1.00@20oC and 0.80@30oC

Viscosities and Densities of some of the used ASP slugs are listed in Table 5-1.

Temperature: Room temperature which varied in the range 18-24 oC. Although

it is more appropriate to control the room temperature, it was not possible to

control the experiment temperature given the large size of the flooding set up

(Figure 5-8) and the laboratory space (about 5 meter by 15 meters and height of

about 4 meters). The ASP slug used anionic surfactants which is not as sensitive

to the temperature as the nonionic surfactants. The change in the room

temperature was acceptable for flooding experiments which faired from 18-24 oC in each experiment. Generally, these small variations in the room temperature

are not expected to complicate the results.

Table 5-1: Viscosities and Densities of ASP slugs at start of each ASP slug ASP slug of SP Viscosity (cP)

@ 25 oC Density (g/mL)

@ 30 oC Density (g/mL)

@ 20 oC ASP- SP15 5.74 1.001 1.005 ASP- SP16 5.43 1.003 1.005 ASP- SP17 5.72 N.D. N.D. ASP- SP18 5.84 N.D. N.D. ASP- SP19 5.46 1.002 1.005 ASP- SP23 5.30 N.D. N.D.


158

5.5.2 Sand Pack Flooding Setup The flooding setup was originally designed for the injection of one phase at high

injection pressures. At the start of this study, there were only two exchange cylinders

which were not prepared for chemical injection and there was no mechanism to sample

or quantify produced fluids. The facility was entirely equipped with high pressure

gauges (3000-10000psi). Some modification were necessary to enable EOR

experiments such as adding and installing: a fraction collector for liquid sampling and

quantification, three exchange cylinders compatible with the ASP slug chemicals,

pressure reducing regulator and pressure transducers with relevant pressure ranges. The

final setup consisted of: a piston pump with a precision stepping motor, injection

cylinders, three exchange cylinders, pressure transducers, tubing and valves, data

acquisition card, a computer and a fraction collector. Figure 5-7 shows a schematic of

the experimental setup. A metallic rig was made to hold the sand pack vertical and to

maintain the fraction collector above the production side of the sand pack because

injection was done vertically with flow direction upwards. Figure 5-8 shows a

photograph of the flooding setup. The models and the brands of the flooding facility

parts are listed below:

The pump was a syringe type equipped with a precision stepping motor that

displaces twin pistons in twin injection cylinders which had a total capacity of about

650 mL. The stepping speed of the pump was calibrated to its discharge rate (Figure

5-9) at atmospheric pressure.

Exchange cylinders: Three exchange cylinders designed to operate in pressures up

to 3500 psi were connected to the pump injection cylinders. One cylinder was used for

oil, one for DW and one for the ASP slug. The pistons inside these cylinders separate

the hydraulic fluid from the liquids prepared for injection.

Valves: Swagelok® brand of shutoff type (ball valve) except for one which was a

metering valve Swagelok® model SS-SS2.

Pressure Tubes: Some were 0.25 inch and some 0.125 inch O.D. (Swagelok®).

Pressure Regulation Unit: A metering valve, a reducing pressure regulator and a

pressure relief valve were placed just ahead of the injection pressure transducer, Figure


159

5-12. The pressure relief valve was not used as a safety device but was used as a

pressure regulating step that was set to ~ 520 psi. The inlet of this valve was the tubing

coming from the exchange cylinders of water and ASP slug. The outlet of this valve

was connected to the pressure regulator inlet. At constant injection rate the reducing

pressure regulators regulates the pressure downstream whereas back pressure regulators

maintain constant pressure upstream. The maximum outlet pressure of the regulator

(inlet to sand pack) was set to roughly 190 psi (four knob turns). A metering valve

(Swagelok® model SS-SS2) was used and was fully opened.

Injection Line : Refers to the part of the injection system between the exchange

cylinders and the injection tubing. It includes the injection pressure transducer, the

pressure regulator, inline safety valve, the shutoff valves and the tubing connecting all

of these. The tubing upstream of the pressure regulator is a combination of 0.25 inch

(O.D.) and 0.125 inch (O.D.) stainless steel. The tubing between the pressure regulator

and the inline injection pressure transducer is 0.125 inch (O.D.) plastic tube. The inline

safety valve and the reducing pressure regulator are used as two stage pressure

regulation mechanism.

Pressure regulator and its safety valve: The pressure reducing regulator was a

spring loaded Swagelok® KLF series, model KLF1GRA411A20000, with 3500 psi

maximum inlet pressure (High pressure port) and 0-250 psi outlet pressure (Low

Pressure port). The pressure relief valve (used as a first stage pressure regulation) is a

Swagelok® brand with operating pressure range of 350-750 psi.

Back pressure regulator: A spring loaded Swagelok® with working inlet pressure

of 60-10000 psi.

Injection and production tubing: These plastic tubes are part of the Teflon plugs

as mentioned in Section 5.4.1. The injection tubing starts downstream of the pressure

transducer. It is a transparent plastic tube with O.D. of 1/8 inch and I.D. of 0.14 cm. The

production tubing starts from the production plug and goes to the fraction collector and

is made of two plastic tubes. The first part is 11 cm long coming from/through the sand

pack’s Teflon plug and its I.D. is 0.14 cm (1/8 inch O.D.). The second part is a thinner

plastic tube 23 cm long and its I.D. is 0.8 mm. These two tubes are connected to each

other and to the fraction collector by a tubing coupler. The thin tubes were employed to

reduce post mixing in the produced fluids.


160

Figure 5-7: Schematic diagram of the sand pack flood experiments setup.

Figure 5-8: Photograph of the experimental setup of the sand pack. The sand pack is fixed to the wooden base by strings and nails. The wooden base is clamped and fixed to the rig. The flow direction is upwards.

7 µm Inline filter

Metering Valve

Production port to Fraction collector

Pressure Reducing Regulator

Sand pack in transparent glass tube

Computer

Pump controls

Data acquisition card

Injection Pump and cylinders

Oil water

Chemicals Cylinder

Injection Pressure transducer

Three way Valve

piston

Re-filling port

Tubing

Pressure relief valve Used as first stage pressure regulator

Valve

Pump Pressure Relief Valve

Exchange Cylinders

Pump Main Valve

Pressure Regulation Unit

Production plug and tubing

Injection plug and tubing

Fraction Collector Sand Pack Wooden base Pressure Reducing

Regulator & Modified Safety valve

Injection Pressure Transducer

Box of Exchange Cylinders

Pump Controllers

Injection Lines

Injection Teflon Plug

Production Teflon Plug

Injection Tubing


161

Calibration Line Between the Pump Motor Stepping Ra te and Discharge Rate for Low Injection Rates

y = 0.0147x - 0.007

R2 = 0.9992

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0 10 20 30 40 50 60Motor Speed Rate (steps/second)

Dis

char

ge R

ate

(mL/

min

)

Figure 5-9: Calibration line of motor stepping and pump discharge. Each point in the graph is an average of 5 or more measurements of discharge rate of the pump at a given stepping speed.

Inline Filter : A Swagelok® 7 µm inline filter was used in the tubing line of ASP

slug ahead of the safety valve and the pressure regulator. This component is important

to ensure that any possible gels in the ASP slug will be broken up before injection into

the sand pack.

Primary pump’s safety valve: Is a Swagelok® brand and set to relief pump’s and

injection line pressure when it approaches 2800-3000 psi. It is installed at the discharge

port of the twin injection cylinders and ahead of the exchange cylinders. It protects the

pump and the whole injection line from unexpected high pressures build up.

Temperature Sensor: Is a thermocouple and its brand is not known. Prior to the

experiments its reading was compared against a mercury thermometer and a good

agreement was found.

Pressure transducers: Two pressure transducers were used; one for injection

pressure and one for pump pressure. The injection transducer range is 0-150 psi and

which could handle an overpressure of 75 psi making it up to 225 psi (Brand: RS ,model:

348-8093) The pump pressure transducer range is 10,000 psi (Brand: Data Instruments,

Model: AB OPTION 7HP).


162

Data Acquisition Card: Is from National Instruments model (PCI-6031E)

Control and Data Acquisition Software: Precision Data Acquisition and Control

Version 8.02.02 developed by Masizame Technologies (Riordan Cox and

Associates Pty Ltd) on a LabView (National Instruments) based environment and

was used to control pump, collect and store pressure and temperature data.

5.6 Flooding Procedure

The flooding experiments started with the installation of the sand pack on the injection

rig. Then the process of saturating and flooding the sand pack was performed. During

this process, the mass of the sand pack was measured between the saturation and

flooding steps to calculate changes in oil and water saturations. Produced fluids were

then collected to determine production rates, emulsion production, oil cut and chemical

composition.

5.6.1 Installation and Removal of the Sand Pack on the Flooding Rig The installation of the sand pack on the rig involved three steps. In the first step, a

wooden base with curved surface was used to hold and centre the sand pack vertically

during the floods and saturation stages. Strings/cords and nails were used to firmly fix

the sand pack to this base. In order to install the sand pack on the wooden base, the ends

of the injection and the production Teflon plugs at both ends of the sand pack were first

tightly placed between nails pre-set in positions matching the length of the sand pack.

Strings at end and middle of the sand pack were used to tighten the sand pack to the

base and the plugs to the nails. This ensured that the plugs would not move out or creep

when the injection pressure was increased.

In the second step, the injection tubing was connected to the injection line. The injection

plug and production plug each has quick-connect fittings. The injection tubing was

connected/disconnected/re-connected to the pressure transducer at the end of the

injection line by Swagelok® fittings. In the third step, the sand pack was slowly and

carefully placed in a vertical orientation and mounted on the rig. The wooden base itself

has nails which fit to holes in the metallic rig frame. When the nails are placed in the

holes correctly, then it could be firmly clamped and fixed vertical to the metallic rig


163

body by strings/cords. The wooden base could be easily attached or detached from the

metallic rig. Finally, the production tubing was connected/disconnected/re-connected to

the fraction collector by a tight plastic tubing coupler. The process to remove the sand

pack from the rig is essentially in reverse order to the installation process.

5.6.2 Injection Sequence

The injection sequence is defined in 4 main stages which are: saturating with CO2,

saturating with DW, saturating with oil, water flood and finally ASP flood with water

drive. The mass of the sand pack and its Teflon plugs with their tubing was measured

between each stage. The detailed sequence of the experimental procedure followed in

each run is as follows:

1. Assemble the sand pack and install production/injection Teflon plugs with their

tubing

2. Measure the dry mass of the sand pack while the sand pack was sitting horizontally.

The dry mass of the sand pack was measured including the production Teflon

Production plug and its production tube and injection Teflon plugs with it injection

tubes. The plugs were cleaned and dried at the beginning of each run.

3. Place and fix the sand pack on the wooden base as mentioned in Section 5.6.1.

4. Saturating with CO2

4.1. The sand pack was mounted vertically and connected to the CO2 cylinder.

4.2. The sand pack was saturated with CO2 by injecting CO2 at a high injection rate

by setting a gas pressure regulator initially to 30-50 psi and let it flow freely

though the sandpack for 7 minutes or more.

5. Saturating with water

5.1. The sand pack was placed slightly off vertical.

5.2. The sand pack was slowly saturated with DW by flooding at a low injection rate

of 0.07 mL/min and the pressure response was recorded.

5.3. The frontal movement of the water was monitored to check that the water front

was stable and effectively removing the CO2 and that no gas bubbles were left

behind.

5.4. About 4 PVs of DW were injected before the injection was stoped.


164

5.5. The sand pack was slowly placed horizontally with the wooden base.

5.6. The sand pack with its production/injection plugs and their tubing intact was

removed from the base and placed horizontally on a mass balance.

5.7. The mass of the water saturated sand pack was measured, the pore volume was

then determined by the mass difference, the DW density was assumed to be

1.000 g/mL. The sand pack was moved gently and slowly to avoid inducing

disturbance. It was assumed that no significant CO2 bubbles remained in the

vertical sand packs. Visual inspection through the glass supported this

assumption. Any remaining bubbles would be micro bubbles and should not

take up significant volume in the unconsolidated sands.

5.8. The injection line was flushed with oil in preparation for oil injection, any air

getting into the sides of injection lines or tubes while the sand pack was off the

injection line was removed and the injection line was re-connected to the sand

pack

5.9. The sand pack was carefully placed vertically again.

6. Saturating with oil:

6.1. Oil was injected to saturate the sand pack with oil at an injection rate of 0.07

mL/min while the sand pack was vertical; 3.5 PVs of oil were injected.

Produced fluids were collected in a graduated tube. The volume of produced oil

and water were then measured.

6.2. Then the sand pack was left for ~24 hours undisturbed, further discussion on

this point is at the end of this experimental sequence.

6.3. The sand pack was slowly placed horizontally and taken out of the wooden base.

6.4. The mass of oil saturated sand pack was measured to determine initial oil

saturation and irreducible water saturation.


7. Water flood (Secondary Recovery)

7.1. The water flood was started at a constant injection rate of 0.07 mL/min, the

pressure was recorded, and the effluent is collected once the flow started.

7.2. A fraction collector was used to sample the produced liquids every 42.86

minutes in 3.5 mL glass vials.

7.3. The water flood continued until no significant oil was produced, that is, residual

oil saturation was achieved. Typically this was achieved by injecting for 3.5 PV

(~34 hours).


165

7.4. The sand pack was slowly placed horizontally and removed from the wooden

base.

7.5. The mass of the sand pack at residual oil saturation was measured to determine

the residual oil saturation.


8. ASP flood and water drive (EOR)

8.1. The ASP slug was injected for 0.4 PV, and then DW was injected for 2 PVs as a

water drive. The produced fluids were sampled in 3.5 mL glass vials, each vial

set to collect for 42.86 min.

8.2. The sand pack was slowly placed horizontally.

8.3. The mass of the sand pack after the ASP flood and water drive was measured to

determine the enhanced oil recovery.

8.4. The effluents from the ASP flood were chemically analysed to find the

concentration of the ASP components.

In performing item 6.2 in the experimental sequence above, the oil injection is

continued long enough to ensure that moveable water is removed from the sand pack

and irreducible water saturation is reached. This could be checked by inspecting the

transparent production tube. When no water is produced then, the irreducible water

saturation is reached. The remaining water in the sand pack at this saturation is captured

by capillary forces. The buoyancy forces are not able to move the water or oil

down/upwards when at its residual saturations as discussed in Chapter 2. The oil

distribution was checked along the sand pack by eye inspection. If there was buoyancy

force taking effect during the aging time of the 24 hours, the bottom of the sand pack

would show more water and the top of the sand pack would show more oil. In all the six

well-controlled experiments the oil distribution was even along the sand pack. This

confirmed no buoyancy has occurred during the aging stage. Therefore, this water

should not affect the outcome of the experiments.

In any case, the same procedure of saturating with oil has been applied to the six well-

controlled experiments, therefore, if any significant influence of buoyancy has existed it

should affect all the experiments equally and thus, the heterogeneity in the permeability

remains the only variable that has been changed in the experiment.


166

5.6.3 Pore Volume Determination

The pore volumes of the sand packs were determined by the mass difference between

the dry mass and the water saturated mass of the sand pack. The dry mass of the sand

pack included the glass tube, the mesh, the production and injection Teflon plugs and

their tubing. The water saturated mass of the sand pack included the dry mass of the

sand pack plus the mass of the water retained inside the sand pack and the mass of the

production/injection tubing. The mass of water retained inside the production/injection

tubing was found to be 0.660 g. Therefore, the net mass of the water within the sand

column of the sand pack can be accurately calculated. The mass is converted to volume

by dividing by the water density assuming a water density of 1.000 g/mL.

5.6.4 Oil and Water Saturations Determination Method

The saturation of oil and water before and after the EOR application is the main criteria

to detect the effect of the heterogeneity on the ASP process. The saturations of the sand

packs were determined by measuring the dry mass of the sand pack and its saturated

mass. The saturation of oil and water can thus be determined by Equations 5-1 and 5-2.

Care must be taken to move the sand pack horizontally as quick as possible to measure

the mass and to minimise disturbance to the sand pack. However, since the residual

saturation consists of capillary trapped oil, weak mechanical disturbance is expected to

have no significant effect. The derivation of these equations is provided in Appendix

B1. The relationship employed to calculate the residual saturations are:

ow

o

wPV

m

Sρρ

ρ

−

−∆

=

wo SS −= 1

where PV is the pore volume of the sand pack (mL), ∆M is the difference between the

dry and wet (saturated) mass of the sand pack (g), ρo is the oil density (g/mL), ρw is the

water or ASP slug density (g/mL), So is the oil saturation (fraction) and Sw is the water

saturation (fraction).

5-1

5-2


167

5.6.5 Measurements of the Production Rates The injection rate was set to a constant flow rate of 0.07 mL/min. The produced

fractions of the oil and water would change as the saturation of the phases change inside

the sand pack. In order to quantify the fractions of oil and water in the produced fluid

the produced fluids were collected in cylindrical glass vials. The dry mass of each vial

was recorded before and after the cylindrical vial was filled. Then a digital image of the

vial was taken for further analysis, Figure 5-10 displays an image of vials containing

produced liquids of: ASP slug, water, oil and emulsion which were sampled from the

ASP flooding and water drive experiment in SP16.

Figure 5-10: Photograph of SP16 vials, with ruler as a reference. The vials contain the oil bank and emulsion. The initially transparent ASP attained a brownish colouration in samples 14 and 15. The image is analysed to determine volume of the oil and water fraction, the method

details are described in Appendix B2. The collection time was pre-set and known so the

production flow rate of each phase could be calculated. The oil recovery measurements

were mainly based on mass measurements to ensure accuracy. The error in the flow rate

from images was not measured and assumed to be small.

5.6.6 Constant Flow Rate Control

A good control over the injection rate was important to maintain repeatable injection

rates in all flooding runs. In ideal situation, a constant production rate from a high

permeability sand pack fully saturated with oil and water should equalise the targeted

Oil Water

Emulsion ASP


168

injection rate almost spontaneously. In reality, it was difficult to maintain a constant

injection/production rate in the trial flooding experiments. The production rate took

significant time to equalise with the injection rate. The time lag required for the system

to equalise the production and injection rates originates from the storage capacity

(compressibility) of the injection system. The issue is encountered in experiments when

low injection rates are used. The storage capacity issue has been theoretically addressed

by several works for the determination of very low permeabilities using constant

injection rates (Esaki et al., 1996; Zhang et al., 1998; Fox and Zhu, 1999).

As the injection pump’s piston is advancing at a constant stepping rate to produce a

constant injection rate, the fluid may continue to compress inside the injection system

instead of being discharged into the sand pack at a stable constant rate. High injection

rates could resolve the issue, however, in EOR evaluations, it is important to get

accurate results of the EOR methods free form the influence of high injection rates

which are not sustainable in real reservoir flooding.

The initial setup of this injection system (described in Section 5.5.2) was not suitable

for injection of low constant rates for low injection pressures due to the large total

amount of liquids contained in the pump cylinders, injection lines and exchange

cylinders. Figure 5-11 shows an ill-controlled secondary flood experiment where a

target constant injection rate took long time to stabilise using this setup. As no other

flooding setup was available for this project, solving the compressibility issue was

necessary. A two stage pressure regulation mechanism was tested and was found to be

reasonably successful.

The two stage pressure regulation mechanism helped quickly reach and maintain

constant injection rate. Figure 5-12 shows a photograph of this pressure regulating unit

which consists of a modified safety valve and pressure reducing regulator. The

components of this unit are described in more detail in Section 5.5.2. It maintained the

injection liquids compressed above 520 psi behind the modified safety valve and

eliminates the long time required to stabilise injection rate. Downstream of the pressure

reducing regulator, the pressure reducing regulator with the metering valve maintain

constant injection rate with variable injection pressure that depends only on the

permeability of the sand pack. The pressure is equal to zero for a zero flow rate and


169

increases as the flow rate is increased. Figure 5-13 shows a successful experiment

where the injection rate was well-controlled with a target flow rate of 0.07 mL/min.

Compressibility Delay on Pump and Injection Pressur e Response for a Target Injection Rate of 0.07 mL/min and actual Flo w Rate

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0 0.3 0.6 0.9 1.2 1.5Pore Volumes of Injection

qt (

cm3 /m

in)

0

10

20

30

40

50

60

70

80

Inje

ctio

n P

ress

ure

(psi

)

Pum

p P

ress

ure

(x10

psi

)

qt Injection Pressure Pump Pressure

Figure 5-11: An ill-controlled water flood of secondary recovery in the trial floods.

Figure 5-12: Images shows the configuration of the two stage pressure regulation.

Modified Safety valve (First Pressure Regulation stage)

Pressure Reducing Regulator (Second Pressure Regulation stage)

Metering Valve

Injection Teflon Plug

Pressure Transducer

Injection tubing

Sand Pack Fixing cords Wooden base


170

The oil injection was also maintained at 0.07 mL/min. A back pressure regulator was

used to avoid contaminating the pressure reducing regulator and the modified safety

valve with oil. This back pressure regulator was set to 120 psi and only used during oil

injection. The contamination could affect the performance of the two stage pressure

regulating unit during water flooding, ASP flooding and water drive. Before the oil

injection was started, the pressure regulating unit (pressure reducing regulator, modified

safety valve and metering valve) was removed and the back pressure regulator was

installed. The pressure regulating unit was only used with water saturation, water flood,

ASP flood and water drive. In each removal or installation, air was removed from the

regulators and valves before commencement of injection.

Compressibility Issue Resolved: Pump and Injection Pressure Reponses for a Target Injection Rate of 0.07 mL/min and actu al Flow Rate

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 0.3 0.6 0.9 1.2 1.5Pore Volumes of Injection

qt (

cm3 /m

in)

0

10

20

30

40

50

Inje

ctio

n P

ress

ure

(psi

)

P

ump

Pre

ssur

e (x

10 p

si)

qt Pump Pressure Injection Pressure

Figure 5-13: Well-controlled water flood for secondary oil recovery. Note that the pump pressure is set to about 520 psi.

5.6.7 Flow Impairment in the Sand Packs

Flow impairment was observed in several sand packs after switching the flood from

ASP injection to water injection (water drive). Figure 5-14 shows the pressure response

to ASP flood and water drive in SP11. The injection pressure initially increased then

started to drop after the oil bank and emulsion break through. Suddenly, it started to

build up again after injecting about one pore volume of drive water. The pressure


171

continued to build up until the sand pack glass tube was destroyed. Consequently, the

injection pressure was not allowed to go beyond 190 psi in the well-controlled sand

packs as a measure to protect the glass tubes. Since the flow impairment event took

place after the oil bank and emulsion came out of the sand pack, the emulsion was not

suspected to be the cause of this impairment. More detailed investigation on the flow

impairment is given in Chapter 6.

ASP flood Pressure Response of SP11

0

50

100

150

200

250

300

350

400

0 0.5 1 1.5 2 2.5 3Pore Volumes of Injection

Pre

ssur

e (p

si)

Pump Injection

Switch to water drive

glass tube fractureat 384 psi

Flow impairment onset

Oil bank and emulsion breakthrough

Figure 5-14: A trial sand pack (SP11) suffered from flow impairment after switching from ASP injection to water drive. The glass was broken because this pressure build up was not expected and no pressure protection was in place at that time. Injection pressure transducer reached its upper limit (blue line), approximate pressure reading could be taken from pump pressure (pink line).

5.6.8 Injection System Performance During Flow Impairment

The pressure regulating mechanism, as discussed earlier in Sections 5.6.6 and 5.5.2,

was used to eliminate the liquids compressibility and quickly reach and maintain a

constant injection rate. It also was used to set a maximum limit to the allowable

injection pressure to protect the glass tubes from excessive high pressure. The

maximum allowable injection pressure was set to 190 psi for the six well-controlled

sand pack floods. When the flow in the sand pack is impaired, the injection pressure

approached the maximum allowed pressure (190 psi) and the reducing pressure


172

regulator takes action by chocking the flow and the upstream pressure increases. Figure

5-15 shows the pressure evolution in an impaired sand pack. Some flow continues to go

through the impaired sand pack corresponding to the maximum pressure allowed by the

pressure regulator. As a result, the pressure upstream of the pressure regulator may

continue to build up until it reaches a plateau. It remains at the plateau value of 190 psi

as long as the flow in the sand pack remains impaired.

Change in Flow Rate Due to Sand Pack Flow Impairmen t when Injection is Switched from ASP to DW

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07


qt (

cm3 /m

in)

0

50

100

150

200

250

300

350

Inje

ctio

n P

ress

ure

(psi

)

P

ump

Pre

ssur

e (x

10 p

si)

Flow Rate Injection Pressure Pump Pressure

ASP Waterslug drive

Figure 5-15: The two stage pressure regulation reaction to flow when the flow is impaired by sand pack heterogeneity action on ASP flood and water drive. Note that the pressure regulator is set to maintain pump pressure at 520 psi and constant flow rate. The safety valve of the pump (it is different to the safety valve within the pressure

regulating stages) was part of the original setup and was set to 3000 psi (Figure 5-7 and

Figure 5-12). When the upstream pressure is close to this value some of the hydraulic

fluid (water) will drop out through this valve. This partially diverts some of the flow

from the impaired sand pack to the atmosphere and thus protecting the sand pack glass

from breaking while the injection pressure is still purely responsive to the flow

impairment. If the impairment eases then the flow rate will increase and the injection

pressure will drop. Therefore, the flow impairment and injection pressure response

occurs purely due to the permeability changes inside the sand pack and are independent

of the pressure regulating mechanism.


173

5.7 Constant Phase Behaviour

The phase behaviour of emulsion and its importance has been discussed in Chapter 2.

The phase behaviour is expected to change due to chromatographic separation but also

as the chemical slug progresses through the porous medium (Austad and Strand, 1996).

In this PhD work, it was important that the phase behaviour be kept the same in all ASP

floods to equalise the impact of phase behaviour changes on oil recovery. Unpredicted

changes in phase behaviour could affect the amount of oil recovered by the ASP flood

and undermine the experimental efforts to keep all variables of ASP process constant,

except for the heterogeneity. Injecting the ASP slug at the optimum salinity is desired

for maximum oil recovery (Nelson, and Pope, 1978). In this study, phase behaviour

scans were conducted to find the optimum salinity of Oil 3/Alfoterra 145-S4 system as

has been reported in Chapter 4. The system showed sudden transition from phase –II to

phase +II as salinity was increased and thus was not possible to find the optimum

salinity to get the system into phase III.

Injecting ASP flood at phase behaviour -II or +II may not recover the maximum

possible oil compared to the optimum phase III, however, it still can recover

significantly more oil than would be recovered using just water flood (Taugbøl, Ly, and

Austad, 1995). Perhaps, the most direct way to obtain constant phase behaviour is to

design a system that is either well above or well below the optimum salinity. The

system could be kept at +II phase using higher salinities of NaCl. The system, could

also, be kept at phase –II when no NaCl is added. Higher surfactant concentration may

also place the system at phase +II. Thus, to keep the phase behaviour constant, the

system either could be placed at phase +II by adding NaCl or could be kept at phase –II

by adding no NaCl. The phase –II is easier to achieve and sustain throughout the flood

than phase +II because there was no need to add NaCl to the ASP slug or to the water

used to saturate sand packs. Therefore, phase behaviour –II was chosen and the sand

packs were saturated with DW to ensure that that the phase behaviour remained in the –

II phase behaviour.

The phase behaviour type of the emulsion produced in the ASP floods was tested after

the floods to ensure it was indeed phase -II using NMR-PFG-STE and electrical

conductivity described in Chapter 4 and Chapter 6. Both electrical resistance and NMR-

PFG-STE confirmed that the emulsion is oil-in-water in all the six ASP floods.


174

5.7.1 Chemical Slug and Sand Stability

The ASP slugs for all the sand pack floods should be stable and have the same

composition so as to be able to interpret the results and relate any changes solely to the

heterogeneity. The stability of the ASP chemicals is discussed here.

Polymer HPAM does suffer from degradation by hydrolysis and this is enhanced in a high pH

environment (Sorbie, 1991). Note that high pH is one of the characteristics of the ASP

process. According to Levitt et al. (2011), the hydrolysis can change the viscosity and

thus the viscosity could be as a rough indicator of hydrolysis. It is notable in the work of

Levitt that the hydrolysis rate observed was initially fast then becomes slower with time.

Consequently, in this work of ASP experimental floods, caution was taken to inject an

ASP slug of same age in all runs. This aimed to ensure the polymer had a similar degree

of hydrolysis in all runs. In addition, ASP slug viscosity at the start of each run was

measured and if any major viscosity changes were observed a new slug was prepared.

Table 5-1 shows that in all the runs the slugs had similar viscosities and thus it was

likely all had similar degree of hydrolysis at flooding commencement.

Surfactant Sulphate surfactants are chemically stable at room to high moderate temperatures,

though, specific conditions/variables such as pH may affect this stability (Tally, 1988).

Based on Tally’s work alkyl sulphate surfactants have a decomposition half life of

roughly 80 years at pH=11 and temperature of about 27 oC. Note that Tally mainly

studied ethoxy sulphate while the surfactant that was used in this PhD research is a

propoxy sulphate. These two are similar in structure, ethoxy groups are more

hydrophilic while the propoxy more lipophilic but with similar structure. Therefore, it is

reasonable to assume it will be stable during the experiments at laboratory conditions.

This was evidenced in the fact that a stable emulsion was produced in the ASP floods in

this work and this is a strong indication on the surfactant action and thus its stability for

the experiment duration. Note that all the ASP slugs were made about 24 hours before

each run, if there were some surfactant degradation, it would be the same for all runs.

Alkali and Silica Dissolution The sands used in this research were both silica sands (quartz) which can dissolute

under alkaline conditions. The ASP flooding experiments were conducted at high pH,

thus, silica dissolution should be mentioned and addressed. The report of Saneie and

Yortsos (1993) reflects the importance of the silica dissolution in high temperature and


175

high pH alkaline flooding. In one hand, Kennedy (1950) established that below 150 oC

the solubility of silica is very low and thus essentially insoluble in neutral water. On the

other hand, Alexander et al. (1954) showed that at high pH the solubility increases to

significant values, for example, at pH of 11 the solubility of silica is just below 0.5%.

However, this solubility was measured after 6 months and three weeks of equilibration.

The ASP slug did have a high pH value of about 12.5, but since the exposure of the

sand to the ASP slug in the sand packs floods was only for less than a day and the

temperature was at room temperature it was safe to ignore this dissolution. The work of

Alexander et al. (1954) was based on amorphous silica and the silica sand used in this

PhD project was crystalline silica (quartz) which should be more resistant to dissolution

(Siever, 1962). In addition, in any case of dissolution activities in the sand packs, it

should take place roughly equally in the heterogeneous sand packs (Pair 2 and Pair 3)

because these packs were identical in terms of the sand amount and type. In the case of

Pair 1, there was a larger amount of the -75 µm sand in the sand mixture of SP15

whereas SP18 had only the -300 µm sand, therefore, SP15 may have slightly higher

silica dissolution because of the smaller grains which are easier to dissolve. Only Pair 2

and Pair 3 were used for heterogeneity impact comparison.

5.8 Results and Discussion

The results of the water and ASP flooding experiments: oil recovery profiles, injection

pressure responses, ASP chemical production in produced water are reported below in

graphical and tabular formats.

5.8.1 Sand Pack Permeabilities and Porosity Repeatability Quality

Table 5-2 demonstrates that the porosities, mass gradient and permeabilities of the sand

packs pairs are reasonably close. These permeabilities were determined during the water

saturation stage, the oil saturation stage, the water flooding stage and were calculated

based on simple application of Darcy’s Law when steady flow conditions were reached.

More detailed calculations and data are shown in Table 5-5 and Table 5-7 at the end of

this chapter. The reported permeabilities are: absolute permeability Ka, the effective

permeability to oil at irreducible water saturation (end effective oil permeability, Keeo),

effective permeability to water at residual oil saturation (end effective water


176

permeability, Keeo). All measured at injection rate of 0.07 mL/min in vertical

configuration of the sand packs.

Note that the absolute permeabilities of SP17 and SP18 are less than the effective

permeabilities to oil. The absolute permeability should be bigger than the end effective

permeability of oil or water. The injection rate was quite low (0.07 mL/min) and the

water head hydraulic pressure in these sand packs was found to be about 2.3 psi (this

consist of ~155 cm sand pack height and tubing above the pressure transducer in

addition to the tubing to the fraction collector with a net vertical height of about 10 cm).

Therefore, the resulted injection pressure corresponding to this low injection rate in the

sand packs may not be big enough to overcome capillary end effects especially when

the injected liquid had a low viscosity. The measured pressure at the transducer pressure

may include the capillary end effects. Capillary end effects are encountered when the

flow rate is low and usually overcome by injecting at higher rates (Tiab and Donaldson,

2004). As a result, the absolute permeabilities determined in Table 5-2 are not reliable.

However, in the case of Oil 3 which has a viscosity of 20-30 times higher than the

viscosity of water, thus, the resultant pressure of injecting oil at constant rate was big

enough to screen capillary effects and thus was more reliable to determine the

permeability. Therefore, the effective oil permeability is used as the reference

permeability and is believed to have closer value to the absolute permeability of the

sand packs than those found by water injection. Table 5-2 shows that the heterogeneous

sand pack pairs have reasonably close values of effective permeability to oil. Note that

the sand packs with lower-to-higher permeability configuration (SP17 and SP23)

showed similar values of effective permeabilities to water and values of about twice of

the higher-to-lower permeability transition sand packs (SP16 and SP19).

Table 5-2: Porosities, mass gradients and Permeabilities of Sand Packs

Sand Pack

Permeability Configuration

Porosity (Fraction)

Mass Gradient of sand inside Sand Pack

(g/cm)

Ka (D) Keew

(D) Keeo (D)

Length of

lower K section

(cm)

Length of

higher K section

(cm)

15 L 0.344 1.39 1.527 0.222 0.986 147.4

18 H 0.373 1.33 1.277 0.147 6.096 147.3

19 H-L 0.373 1.34 2.436 0.298 2.270 73.8 73.8 23 L-H 0.372 1.33 3.669 0.474 2.631 73.4 73.9

16 H-L 0.369 1.33 5.72 0.282 2.045 73.7 73.8

17 L-H 0.365 1.34 1.355 0.426 1.754 73.8 73.9


177

5.8.2 Water Density Influence on Oil Recovery Calculations

The water retained in the sand packs after the water drive in the ASP floods could have

a density between that of DW and that of ASP slug. The density of water inside the sand

pack is known for the water floods, thus, only one recovery value is obtained. In the

case of the ASP floods, the density of the water inside the sand pack remains between

that of the ASP slug and that of the DW and its exact value is not known.

In all sand packs, most of the ASP slug was produced out of the sand pack before the

2PV injection of the water drive was stopped, except for SP19 where significant amount

of ASP slug was produced and some ASP slug was still retained inside the sand pack.

This is evidenced in the graphs from Figure 5-21 to Figure 5-26, which show the

concentration of the ASP components in the produced water.

When lower water densities are used to calculate saturations (and recoveries) using

Equations 5-1 and 5-2, higher oil recoveries are obtained. Therefore, these equations

will produce a range of oil recoveries with maximum EOR when the DW density is

used in the equations and minimum EOR when ASP density is used. Since most or a

significant amount of the ASP slug was produced out of the sand packs it is more likely

that the water density inside the sand packs is close to that of the DW. However, as a

conservative measure a density value between both the densities of the DW and ASP

was used in the calculations.

5.8.3 Oil Recovery

Results of oil recovery in the secondary and EOR stages are summarised in Table 5-3.

The average difference in incremental recovery between lower-to-higher and higher-to-

lower is slightly more than 5% OOIP. The recovery table shows that the process is more

efficient when the ASP flow direction is from lower-to-higher permeability transition.

This experimental result is based on a 1D physical model. Such a conclusion may not be

appropriate for 3D physical models. However, one would expect a similar physical

behaviour of the emulsion flow in 3D setup (reservoir or core).

Table 5-3 includes the calculation of the residual saturations after the secondary and

EOR floods were applied based on Equations 5-1 and 5-2. Details on the masses of the


178

sand packs before and after each flooding stages are provided in Table 5-5 at the end of

this chapter. The calculations of the residual oil saturations are based on water density

of 1.000 g/mL, ASP density of 1.005 g/mL and Oil 3 density of 0.856 g/mL. The

average EOR recovery is reported based on the average between the minimum and

maximum possible EOR which corresponds to the average density between ASP slug

and DW densities, thus, the reported EOR results are conservative. The possible values

of maximum and minimum oil recoveries are reported in Table 5-6.

Table 5-3: Secondary oil recovery and ASP EOR results of the Sand Packs. Note: Polymer 3430S was used in the ASP slug of SP19 and SP23 pair While Polymer 3630S was used in SP17 and SP16 pair and the homogenous pair.

Sand Pack


Porosity (Fraction) Keeo (D)

Secondary Recovery (%OOIP)

Average EOR

Incremental (%OOIP)

Average EOR

Recovery (%OOIP)

23 Low-to-High 0.372 2.631 76.6 18.8 95.4 19 High-to-Low 0.373 2.270 66.1 13.4 79.5 18 High 0.373 6.096 69.0 13.0 82.0 15 Low 0.344 0.986 53.1 32.9 86.0 17 Low-to-High 0.365 1.754 69.2 26.6 95.8 16 High-to-Low 0.369 2.045 67.8 20.7 88.5

The secondary oil recoveries were high in all sand packs because of the high

permeabilities of the sand packs and their narrow cross-section which confined the

flooding front. SP15 showed the lowest secondary recovery probably because of its

relatively lower permeability. Its entire length is made of lower permeability sand. It

would thus have more ability to trap oil than other sand packs. This trapped oil in SP15

was easily removed by the ASP flood. The resulted oil recovery from this sand pack

was the highest incremental EOR compared to other sand packs. Table 5-3 shows that

the heterogeneity configuration of lower-to-higher permeability transition had

advantage over the higher-to-lower permeability transition in terms of the ultimate oil

recovery. This probably because the higher-to-lower permeability sand packs showed

earlier flow impairment compared to lower-to-higher sand packs. The impairment

would slow the flow and reduced the ability of the ASP slug to mobilise the trapped oil.

As a result, the oil recovery will be slightly different for the two flooding direction.

Since lower-to-higher sand packs showed the incident of flow impairment at latter times,

the ASP slug was able to recover more oil in these packs than the higher-to-lower packs.


179

The reasons for the difference of flow impairment occurrence between the different

permeability transitions are investigated in Chapter 6.

5.8.4 Emulsion Production

The sand packs produced slightly different amounts of emulsion as it can be seen in

Table 5-4. This table shows that the higher-to-lower sand packs (SP19 and SP16)

produced slightly larger amounts of emulsion compared to SP16. It is not known how

much oil is present in these emulsions. The digitals images of SP17’s vials of the ASP

flooding were mislaid, thus, it was not possible to calculate the amount of emulsion

produced from SP17 sand pack.

Table 5-4: Amounts of emulsion produced in ASP floods of the Sand Packs.

Sand Pack


Emulsion Volume (mL)

Emulsion Volume (PV)

23 Low-to-High 2.8 0.07 19 High-to-Low 3.9 0.10 18 High 2.7 0.07 15 Low 3.1 0.08 17 Low-to-High N.D. N.D. 16 High-to-Low 4.3 0.11

5.8.5 Phase Behaviour of Emulsion in ASP Floods

The phase behaviour of the produced emulsion was found by electrical conductivity as

described in Chapter 4. All the ASP floods in the six well-controlled sand packs

experiments produced lower Winsor phase behaviour. Therefore, the observed

variations in the results of oil recovery are confirmed not due to changes in the phase

behaviour.


180

5.8.6 Production Rate and Oil Cut

The flow rates, the recovery profile, oil cut and pressure responses are shown in Figure

5-18 through Figure 5-20. These figures display the total production rate (qt), the oil

cut, the cumulative oil recovery and the pressure response of ASP flood and water drive,

all against the total injection. These figures collectively demonstrate that lower-to-

higher permeability transition has less impact on flow rate and injection pressure. It was

assumed that the emulsion contained 50% and 50% water in the calculations of

cumulative oil recovery curve. Because the exact amount of water/oil portions in the

emulsion is not known, the ultimate recovery is based on the aforementioned mass

measurements. The cumulative recovery curves were corrected to the ultimate EOR

values in Table 5-3 which was based on the mass measurements. Unfortunately, the

images of samples of the EOR recovery of SP17 were lost and could not be retrieved

from the camera memory card. As a result, the EOR recovery profile of ASP17 could

not be provided. However, the water flood and ASP floods recovery results of SP17 can

be found in Table 5-3 above, as well as Table 5-5 and Table 5-6 at the end of this

chapter.

Sand Pack 15 Floods- Homogenous Lower Permeability

020406080

100120140160180200220

0 1 2 3 4 5 6

Pore Volumes of Injection

unit

in L

egen

d

0.000.01

0.020.030.040.05

0.060.070.08

0.090.10

qt (

mL/

min

)

Pinj(psi) Oil Recovery %OOIP Oil Cut %(v/v) qt

ASP Water Drive Water Flood

Figure 5-16: SP15 flooding results, which should be compare to its pair SP18.


181

Sand Pack 18 Floods- Homogeneous Higher Permeabilit y

0

20

40

60

80

100

0 1 2 3 4 5 6Pore Volumes of Injection

units

in L

egen

d

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

qt (

mL/

min

)

Pinj(psi) Oil Recovery (%OOIP) Oil Cut%(v/v) qt


Figure 5-17: SP18 flooding results, which should be compared to SP15. Note there is no flow impairment in the ASP flood of SP18.

Sand Pack 16 Floods- Heterogeneous/ Higher-to-Lower Permeability

0

20

40

60

80

100

120

140

160

180

200


units

in L

egen

d

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09qt

(m

L/m

in)

Pinj(psi) Oil Recovery (%OOIP) Oil Cut (%v/v) qt


Figure 5-18: SP16 flooding results, which should be compared to results of SP17, but the profiles of SP17 were not obtainable. This SP16 behaves same like SP19, higher-to-lower permeability transition.


182

Sand Pack 19 Floods- Heterogeneous/ Higher-to-Lower Permeability

0

20

40

60

80

100

120140

160

180

200


units

in L

egen

d

0.00

0.01

0.02

0.03

0.04

0.05

0.060.07

0.08

0.09

0.10

qt (

mL/

min

)

Pinj(psi) Oil Recovery % (OOIP) Oil Cut % (v/v) qt


Figure 5-19: SP19 flooding results, which should be compared to SP23.

Sand Pack 23 Floods- Heterogeneous/ Lower-to-Higher Permeability

0

20

40

60

80

100

120


units

in L

egen

d

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

qt (

mL/

min

)

Pinj(psi) Oil Recovery % (OOIP) Oil Cut (% wt/v) qt


Figure 5-20: SP23 flooding results, which should be compared to SP19.


183

5.8.7 Chemical Profile of the ASP Components in the Produced Water The details of the methods used for the chemical analysis of the ASP components are

compiled in Chapter 3. Chemical profiles of ASP flood effluent help to visualise the

concentration of ASP chemicals in the produced water. It shows how far the peak of

each chemical lags behind the peaks of other chemicals. It also demonstrates how much

of the slug was produced. A more successful ASP flood will produce profiles with their

peaks close to each other, that to say the flood slug suffered less chromatographic

separation. The profiles from the six sand packs are displayed in Figure 5-21 through to

Figure 5-26. The ASP was injected as a pulse for the duration of 0.4PV of injection

followed by 2PV of water drive injection. The feed concentrations were 1550 ppm for

the polymer, 1 %( w/v) surfactant (active based) and 0.5% (w/v) of NaOH.

In all the runs, the polymer leads the surfactant and the hydroxide. The polymer is

followed by the surfactant and the hydroxide comes last. The sand packs with higher-to-

lower permeability transitions and the homogenous low permeability sand pack (SP15)

appear to retain some of the chemicals. The profiles of the chemicals show that more of

the ASP could be produced if the flow was not severely impaired.

Sand Pack 15 ASP Flood (Homogenous Low Permeability ): Chemical Profile of Produced Water

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5


NaO

H o

r S

urfa

ctan

t C

once

ntra

tion

(% w

/v)

0

100

200

300

400

500

600

700

800

Pol

ymer

Con

crnt

ratio

n (p

pm)

Surfactant Alkali Polymer

ASP Slug

Water Drive

Figure 5-21: Concentrations of polymer, surfactant and NaOH in the produced water in SP15. Most of the polymer and NaOH were produced out, while the surfactant was retained. Liquids collection started after the start of ASP injection as showed by the dashed line in Figure 3-16.


184

Sand Pack 16 ASP Flood (Higher-to-Lower Permeabilit y): Chemical Profile of Produced Water

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 0.5 1 1.5 2 2.5


NaO

H o

r S

urfa

ctan

t C

once

ntra

tion

(% w

/v)

0

200

400

600

800

1000

1200

1400

Pol

ymer

Con

crnt

ratio

n (p

pm)

Alkali Surfactant Polymer

ASPSlug

Water Drive

Figure 5-22: Concentrations of polymer, surfactant and NaOH in the produced water in SP16. Liquids collection started after the start of ASP injection as showed by the dashed line in Figure 5-18.

Sand Pack 17 ASP Flood ( Lower-to-Higher Permeabili ty ): Chemical Profile of Produced Water

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 0.5 1 1.5 2 2.5


NaO

H o

r S

urfa

ctan

t C

once

ntra

tion

(% w

/v)

0

100

200

300

400

500

600

700

Pol

ymer

Con

crnt

ratio

n (p

pm)


ASP Slug

Water Drive

Figure 5-23: Concentrations of polymer, surfactant and NaOH in the produced water in SP17.


185

Sand Pack 18 ASP Flood (Homogenous High Permeabilit y): Chemical Profile of Produced Water

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0 0.5 1 1.5 2 2.5


NaO

H o

r S

urfa

ctan

t C

once

ntra

tion

(% w

/v)

0

100

200

300

400

500

600

Pol

ymer

Con

crnt

ratio

n (p

pm)

surfactant Alkali Polymer

ASP Slug

Water Drive


Sand Pack 19 ASP Flood (Higher-to-Lower Permeabilit y): Chemical Profile of Produced Water

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0 0.5 1 1.5 2 2.5


NaO

H o

r S

urfa

ctan

t C

once

ntra

tion

(% w

/v)

0

200

400

600

800

1000

1200P

olym

er C

oncr

ntra

tion

(ppm

)

surfactant Alkali Polymer

ASP Slug

Water Drive



186

Sand Pack 23 ASP Flood (Lower-to-Higher Permeabilit y): Chemical Profile of Produced Water

0.0

0.5

1.0

1.5

2.0

2.5

0 0.5 1 1.5 2 2.5


NaO

H o

r S

urfa

ctan

t C

once

ntra

tion

(% w

/v)

0

100

200

300

400

500

600

Pol

ymer

Con

crnt

ratio

n (p

pm)


ASP Slug

Water Drive


5.8.8 Injection Pressure Responses to ASP Flood

The injection pressure was recorded in all runs. The following graphs (Figure 5-27 to

Figure 5-30) show the injection pressure response in the sand packs pairs to the ASP

injection and the water drive. These graphs help to show the impact of heterogeneity on

emulsion flow and oil recovery. The plateau in the pressure responses have been

explained in Section 5.6.8. The high permeability homogenous sand pack did not show

severe pressure changes but the remaining sand packs did.

The lower-to-higher permeability transition delayed the increase of pressure loner than

higher-to-lower, thus, the former is more desired to minimise the heterogeneity impact

on the pressure response. To explain the pressure behaviour observed, further

experiments were conducted. These experiments are discussed and reported in the next

chapter (Chapter 6).


187

Injection Pressure Response of the ASP Floods in th e Sand Packs

020

40

60

80100

120

140160

180

200

0 0.5 1 1.5 2 2.5


SP

15,

SP

16,S

P17

and

S

P19

Inj

ectio

n P

ress

ure

(psi

)

0

10

20

30

40

50

60

70

SP

18 a

nd S

P23

In

ject

ion

Pre

ssur

e (p

si )

SP15:L SP16:H-L SP17:L-H SP19:H-L SP18:H SP23:L-H

ASP Waterslug drive

Figure 5-27: Pressure Responses of all ASP floods for comparison. Note that SP18 and SP23 are plotted on the Pressure axis on the right side of the graph for better scale resolution.

Injection Pressure Response of the ASP Floods in th e Sand Packs 15 and 18 :Homogenous Cases

0

20

40

60

80

100

120

140

160

180

200

0 0.5 1 1.5 2 2.5


SP

15 I

njec

tion

Pre

ssur

e (p

si )

0

5

10

15

20

25S

P18

In

ject

ion

Pre

ssur

e (p

si )

SP15 SP18

SP 15: Homogenous: Lower permeability

SP 18: Homogenous: Higher permeability

ASP Waterslug drive

Figure 5-28: Injection pressure response of the ASP floods in homogenous cases of SP15 and SP18. Note the pressure dip at PV~ 0.4 at which switch to water drive occurred.


188

Injection Pressure Response of the ASP Floods in th e Sand Packs 16 and 17: Heterogeneous cases

020406080

100120140160180200

0 0.5 1 1.5 2 2.5


Inje

ctio

n P

ress

ure

(psi

)

SP16 SP17

SP 16: Heterogeneous: High-to-low permeability

SP 17: Heterogeneous: Low-to-high

Figure 5-29: Injection pressure response of the ASP floods in heterogeneous cases of SP16 and SP17. Note the pressure dip at PV~ 0.4 at which switch to water drive occurs. The lower-to-higher case showed less pressure build up and higher EOR. The polymer used in the ASP is 3630 S, it has higher molecular weight than 3430 S.

Injection Pressure Resposne of the ASP Floods in th e Sand Packs 19 and 23: Heterogeneous cases

0

20

4060

80

100

120140

160

180

200

0 0.5 1 1.5 2 2.5


SP

19 I

njec

tion

Pre

ssur

e (p

si )

0

10

20

30

40

50

60

70

SP

23 I

njec

tion

Pre

ssur

e (p

si )

SP19 SP23

SP 19: Heterogeneous: Hgh-to-low permeability

SP 23: Heterogeneous: Low-to-high permeability

Figure 5-30: Injection pressure response of the ASP floods in heterogeneous cases of SP19 and SP23. Note the pressure dip at PV~ 0.4 at which switch to water drive occurs. The lower-to-higher case showed less pressure build up and higher EOR. The polymer in the ASP is 3430 S, lower molecular weight than 3630 S.


189

5.8.9 Colouration of Sampled ASP Effluents

The samples obtained from all runs in the ASP flooding showed brownish colouration.

In contrast, the samples obtained from the water flooding were not coloured. Figure

5-10 in page 167 shows the sudden colour development in the collected samples

indicating the ASP slug arrival. The ASP slug was originally a colourless aqueous

solution. It was a concern that this colouration may point to chemical reaction that

could reduce the efficiency of the ASP slug. Two experimental investigations were

performed to address this concern. In the first investigation, a pulse of ASP slug was

injected in a blank sand pack that was initially saturated with water. The slug was

pushed with water. It was easy to observe the colouration through the wall of the sand

pack glass tube. When the coloured front reached close to the end of the sand pack,

sampling in 3.5 mL glass vials was started. It is emphasised here that this sand pack did

not have any oil prior to the injection of this ASP slug, the colouration varied from

darkest in the front and gradually became colourless towards the end of the flood as

shown in Figure 5-31. Since it is only the samples produced in the ASP flooding are

coloured and no oil was in the sand pack then it is confirmed that the ASP chemicals are

responsible for the colouration. The darkness of the samples was gradual and the

darkest one was the second sample from the left in Figure 5-31. This indicates that the

front cleaned the sand grains and picked some coloured matter. By the time when the

rear of the ASP slug was arrived it did not find more of this matter and remained

colourless. Interestingly flow impairment is also observed here but no record of pressure

was taken by the gradual decrease of collected water. These samples though could not

be used for flow impairment study because at that time the compressibility of the

injection system was not resolved and the pressure regulating mechanism was not in use.

The second investigation aimed to identify which component of the ASP slug was

responsible for this colouration. Another blank sand pack was flushed with separate

solutions of the chemicals of the ASP slug. It was found that the NaOH solution

produced a slightly brownish aqueous phase. The flushes of the polymer and the

surfactant solutions produced colourless aqueous solutions. Furthermore, the

combination of the surfactant and the NaOH enhanced the colouration to a darker brown

colour more than the NaOH solution alone. This reflects that the combination of the

NaOH and surfactant are more effective in picking the coloured matter. The nature of

the chemical compound responsible to this colour change is not known. The formation


190

of trivalent compounds of iron with the hydroxide is suspected especially as ICP-AES

analysis showed that there was some low amount of iron (Table 9-5 in Appendix A3).

Small amount of silicon was also detected in the coloured samples which could be

indicative of silicate compounds from caustic dissolution of the sands. No further

investigation was done to ascertain the responsible compound for this colouration, as

such study is beyond the scope of this research.

Despite the fact that the colouration may indicate some chemical consumption, the ASP

slug remained effective as evidenced by the relatively high oil recoveries and the

formation of emulsions.

Figure 5-31: Coloured effluent from injecting ASP slug in a blank sand pack, it is emphasised here that there was no oil in the sand pack. It also show gradual decrease in the collected water because of the flow impairment discussed earlier.

5.9 Conclusion

The impact of several factors affecting the ASP process such as oil type, slug size and

composition, flow inclination, injection rate and phase behaviour were kept constant in

the ASP floods and only the heterogeneity was varied. The results confirmed that

longitudinal heterogeneity in terms of the permeability variations has a significant

impact on the ASP process. The longitudinal heterogeneity in the present experiments

has more negative impact on the efficiency ASP flooding process (in terms of oil

recovery and injection pressure response) when the permeability trend is decreasing

along the flow path of the ASP slug than the case when the permeability trend is

increasing.

There is a preferential flow direction with respect to the longitudinal heterogeneity

configuration in which the oil recovery is maximised. Based on the results from sand

pack experiments, injecting from a lower permeability zone to a higher one, increased


191

the oil recovery of the ASP process by a margin of ~5% OOIP relative to injecting from

higher- to-lower permeability zone.

All the sand packs suffered from some degree of flow impairment except for the high

permeability sand pack. The impairment occurred at the later stage of the experiment

after the ASP slug reached the end of the sand pack and all the oil bank and emulsion

were produced. Therefore, the ASP slug components were not the cause for direct

physical plugging and flow impairment. More investigation on the flow impairment is

reported in Chapter 6.

The higher-to-lower permeability transition sand packs showed earlier flow impairment

compared to the higher-to-lower permeability transition sand packs. The early

occurrence of the flow impairment during the ASP floods in the higher-to-lower

heterogeneous sand packs increased the injection pressure and reduced the flow

markedly compared to the lower-to-higher sand packs. As result, the ultimate recovery

from lower-to-higher sand packs was higher than those of higher-to-lower permeability

transition.

These conclusions obtained in this study are valid for 1D physical model sand packs

with ASP flood followed by water drive. The cases of 3D physical models of ASP flood

followed by polymer drive or water drive were not investigated.

Ch

ap

ter

5:

AS

P F

loo

ds

In H

om

og

en

ou

s a

nd

Hete

rog

en

eo

us

Sa

nd

Pa

cks

192

Tab

le 5

-5: S

and

pack

s m

ass

es

befo

re a

nd a

fter

diffe

rent

flo

od

ing

sta

ges

San

d P

ack

Per

mea

bilit

y C

onfig

urat

ion

Dry

Mas

s (g

)

Wat

er

Sat

urat

ed

Mas

s

(g

)

Mas

s af

ter

Sat

urat

ing

with

Oil

(g)

Mas

s A

fter

S

econ

dary

W

ater

F

lood

ing

(g

)

Mas

s af

ter

AS

P

Flo

od

(g

)

Soi

(fra

ctio

n)

Sor

(f

ract

ion)

Sor

EO

R

(fra

ctio

n)

(ρw =

1.00

0 g/

cm3 )

Sor

EO

R

(fra

ctio

n)

(ρw =

1.00

5 g/

cm3 )

23

L-H

54

0.08

3 58

1.19

3 57

7.19

2 58

0.28

58

1.11

3 0.

671

0.15

7 0.

014

0.04

7 19

H

-L

542.

659

583.

976

579.

662

582.

544

583.

198

0.72

1 0.

245

0.13

3 0.

163

18

H

540.

642

581.

897

577.

407

580.

534

581.

194

0.75

2 0.

233

0.12

0 0.

150

17

L-H

54

3.59

58

4.11

6 58

0.10

1 58

2.90

8 58

4.04

7 0.

683

0.21

0 0.

012

0.04

6 16

H

-L

541.

439

582.

356

578.

299

581.

081

581.

994

0.68

3 0.

220

0.06

2 0.

094

15

L 55

0.09

2 58

8.20

3 58

5.41

4 58

6.93

9 58

7.91

3 0.

500

0.23

4 0.

054

0.08

6

Tab

le 5

-6:

Oil

reco

very

ca

lcu

latio

ns b

ase

d o

n T

able

5-

5 an

d th

e le

ngth

s o

f sa

nd p

acks

sec

tions

San

d P

ack

Per

mea

bilit

y C

onfig

urat

ion

Sec

onda

ry

Rec

over

y (%

OO

IP)

EO

R

Rec

over

y (M

axim

um)

(%O

OIP

)

EO

R

Rec

over

y (M

inim

um)

(%O

OIP

)

Max

EO

R

Incr

emen

tal

(%O

OIP

)

Min

EO

R

Incr

emen

tal

(%O

OIP

)

Ave

rage

E

OR

In

crem

enta

l (%

OO

IP)

Ave

rage

E

OR

R

ecov

ery

(%

OO

IP)

Leng

th o

f Low

er

Per

mea

bilit

y se

ctio

n

(c

m)

Leng

th o

f Hig

her

Per

mea

bilit

y se

ctio

n

(c

m)

23

L-H

76

.6

98.0

92

.9

21.3

16

.3

18.8

95

.4

73.4

73

.9

19

H-L

66

.1

81.6

77

.4

15.5

11

.4

13.4

79

.5

73.8

73

.8

18

H

69.0

84

.0

80.0

15

.0

11.0

13

.0

82.0

14

7.3

17

L-H

69

.2

98.2

93

.3

29.1

24

.1

26.6

95

.8

73.8

73

.9

16

H-L

67

.8

90.9

86

.2

23.0

18

.4

20.7

88

.5

73.7

73

.8

15

L 53

.1

89.2

82

.8

36.2

29

.7

32.9

86

.0

147.

4

Ch

ap

ter

5:

AS

P F

loo

ds

In H

om

og

en

ou

s a

nd

Hete

rog

en

eo

us

Sa

nd

Pa

cks

193

Tab

le 5

-7:

San

d pa

ck d

ime

nsio

ns,

poro

sitie

s a

nd m

ass

grad

ient

s

San

d P

ack

Per

mea

bilit

y C

onfig

urat

ion

Dry

Mas

s of

Gla

ss

Tub

e (

g)

Plu

gs

Mas

s (g

)

Net

mas

s of

San

d P

acke

d in

(g

)

Tot

al

San

d Le

ngth

(c

m)

San

d M

ass

Gra

dien

t (g

/cm

)

Tot

al

Vol

ume

(cm

3 )

Por

osity

(f

ract

ion)

P

ore

Vol

ume

(cm

3 )

23

L-H

28

6.84

1 57

.697

19

5.5

147.

3 1.

33

108.

9 0.

372

40.5

19

H

-L

287.

494

57.6

90

197.

5 14

7.6

1.34

10

9.1

0.37

3 40

.7

18

H

286.

898

57.6

71

196.

1 14

7.3

1.33

10

8.9

0.37

3 40

.6

17

L-H

28

7.51

9 57

.679

19

8.4

147.

7 1.

34

109.

1 0.

365

39.9

16

H-L

28

6.90

0 57

.676

19

6.9

147.

5 1.

33

109.

0 0.

369

40.3

15

L 28

7.53

5 57

.679

20

4.9

147.

4 1.

39

108.

9 0.

344

37.5

194

6 Investigations of ASP Flooding Flow Impairment and Permeability Impact on Emulsion Droplet Size

Distribution

This chapter reports the experimental investigations undertaken to address the flow

impairment observed in the well-controlled sand pack experiments of the ASP flooding

which has been discussed in Chapter 5. The emulsions produced in these floods were

analysed to find the droplet size distribution. The procedures are described in this

chapter.

6.1 Background

In the experiments conducted in Chapter 5, all the sand packs suffered from some

degree of flow impairment except for the high permeability sand pack. Although, Shen

et al. (2009) used similar injection sequence, they did not report flow impairment

because their physical model was only 0.5 m long compared to the 1.5 m long sand

packs used in our experiments. An explanation of the flow impairment is important to

understand the ASP process.

Furthermore, the oil recovery experiments with the heterogeneous sand packs of lower-

to-higher permeability transition showed less flow impairment and a higher EOR

compared to the cases of higher-to-lower sand packs (Table 5-3). This indicates there is

some dependence between the longitudinal heterogeneity of permeability and flow

direction in the ASP flooding process. This dependence could be related to the flow of

in-situ generated emulsion. Investigating emulsion flow impact on the permeability and

ASP EOR process would need to address the emulsion droplets size distribution.

6.2 ASP Flooding Flow Impairment Investigation In the well-controlled sand pack experiments, increase in injection pressure due to the

flow impairment was observed in five sand packs. The experiments were adjusted to a

constant injection rate of 0.07 mL/min, thus, the observed increase in injection pressures

indicate changes to the permeability of the sand packs. Figure 5-27 and Figure 6-2

Chapter6: ASP Flooding Impairment and Emulsion Droplet Size Distribution

195

show the injection pressure and production rates responses in these experiments. Note

that the changes in the injection pressures and the production rates here were not

because of the storage capacity (compressibility) or ill-performance of the flooding

setup (Chapter 5). These changes in the injection pressure and the production rates were

experimental outcomes in response to the ASP flood.

Injection Pressure Response of the ASP Floods in th e Sand Packs

020

40

60

80100

120

140

160

180

200

0 0.5 1 1.5 2 2.5


SP

15,

SP

16,S

P17

and

S

P19

Inj

ectio

n P

ress

ure

(psi

)

0

10

20

30

40

50

60

70

SP

18 a

nd S

P23

In

ject

ion

Pre

ssur

e (p

si )

SP15:L SP16:H-L SP17:L-H SP19:H-L SP18:H SP23:L-H

ASP Waterslug drive

Figure 6-1: Pressure responses of all ASP floods for comparison. Note that SP18 and SP23 are plotted on the pressure axis on the right side of the graph for better scale resolution.

Production Rates of the Six well-Controlled Sand Pa cks Experiments and Flow Impairment

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 1 2 3 4 5 6


qt (

mL/

min

)

SP15 SP16 SP18 SP19 SP23


Figure 6-2: Flow rate impairment in the ASP floods happened after switching to water drive.


196

The homogenous sand pack with high permeability (SP18) showed no flow impairment.

On the other hand, increase in injection pressure due to the flow impairment was

pronounced for the homogenous low permeability sand pack (SP15). In the

heterogeneous sand packs, the higher-to-lower permeability transition packs (SP16 and

SP19) showed more severe and earlier flow impairment compared to the lower-to-

higher packs (SP17 and SP23).

There was a difference in the pressure profiles between the two cases of the

heterogeneous sand packs. In the case of the higher-to-lower permeability transition, the

injection pressure started to increase when the ASP slug was injected and continued to

increase until it reached the maximum allowed injection pressure. In the case of the

lower-to-higher permeability sand packs, the pressure increased until it reached a peak

and then started to decrease gradually. This peak corresponded to the oil bank

breakthrough. At a later time the emulsion came out. After injecting about 1 PV of

water drive, the pressure started to rise again. Severe flow impairment events occurred

at later time in the lower-to-higher permeability transition compared to the higher-to-

lower.

The flow impairment may choke the flow and may drop the production rates in the sand

packs, thus, reduce the oil recovery. An explanation to this flow impairment and why it

had happened always after switching from ASP injection to water injection is required.

The flow impairment happened in all cases after the oil bank and emulsion were

produced, thus, it was initially thought that the emulsion was not involved. In addition,

the impairment was more severe in some of the sand packs than others. Some of the

possible mechanisms of this flow impairment are: asphaltene and wax deposition,

surfactant precipitation, fine migration, polymer plugging, gelation process,

modification to water relative permeability by polymer adsorption and formation of

stable emulsion. Each of these is discussed in the following sections.

6.2.1 Elimination of Wax and Asphaltene Deposition

Initially, asphaltenes or wax depositions were suspected to be responsible for the flow

impairment. In some small scale experiments, asphaltene had been shown to damage the

cores during core flooding experiments and reduce permeability (Minssieux, 1997). In a

large scale asphaltenes can deposit in petroleum production tubes and cause flow

problems (Haskett et al., 1965). Yet, the assay of the Stag oil (which was used to make


197

Oil 3 as described in Chapter 4) shows insufficient amounts of wax and asphaltenes to

cause physical plugging (Santos, 2011). On the other hand, Ondina 15 is pure paraffinic

oil which, meant that the oil mix (Oil 3) should have lower concentrations of

asphaltenes and wax. Furthermore, it was suspected that the crude oil container perhaps

was sampled from a stock tank or a well location that possesses higher concentrations of

solids like wax and asphaltene. This suspicion was supported by the observation of

significant amounts of suspended and sediment solids in Oil 3 which possibly originated

from the Stag Crude used to prepare Oil 3 (Figure 6-3).

Figure 6-3: Solid particles suspended in Oil 3, image taken through the camera of the IFT cell described in Chapter 4.

In order to check whether the observed suspensions in Oil 3 were asphaltenes (organic)

or inorganic sediments, about 2 grams of the solid residues from the Stag Crude 15 L

iron container were taken for further analysis. This sample was taken from the bottom

of the container using an iron blade. About half of a gram of these residues was heated

to 550 oC in a muffle furnace for 24 hours. There was very little change in the

appearance of the residue after the heat treatment. It went from a brown paste to a

red/brown powder. The temperature was then increased to 650 oC for a further three

hours. No further change was observed. A portion of lube oil (heavy end alkanes with

carbon chain of 50+) was carried through the procedure to confirm that the conditions

used would remove all organic material. After only a few hours at 550 oC, there was no

trace left of the lube oil. This test confirmed that the residue from crude oil was mainly

inorganic in nature, thus, the asphaltenes and wax deposition was ruled out.

Some of the red/brown powder was added to concentrated hydrochloric acid. This

resulted in total dissolution of the powder into the acid. Analysis of the acid solution by

ICP-AES confirmed the presence of a number of metals with Fe being the most

abundant. The iron was possibly introduced into the (2 gram) sample from the bottom of

2 mm


198

the iron container during the 2 gram sample collection. Table 10-1 (Appendix B3)

shows the relative concentrations of the detected metals. The ICP-AES system was not

calibrated for this sample analysis, therefore, the provided numbers are only qualitative

and reflect the relative amounts of the detected metals.

In summary, these solid suspensions in Oil 3 did not plug the flow because at the oil

saturation in the sand pack and secondary oil recovery stages there was no flow

impairment. The impairment only happened during water drive after the ASP flood.

6.2.2 Elimination of Surfactant Precipitation

Surfactant precipitation could take place if there were sufficient quantity of divalent

ions like Ca++ or Mg++ present during the flooding (Lake, 1989). The water that was

used to prepare the ASP slug and saturate the sand packs was deionised water and the

sands had been washed by deionised water. Some samples which were collected during

the ASP floods were tested for the presence of divalent ions. The ICP-AES confirmed

that this deionised water had very low concentrations of divalent ions (Table 9-5 in

Appendix A3). It also confirmed that the content of divalent ions in samples obtained

from the actual runs were very low. Therefore, it was concluded that surfactant

precipitation was unlikely the source for the flow impairment.

6.2.3 Elimination of Fine Migration

Fine migration was also ruled out because the sands were washed as described earlier in

Chapter 5. The very fine particles are allowed to float out during the sand washing.

Moreover, the flow rate within the sand pack was thought to be too slow to induce the

flow of threatening fines migration. There were also no solids observed in the

transparent production tube lines or the collected samples which further confirmed that

the sand pack porous medium matrix was preserved against fine migration during the

flooding experiments.

6.2.4 Elimination of Polymer Plugging

One could suspect the polymer of the ASP slug to act as a plugging agent. In the

process of polymer retention in a porous medium, severe reduction to permeability

could occur (Sorbie, 1991). The plugging that is discussed here refers to the process in

which the polymer is physically blocking the flow by clogging the pore throats. During


199

the ASP flooding, the ASP slug initially appeared to flow through the sand pack and the

flow impairment occurred at a later time after injecting about 0.4 PV of ASP and 0.5-1

PV of water drive. This meant that the polymer (within the ASP slug) flowed through

the sand pack and its physical existence in the pores did not clog the pores and did not

block the flow. Moreover, there was a 7 µm inline filter in the ASP injection line to

break up any possible polyacrylamide gels and prevent solids getting into the sand pack.

In all experiment runs, most of the polymer, if not all, was largely produced out of the

sand pack before flow impairment took place. This is supported by the chemical profiles

of the produced water in the ASP flood. Therefore, the direct physical plugging of the

polymer (the ASP slug) to the pore throats was deemed unlikely.

In order to confirm that the polymer molecules did not plug the pore throats and impair

the flow, one ASP flood was run in SP22 by injecting about 1.4 PV of ASP for EOR

without water drive. This pack was subjected to all the steps listed in Section 5.6.2

except for the water drive. In this case, there was no flow impairment and the oil was

entirely recovered. The injection pressure reached a plateau of 45-50 psi after the oil

bank and emulsion breakthrough (Figure 6-4). Subsequently, no further increase in

pressure was observed. The ASP flooding was stopped after the produced fluid became

entirely clear ASP slug. Mass check confirmed that the oil was entirely recovered

except for some traces. This confirms that the polymer and ASP components did not

physically plug the sand packs.

SP22 ASP Flooding without Water Drive 1.4 PV of ASP injection

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1 1.2 1.4Pore Volumes of Injection

Inje

ctio

n P

ress

ure

(psi

)

Figure 6-4 : No flow impairment in SP22 was observed during the injection 1.4 PV of ASP slug for EOR without water drive.


200

6.2.5 Elimination of Polyacrylamide Polymer Gelation Process

Gelation was discussed in Chapter 2. The long chains of polyacrylamide polymer may

cross-link to form 3D network of polyacrylamide chains which in turn form gels. This

process is initiated deliberately in some fields as a measure for water production control

(Green and Willhite, 1998). These gels may sometimes block the flow entirely. It was

suspected that gelation process could had occurred in the ASP sand pack floods and was

the reason behind the observed flow impairment. The main requirements for the

gelation process to occur are generally: trivalent ions (Al+++ or Cr+++) from sources such

as sodium dichromate (500-1000 ppm), reducing agents like sodium bisulphate (500-

1000 ppm) and polymer like HPAM (2000-6000 ppm) (Green and Willhite, 1998, pp.

145).

Some samples obtained from water and ASP floods in the sand pack were analysed

using ICP-AES. Elements like Fe and Al were detected; however, the ionic state of

these metals was not determined. These metals can become positive tri-ions which is a

prerequisite for the gelation process. The maximum detected concentration of these

elements in the samples was found to be less than 6 ppm. This is a low concentration

and is unlikely to initiate the gelation process (Green and Willhite, 1998, pp. 145).

Moreover, there was no oxidising agent injected to ionise these elements into their third

ionisation state. Therefore, the gelation process was unlikely to be responsible for the

observed flow impairment in the sand packs.

6.2.6 Eliminating Meshes Impact on Flow Impairment

These meshes (scouring pads) did not affect the experiment during the water flooding or

oil injection stages. This indicates that the meshes are not blocking the flow. During the

initial flow out of the ASP slug, there was no flow impairment, this implicitly suggest

that the physics behind the flow impairment is not related to the meshes. There are two

meshes, one in the injection side of the sand pack and the other at the production side. If

the meshes were blocking the flow, the first mesh that comes just after the Teflon

injection plug (Figure 5-4) would not allow the ASP slug to get into the sand pack and

the injection pressure will start sharply increasing immediately at the start of the ASP

slug injection. This is not the case as seen in, for example, Figure 5-18 and Figure 6-5

where the increase in injection pressure starts after switching to water dive. Furthermore,


201

when 1.4 PV of ASP slug were injected in SP22 (Section 6.2.4), the meshes were

present in the sand pack during this flood but no plugging was observed. All of these

confirm that the meshes are not clogging the flow.

6.2.7 Polymer Adsorption Contribution to Flow Impairment

The polyacrylamide polymers can significantly change the relative permeability of

water and for this very reason are used as relative permeability modifiers (White et al.,

1973). Note this is not the same phenomena as the physical plugging previously

discussed in Section 6.2.4. Physical plugging is due to the physical assembly of the

polymer in the flow path while adsorption is the process in which the polymer

molecules adhere to the pore walls or pore throats and it do not completely block the

flow through the pores although could narrow them.

The possible decrease in water relative permeability in the sand packs by polymer

adsorption was investigated as a possible cause for flow impairment in the sand packs.

It is well known that the polymer adsorption reduces the permeability to water flow in

the porous medium (Sorbie, 1993). The residual resistance factor (RRF) is a measure to

evaluate this change and is defined in Equation 2-29 (Lake, 1989). No adsorption

measurements were performed in this project. However, polymer adsorption on the

silica grains of the porous medium is very likely to occur during chemical flooding

processes involving polymer (Lake, 1989; Green and Willhite, 1998). Therefore, it was

assumed that some of the polyacrylamide polymer injected in the ASP slug had

adsorbed in the sand packs.

In order to investigate the possible effects of the polymer adsorption on the permeability

of the sand packs to water, two sand packs, SP21 and SP22, were used. SP22 had been

used earlier to test polymer plugging when a 1.4 PV ASP slug was injected as discussed

in Section 6.2.4. After recovering the oil from SP22, it was further used to study the

changes to permeability during ASP flooding and water drive. As mentioned in Section

6.2.7, the oil was almost entirely recovered by injecting 1.4 PV of ASP slug and only

traces of oil or emulsion remained in the sand pack.

At the beginning of the subsequent test using SP22, the sand pack was initially saturated

with ASP slug remaining from the test in Section 6.2.4. More ASP slug was injected


202

again into this sand pack to reach stable flow. After injecting 0.4 PV of ASP slug the

injection was switched to water drive. The injection pressure started to rise sharply after

injecting about 1 PV of drive water (Figure 6-5). This is a similar response to the

pressure response observed in the well-controlled experiments, albeit to a less extent.

The second sand pack that was used to study the possible impact of polymer adsorption

on water relative permeability was SP21. This sand pack was not subjected to oil

saturation and thus no emulsion or oil traces existed in this sand pack. SP21 was first

saturated with DW. After establishing a constant flow, the injection was switched to

ASP flood. After injecting 0.4 PV of ASP, the injection was then switched back to

water drive. This allowed the polymer time to adsorb on the sand pack with comparable

time as for the six well-controlled ASP flooding experiments. Reduction in water

relative permeability was also observed in SP21, but to a much less extent as evidenced

by a smaller increase in the injection pressure shown in Figure 6-4.

The investigations on these two sand packs (SP21 and SP22) confirmed that the

polymer adsorption reduces the relative permeability to water. The presence of oil traces

or emulsion enhances the reduction of water relative permeability. These observations

are consistent with the findings of Zheng et al. (2000) who reported that the adsorbed

polyacrylamide polymer in the presence of oil causes a larger relative permeability

change to water than when no oil is present.

In summary, this confirms that the polymer adsorption may have contributed to the flow

impairment but was not enough in itself to cause the severe flow impairments observed

in SP15, SP16, SP17, SP19 and SP23. Moreover, the reduction in water relative

permeability did not explain why the observed increase in the injection pressure

occurred earlier in the higher-to-lower permeability transition pack compared to those

of lower-to-higher packs. The remaining possible agent that can cause the flow

impairment is the emulsion which was not initially suspected. The emulsion was not

initially suspected because the flow impairment event occurred in all sand packs after

the emulsion was produced. However, this new view on polymer adsorption suggests

that both emulsion flow and reduction in water relative permeability by polymer

adsorption may have collectively contributed to the flow impairment. Therefore, the

possibility of emulsion causing the flow impairment was investigated and is discussed

in the next section.


203

Change in Injection Pressure to Water Drive After ASP Flood SP21 and SP22

0

20

40

60

80

100

120

140

160


Inje

ctio

n P

ress

ure

(psi

)

SP22: Initially Saturated with ASP slug and some oil traces

SP21: Initially saturated with DW

ASP Waterslug drive

Figure 6-5: Change in injection pressure to water drive after ASP flood in two sand packs of which one was saturated with DW (SP21) and was not subjected to oil saturation, the other sand pack was saturated with ASP slug and was subjected to oil saturation (SP22).

6.2.8 Emulsion Contribution to the Flow Impairment

Emulsion was observed to form in situ during the ASP process. Emulsion flow in a

porous medium is discussed in Chapter 2. The stable emulsions specific to the ASP

process are not new, and have been the subject of some published papers (Kang et al.,

2000; Guo et al., 2006). However, these papers were addressed the stability of

emulsions found in the produced oil/water from the ASP process. Formation of stable

emulsions in produced water in the ASP process has been associated with the

synergistic effects of the alkali, surfactant and polymer in the ASP process (Kang et al.,

2000). In another study, asphaltenes were also suggested as causative agents able to

reduce IFT and enhance emulsion stability in the ASP process (Guo et al., 2006). The

ratio of resins to asphaltenes plays an important role on the formation and stability of

emulsions (Graham et al., 2008).

None of the studies above, including those studies reported in Chapter 2, on emulsion

flow in porous medium was dedicated to systematically study the droplet size

distribution of the emulsions generated in situ during the ASP process. Therefore, this is


204

suggested as an area for future study related not only to ASP flooding, but other

processes which may involve in-situ emulsion generation.

The stability of the emulsion can cause flow problems when the emulsion droplets has

size that can overlap with the pore throats (McAuliffe, 1973; Soo and Radke, 1986).

However, the presence of the surfactant in the ASP slug should facilitate the break up of

droplets plugging the pore throats (Arriola et al., 1983). This view was supported by the

fact that the impairment occurred after the emulsion was produced. Further investigation

on possible contribution of the emulsion to flow impairment would require finding the

EDSD. The determination of EDSD is discussed in the next section.

6.3 Emulsion Droplet Size Distribution

The literature on the determination of EDSD using NMR-PFG-STE and the reasons for

choosing this technique has been covered in Chapter 2. In this study, emulsions were

collected from the ASP floods in the six well-controlled as described in Chapter 5.

These emulsions were then subjected to the experimental procedure described in the

following section.

6.3.1 Experimental Procedure of NMR-PFG-STE Experiments

The NMR instrument that was used to determine the EDSD was a Brurker Avance 500

MHz NMR spectrometer. The NMR signal of hydrogen proton 1H was followed.

Because of the high frequency of this instrument, it was possible to follow the water and

oil stimulated spin-echo signals simultaneously. Therefore, the same spectrum from one

emulsion experiment could be used to perform theoretical curve fittings to find the

droplets sizes whether the emulsion is oil-in-water or water-in-oil. For the oil-in-water

emulsion, the oil NMR signal is analysed, and for water-in-oil emulsion, water signal is

analysed.

Emulsion samples were obtained form ASP floods in SP15, SP16, SP17, SP18, SP19

and SP23. For the emulsions obtained from each of these sand packs, about 0.5 mL of

emulsion was placed in an NMR grade glass tube. These samples were then, in turn,

placed in the NMR spectrometer and given enough time to equilibrate with the

spectrometer’s temperature of 25 oC. All experiments were done at this temperature.

The NMR pulse sequence that was used in this work was the PFG-NMR-STE which is


205

shown in Figure 2-22. Instrument setting employed 300 ms for ∆ (time between

gradient pulses) and 3.6 ms for δ (duration of gradient pulsed). Several magnetic

gradients with different amplitudes (0.68-32.5 G/cm) were applied and the amplitudes

of the returned (attenuated) NMR signals of the STE were recorded. The diffusion

coefficients for molecules in the free Oil 3 and free ASP slug were also obtained using

the method of Tanner and Stejskal (1968) already reported in Chapter 2.

6.3.2 NMR Diffusions Coefficients and Signal Attenuation Results

The applied magnetic field gradients and the amplitude of the returned stimulated spin-

echo for oil and water are reported in Table 6-1 and Table 6-2. The unrestricted

diffusion coefficient of free water molecules in the ASP slug and free Oil 3 molecules

were found to have values of 2.20 x 10-9 and 3.75 x 10-11 m2/s, respectively. These

coefficients are needed for later use in the restricted diffusion model in the next section.

Table 6-1: NMR-PFG-STE attenuation of oil peak Pulsed Field

Gradient (gauss/cm)

SP15 SP16 SP17 SP18 SP19 SP23

0.684 1.00 1.00 1.00 1.00 1.00 1.00 5.226 0.99 0.99 1.00 0.97 1.00 1.04 9.768 0.96 0.99 0.97 0.91 0.98 1.01 14.31 0.89 0.97 0.88 0.83 0.93 0.92 18.85 0.80 0.94 0.77 0.72 0.87 0.80 23.40 0.70 0.91 0.66 0.62 0.80 0.68 27.94 0.59 0.87 0.54 0.51 0.74 0.56 32.48 0.49 0.83 0.44 0.41 0.67 0.44

Table 6-2: NMR-PFG-STE attenuation of water peak Pulsed Field

Gradient (gauss/cm

SP15 SP16 SP17 SP18 SP19 SP23

0.684 1.00 1.00 1.00 1.00 1.00 1.00 5.226 0.40 0.36 0.33 0.39 0.37 0.58 9.768 0.14 0.04 0.07 0.15 0.06 0.25 14.31 0.07 0.01 0.04 0.11 0.02 0.19 18.85 0.06 0.00 0.03 0.10 0.01 0.18 23.40 0.05 0.00 0.03 0.09 0.01 0.16 27.94 0.04 0.00 0.02 0.08 0.01 0.15 32.48 0.04 0.00 0.02 0.07 0.00 0.13


206

6.3.3 Numerical Procedure of NMR Experiments The theoretical model of the restricted diffusion in emulsion (Equation 2-37) was used

to fit matching curves to the experimental results shown in Table 6-1 and Table 6-2.

The fitting parameters which were used to produce the theoretical curves were the mean

droplets diameter and the distribution width. As discussed in Chapter 2, this model

assumes a log-normal distribution for emulsion droplet sizes. The theoretical results and

experimental results would match when these two parameters produce a distribution that

overlaps with the emulsion distribution in the sample. These parameters are then used to

construct a log-normal EDSD distribution.

Nonlinear least squares curve fitting function lsqcurvefit in MATLAB ® was used to

perform the curve fitting (version 7.12.0, R2011a release, MathWorks Inc, USA).

MATLAB ® code was made to model the equation of restricted diffusion in emulsion

(Equation 2-38 which includes Equation 2-35). The code is provided in Appendix C1.

This model needs the roots of the Bessel function (Equation 2-37). These roots were

obtained using MATLAB® code provided in Appendix C2. Instructions on how to use

the codes in Appendix C1 and C2 to perform theoretical fitting of Equation 2-38 to

match experimental results and obtain the emulsion droplets size distribution are

described in Appendix C3.

6.3.4 The Results of Emulsion Droplet Size Distribution

The NMR-PFG-STE experimental results from Table 6-1 and Table 6-2 are reproduced

in graphical format with the fitted theoretical curves and are shown in Figure 6-6 and

Figure 6-7. The dotted lines in the two graphs show the behaviour of the unrestricted

diffusion model (Equation 2-34) with the same input parameters (gradients amplitude,

∆, δ and diffusion coefficient) used to construct the fitted curves of the restricted model.

As the droplet size increases the response approach that predicted for unrestricted

motion of the molecules.

According to the model of restricted diffusion in emulsion droplets (Equation 2-38),

the theoretical curves match the experimental results when the theoretical droplet size

distribution width and mean match with those of the real droplet size distribution. Since

the emulsion was found in the lower Winsor phase behaviour (oil-in-water) using


207

electrical resistance as reported in Chapter 4, the NMR signal from oil stimulated spin-

echo were used to find EDSD. The values of distribution width and mean (fitting

parameters) which produced the best fitting curves in Figure 6-6 are reported in Table

6-3 as well as the sum of the least squares residuals. The log-normal size distributions

of the emulsion droplets were then constructed using the distribution means and widths

as inputs into Equation 2-39. The resulting distributions are shown in Figure 6-8 and

Figure 6-9. The water NMR signal from all the emulsions showed the behaviour of

unrestricted diffusion (Figure 6-7). As a result, the fitted curves based on water NMR

signal produced large least square sums, therefore, the results were discarded.

Table 6-3: Mean droplet diameter and distribution width obtained from curve fitting based on oil NMR signal (Oil-in-Water emulsion)

Emulsion of Sand Pack

Mean droplet diameter (µm)

Distribution width

Sum of least squares residuals (x10-3)

SP15 16.7 0.0048 1.00 SP16 5.4 0.2143 0.16 SP17 22.4 0.0004 2.70 SP18 0.2 1.6086 0.20 SP19 7.4 0.3169 0.31 SP23 18.9 0.0041 14.00

0 5 10 15 20 25 30 35 40

0.4

0.5

0.6

0.7

0.8

0.9

1


Spi

n-E

cho

Att

enua

tion

Predicted and Observed Attenuation of Oil NMR Signal in ASP-Oil Emulsion

ExpFit

UnrestrictedDiffusion Model

SP16

SP19

SP15

SP17 and SP23SP18

Figure 6-6: Observed and fitted curves of restricted diffusion of emulsion formed in the ASP flooding of the sand packs for ∆=300 ms, δ= 3.6 ms, D (diffusion coefficient of oil =3.75 x 10-11 m2/s).


208

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1


Sp

in-E

cho

Att

en

uat

ion

Observed Attenuation of NMR Signal of Water in ASP-Oil Emulsion

UnrestrictedSP15SP16SP17SP18SP19SP23

Figure 6-7: Observed and fitted curves of restricted diffusion of emulsion formed in the ASP flooding of the sand packs for ∆=300 ms, δ= 3.6 ms, D (diffusion coefficient of water in ASP slug =2.20 x 10-9 m2/s).

0 5 10 15 20 25 300

0.5

1

1.5

Pro

babi

lity

dens

ity

(Nor

mal

ised

to

max

imum

pea

k he

ight

)

Droplet Size Distribution of Oil-in-ASP Emulsions from the Heterogeneous Sand Packs

Droplet Diamter (µm)

SP16:H-LSP17:L-HSP19:H-LSP23:L-H

Emulsion ofLower-to-Higher Permebility TransitionSP17 and SP23

Emulsion of Higher-to-Lower Permebility TransitionSP16 and SP19

Figure 6-8: Droplet size distribution of emulsion produced in ASP floods in the heterogeneous sand packs (SP16, SP17, SP19 and SP23) using NMR-PFG-STE.


209

0 2 4 6 8 10 12 14 16 180

0.5

1

1.5P

roba

bilit

y de

nsity

(N

orm

alis

ed t

o m

axim

um p

eak

heig

ht)

Droplet Size Distribution of Oil-in-ASP Emulsions from the Homogenous Sand Packs


SP15:LSP18:H

SP18 SP15

Figure 6-9: Droplet size distribution of emulsion produced in ASP floods in the homogenous sand packs (SP15 and SP18) using NMR-PFG-STE.

6.3.5 Discussion on Emulsion Droplet Size Distribution Sand packs with higher-to-lower permeability transition (SP16 and SP19) produced

emulsions droplets with mean diameters of 5.4 and 7.4 µm, respectively. The droplet

size distributions of emulsion from the heterogeneous sand packs with lower-to-higher

permeability transition (SP23 and SP17) were found to have very narrow distributions.

The very narrow widths of these two distributions indicate that the molecules have

diffusion coefficients with very close values. This meant that the molecules had

travelled equal distances during the measurement time between the two gradient pulses.

In unbounded liquid, the molecules would be expected to diffuse randomly with equal

distances. The difference between these travelled distances, if unrestricted, will

probably fall within a very narrow range. In a successful theoretical fitting, the

nonlinear fitting function would produce a narrow distribution corresponding to the

narrow variance in the diffusing distance travelled by the molecules. This in turn meant

that the measured diffusion in these emulsions was largely unrestricted. Therefore, the

droplet sizes of emulsions from the ASP floods of SP23 and SP17 were beyond the

upper measurement limit of the NMR-PFG-STE.


210

The upper limit of the maximum measurable droplet size using NMR-PFG-STE method

is discussed in Chapter 2. This upper limit for oil-in-water droplets was found to be

equal to 10 µm. This distance was calculated using Equation 2-40 based on

experimental values of measurement time (∆) between the two gradient pulses and Oil 3

diffusion coefficient of 300 ms and 3.75 x 10-11 m2/s, respectively. This means SP23 and

SP17 have emulsions with droplet sizes bigger than 10 µm. Therefore, the displayed

distributions of these two sand packs in Figure 6-8 do not represent the real EDSD of

these two emulsions but do reflect that their real droplet size distribution would be

larger than 10 µm.

The fact that SP23 has slightly wider droplet size distribution than SP17 reflects that the

droplets of the emulsion from SP23 probably had droplets with a size distribution closer

to the upper limit of the NMR-PFG-STE than SP17. This would mean that a small

number of the molecules in the droplets of SP23 emulsion had been hitting the droplets

boundaries while a much smaller number of molecules in SP17 had managed to hit the

boundaries during the time of measurement. Therefore, the emulsion droplets of SP17

are larger than the droplets in SP23.

The observations discussed in the last few paragraphs show that the lower-to-higher

permeability transition sand packs produced larger emulsion droplets than those

droplets produced in higher-to-lower permeability transition sand packs. In other words,

those pack ending with higher permeability section produced an emulsion with larger

droplet size compared to those ending with lower permeability section. Therefore, it can

be concluded that permeability has some influence on droplet size of the emulsion

produced in the ASP process.

The EDSD of the homogenous sand packs (SP15 and SP18) are shown in Figure 6-9.

Based on the discussion above, SP15 (having lower permeability) was expected to

produce emulsion droplets with sizes comparable to those of SP16 and SP19. On the

other hand, SP18 (having higher permeability) was expected to produce emulsion

droplets comparable to those of SP17 and SP19. This was not the case, SP15 produced

emulsion with droplet size beyond the upper limit of the NMR-PFG-STE while SP18

produced emulsion with mean droplet diameter of about 0.2 µm. In addition, the

attenuation in the NMR signal of the emulsion from SP18 shown in Figure 6-6 decayed

more than the NMR signals for emulsion from SP17 and SP23 as the gradient strength


211

was increased. This would mean that SP18 had emulsion droplet size larger than SP17

and SP23 which was contradictory. To resolve this, microscopy techniques were used to

check the emulsions from SP15 and SP18.

The optical microscope (Olympus which was reported in Chapter 4) was used to take

several images of the emulsions of SP15 and SP18. These images were then manually

analysed using ImageJ software. The required number of droplets needs to be more than

500 to produce representative droplet size distribution (O’Rourke and MacLoughlin,

2005). However, only about 150 droplets were analysed in this work. The resulting

sizes where then used to find the mean and distribution width. The final distributions of

SP15 and SP18 emulsions based on image analysis from microscopy are reported in

Figure 6-10 with the data for droplet ranges. The EDSD of SP15 which was obtained

from the image process has very close mean and width (Table 6-4) to those obtained

from the NMR-PFG-STE (Table 6-3). Note a normal distribution was used to make the

distribution fit to the experimental date instead of using log-normal distribution because

the histograms derived filled better with a normal distribution.

SP18 was found to have a mean droplet size of 42 µm using image processing compared

to 0.2 µm using NMR-PFG-STE. This is a large difference between the results of the

two methods. However, the images of emulsion from SP18 looked more complex than

the emulsion from SP15. A sample image of emulsion from the flood of SP18 is shown

in Figure 6-11. This image shows that there are much smaller droplets within the larger

droplets. This image suggests that the emulsion was a multiple emulsion. In contrast,

emulsion from SP15 was much simpler (Figure 6-12) with clear droplet structure and

there was no multiple emulsion type.

Table 6-4: Mean droplet diameter and distribution width obtained from image processing of emulsion of the homogenous sand packs (SP15 and SP18) Mean droplet diameter (µm) Distribution width

SP 15 18.6 0.347 SP 18 42.7 17.520


212

0 10 20 30 40 50 60 70 80 900

0.01

0.02

0.03

0.04

0.05

0.06

0.07


Pro

babi

lity

Den

sity

(

Arb

itrar

y U

nits

)

Droplet Size Distribution of Oil-in-ASP Emulsions from the Homogenous Sand Packs (SP15 and SP18)

SP18:H Data SP18: FitSP15:L Data SP15: Fit

Figure 6-10: EDSD based on image processing of emulsion images of SP15 and SP18. Only about 150 droplets were analysed in each of these two emulsions and the histograms are plotted to show the actual size ranges.

Figure 6-11: An image showing the emulsion of SP18 with clear evidence of multiple emulsions. Note the much smaller droplets within the larger droplets.


213

Figure 6-12: An image showing the emulsion of SP15.

Given the complex structure of emulsion from SP18 (Figure 6-11) it possible that there

were several discrete distributions of emulsion droplet sizes within this emulsion. The

NMR-PFG-STE method would have given an average distribution of those droplets

below its upper measurement limit, whereas, those droplets with sizes far from its upper

measurement limit contributed to unrestricted diffusion. The larger droplets of this

emulsion were out of the range of the NMR-PFG-STE, but within the range of optical

microscopy. Therefore, it is plausible to regard both distributions derived from NMR-

PFG-STE and image processing as being consistent.

With this reasoning it is possible to explain the observed decrease in the NMR signal of

SP18 (Figure 6-6) which showed a decay similar to the decay observed in unrestricted

diffusion. In the SP18 emulsion, most of the oil bulk was dispersed in form of oil-in-

water droplets with sizes far above from the NMR measurable upper limit. Therefore,

the NMR signal would decrease as if the diffusion was unrestricted leading to a larger

signal decay as the pulsed field gradients were increased. However, the smaller droplets

of this emulsion which were within the measurement range of NMR-PFG-STE would

contribute a restricted diffusion component to the curve shape. Therefore, the theoretical


214

fitting to find the droplets size distribution from the NMR signal of this emulsion had

probably given curves with a shape that complied with this component and was largely

unaffected by the contribution from the unrestricted diffusion in the much larger

droplets.

SP15 should had produced emulsion with droplet sizes close to those produced from

SP16 and SP19 because they all end with lower permeability in their second half with

respect to the ASP flood direction. In contrast, SP15 produced droplets with almost

double the size (Table 6-3 and Table 6-4). The reasons for this contradiction are not

clear. However, the emulsions are complex systems which are not easy to comprehend.

No further work was conducted to address the droplet size anomaly of emulsion from

SP15.

In summary, the size of the emulsion droplets produced during the ASP flooding in the

well-controlled experiments showed dependence on the permeability of the sand pack.

Tighter permeability would promote the production of emulsion with smaller droplets.

This observed size dependence between emulsion and permeability would have

consequences on the flow of emulsion in the ASP process and the overall performance

of the process. More discussion on the droplet size of the in-situ generated emulsion and

its relation to the flow impairment is provided in the next section.

6.4 Average Droplet Size of In-Situ Generated Emulsi on and Permeability

The results of the EDSD reported in Section 6.3.4 suggest that the average droplets size

of the emulsion formed in situ during the ASP process is moulded by the size of the

pores. The following proposes an explanation of how the emulsion droplet size is

influenced by the size of pore and pore throats in the ultra low IFT conditions in the

ASP process.

When flowing droplets overlap with some of the pore throats or pore constrictions

(Figure 2-17), they may get stuck (straining) in the pore throats and partially block the

flow in the porous medium (Figure 2-18). According to Equation 2-30, the stuck

droplets would require higher pressures to flow through the narrower parts of the pores.

When the flooding environment supports ultra low IFT, droplets stuck in pore


215

constrictions may break up and disintegrate into smaller droplets able to get through the

constrictions (Arriola et al., 1983). The ASP slug used in this study provides ultra low

IFT as was proved in Chapter 4, thus, if the flow of the ASP slug exerted enough

pressure, the initial emulsion would convert into emulsion with smaller droplets when it

gets through narrower pore throats. As the flooding continuous with enough pressure,

these smaller droplets would go through a coalescence process when they get through

wider pore throats. They would break up again when they flow through narrower pores

as long as the ultra low IFT is maintained. As the droplets continue to flow, the process

of breaking up and coalescence also continue and the average droplet size would be

moulded by the average size of the pore throats.

With respect to ASP floods in the heterogeneous sand packs, the flow of ASP slug in

the packs ending with the tighter half section (higher-to-lower permeability transition)

produced emulsion droplets with smaller diameters compared to those formed in packs

ending with the more permeable half section (lower-to-higher permeability transition).

This is supported by the findings reported in the last section (Figure 6-10, Figure 6-8,

Table 6-3 and Table 6-4). The pore throat size distributions of the sand packs used in

this study were not determined. However, Carman-Kozeny relation (Equation 2-3)

suggests that porous medium with higher permeability would have pores and pore

throats with larger sizes and those with narrower pores and pore throats would have

lower permeability. Therefore, it is possible to propose that during ASP flooding with

ultra low IFT, size of the droplet of the in-situ generated emulsion depends on

permeability.

6.4.1 A Proposed Explanation of the Flow Impairment

The most acceptable explanation based on the experimental evidence available and the

viewed literature is the combined effect of two factors:

1) Emulsion formation during the ASP flow and,

2) Change of the relative permeability to water by polymer adsorption on the sand

grains.

Polymer adsorption was found to reduce the water relative permeability as discussed in

Section 6.2.7. This permeability reduction increases the injection pressure required by

the constant injection rate and would have taken place in all the six ASP floods


216

described in Chapter 5. Emulsion would normally need more pressure to flow through

the packs than normal water flooding. Thus, there would be expected increase in the

injection pressure when emulsion is formed but such significant pressure increases like

those shown in Figure 5-27 were not expected. The flow direction with respect to the

heterogeneity showed different pressure responses. This paragraph below proposes an

explanation to the different pressures responses observed in the heterogeneous sand

packs.

When the emulsion is formed in the higher permeability section and flows to the lower

permeability section (higher-to-lower permeability transition), it would have an average

droplet size comparable to that of the average pore throats in the higher permeability

section. As the emulsion is entering into the lower permeability section, it would need

to break up into smaller droplets, and would thus see a bigger flow resistance compared

to the resistance were it was formed. This resistance translates into significant pressure

increase which needs some time to build up. This extra pressure comes from

compressing the injected water during water drive stage, thus, the production rate drops.

This is the case for SP16 and SP19. When the emulsion is formed in the lower

permeability sections and flows to higher permeability section (lower-to-higher

permeability) it would have smaller droplet size than the pore throats in the higher

permeability section and thus does not need to break up and would faces less flow

resistance. SP17 and SP23 are examples of this case. Therefore, the increase in the

injection pressure in the case of lower-to-higher permeability transition comes from two

components: Emulsion flow and the changes of the relative permeability due to polymer

adsorption. In the case of higher-to-lower permeability transition, the increase in the

injection pressure comes from three components: Emulsion flow, polymer adsorption in

addition to the extra pressure increase required to break emulsion into smaller drops.

Therefore, the direction of emulsion flow in the ASP flooding would matter. This

finding helps to explain the observed flow impairment in the sand packs ASP floods.

6.4.2 Determination of Winsor Phase Behaviour Using NMR

Although, the phase behaviour of the emulsion of the six ASP flooding experiments

have been found to be lower Winsor phase behaviour (phase –II) using electrical

resistance as discussed in Chapter 4, the attenuation of the NMR-PFG-STE signals

reported in Figure 6-6 and Figure 6-7 can be used further to check the phase behaviour.


217

The results of the stimulated spin-echo signals attenuation from the emulsions formed in

all the ASP floods show that the water NMR signal decay faster than the oil signal as

the gradient field magnitude is increased (Table 6-1, Table 6-2, Figure 6-6 and Figure

6-7). This difference in the decay of the NMR signals in response to the NMR-PFG-

STE sequence indicates that the water molecules experience unrestricted diffusion. On

the other hand, the oil signal shows less attenuation as the pulsed field gradient is

increased which mean that the oil molecules experience restricted diffusion.

Consequently, the aqueous phase is continuous or has large droplets such that the water

molecules do not hit a boundary during the measurement time between the two gradient

pulses. In contrast, the oil phase is dispersed with oil molecules diffusing in restricted

structure (droplets). This confirms that the emulsion is of the oil-in-water type and this

is in agreement with the earlier electrical resistivity characterisation in Chapter 4.

6.4.3 Further Discussion on the Polyacrylamide and NMR Results

In Figure 6-7, it is noticed that the attenuation did not reach zero as did the unrestricted

diffusion model for the same input parameter. Probably, the presence of the

polyacrylamide long chain restricts the motion of water molecules in the vicinity of its

hydration radius. This restriction on diffusion of hydration water molecules had

probably prevented the total loss of the NMR signal as the magnitude of the pulsed

gradient is increased. The contribution of hydrogen nuclei of the polymer chain to the

overall signal may be too low to affect the NMR signals because of the low

concentration of the polymer in the ASP slug. However, the amount of hydration water

bound by the polymer chain could be significant and able to affect the observed NMR

signal. Moreover, the polyacrylamide chain may coil around itself and adopt spherical

like conformation as outlined in Chapter 2. Therefore, the spherical conformation could

probably bind some of the water molecules and restricts its diffusion in analogy to

emulsion droplets.

The diffusion of water molecules within the hydration shell of the long polyacrylamide

may be of interest to the ASP flooding. However, the limited time available of this

research did not allow further investigation and no discussion of the topic is taken

further.


218

6.5 Conclusion

All the sand packs suffered from some degree of flow impairment except for the high

permeability sand pack. The sand packs of the higher-to-lower permeability transition

suffered more flow impairment than those of the lower-to-higher permeability transition

packs. The impairment occurred at the later stage of the experiment after the ASP slug

reached to the end of the sand pack after the oil bank and emulsion were produced.

Therefore, the ASP slug components were not the cause for direct physical plugging

and flow impairment. The most possible mechanism was the combined effect of change

in relative permeability to water by the polymer adsorption on sand grains and the flow

of the in-situ generated emulsion. Other possible factors for the flow impairment such

asphaltene and wax deposition, fine migration, polymer physical plugging and

surfactant precipitation were evaluated and found unlikely the cause of the flow

impairment.

Sand packs with higher-to-lower permeability transition (SP16 and SP19) produced

emulsions droplets with mean diameters of 5.4 and 7.4 µm, respectively, whereas, packs

with lower-to-higher permeability transition (SP23 and SP17) produced emulsion

droplets with size beyond the limits of the NMR-PFG-STE method. The homogenous

sand pack, SP18 (high permeability) and SP15 (low permeability) produced emulsion

with sizes of 42 and 18 µm, respectively.

The sizes of the pore throats and pore geometry have some impact on the droplets sizes

of emulsion generated in-situ during ASP process. Permeability of porous medium can

be related to the pore size through the Carman-Kozeny relation. Therefore, the

permeability (pore throats) decided the droplet size in the ASP process provided that the

ASP slug is able to reduce the IFT. As a result, higher permeabilities zones would

produce larger emulsion droplets compared to those produced in lower permeability

zones and tighter permeability would promote the production of emulsion with smaller

droplets. Consequently, the ASP flooding direction whether it is high-to-low or low-to-

high permeability is detrimental to the response of injection pressure and oil recovery.

This confirms that the performance of the ASP process depends on the direction of flow

and the longitudinal heterogeneity in the permeability,

219

7 General Conclusions and Proposals for Future Work

7.1 Conclusions

The prime outcome of this PhD study is that the longitudinal heterogeneity does affect

the ASP process and that the process is dependent on the flooding direction with respect

to the longitudinal heterogeneity. Previous studies on ASP flooding in multi-layer

physical model (resembling 3D problem) showed that the ASP flooding was successful

to reduce the impact of vertical heterogeneity and enabled the recovery of some more

oil remaining after water flooding. In this current experimental study on heterogeneous

long thin sand packs (resembling 1D problem), we showed that within one layer, the

EOR of an ASP flooding could be further improved by flooding in a direction

coinciding with increasing permeability transition.

When the flow direction in ASP flooding goes from higher-to-lower or lower-to-higher

permeability transition, there is an observable difference in the amount of oil recovered

and the response of the injection pressure. The case of lower-to-higher permeability

transition is preferred for higher oil recoveries. In this work, the recovery margin

between lower-to-higher and higher-to-lower was about 5% OOIP. The ultimate oil

recoveries of the ASP floods from the higher-to-lower permeability transition packs,

SP16 and SP19, were 88.5% and 79.5% OOIP respectively and for the lower-to-higher

permeability transition packs, SP17 and SP23, were 95.4% and 95.8% OOIP

respectively.

The average droplets size of the in-situ generated emulsion in ASP process was shown

to depend on the size of the permeability (pore throats), however, more work is needed

to define this dependency. This average droplets size in the emulsion makes the ASP

process sensitive to the longitudinal heterogeneity. The droplet size distributions of the

emulsions produced in the ASP flooding experiment were measured using the NMR-

PFG-STE technique. Heterogeneous sand packs with lower permeability (narrower pore

throats) in their second half, SP16 and SP19, produced smaller emulsion droplets with

mean diameter of 5.4 and 7.4 µm, respectively. In contrast, sand packs with high

220

permeability (wider pore throats) in their second half, SP17 and SP23, produced larger

emulsion droplets beyond the NMR-PFG-STE maximum measurement limit of 10 µm.

This size dependence between droplets and pore throats would have consequences on

the injection pressure and make the ASP process direction dependent. During the flow

of emulsion in the ASP process, porous medium with higher permeability has larger

pore throats which translate, with respect to lower-to-higher permeability transition, into

smaller drops flow to larger pore throats, whereas, for the higher-to-lower permeability

transition, it translates to larger emulsion droplets travelling to smaller pore throats. As

a result, the case of higher-to-lower permeability transition showed more flow

impairment and larger injection pressure rise. This explains the observed remarkable

difference in the injection pressure profile between the two cases.

The effluents of the ASP flood were analysed for ASP components concentrations in the

produced fluids. Although, the accuracy of the determination is potentially

compromised due to the multiphase nature of the effluents and possible interferences

from the emulsion, the concentration profiles revealed the general trend and that the

higher-to-lower permeability transition trapped more of the ASP chemicals.

In the course of this investigation, it was found necessary to build an IFT cell that could

estimate ultra low IFT. The performance of the in-house-made cell was cross-checked

with the verified spinning drop technique. Reasonable agreement was found between

the two methods for surfactant concentration above 0.02% (w/v). The two methods

diverge below this concentration. This cell was used to estimate the IFT of ASP/Oil 3

system. This setup could be of value to researchers who deal with transparent oils where

only an estimation of IFT is needed.

Polymer adsorption was found to impact on the relative permeability. When deionised

water was injected following the ASP slug, the relative permeability to water changes to

a lower value. When the ASP slug was injected continuously, there was no observed

change in the relative permeability. Therefore, the polymer should be used to drive the

ASP slug, not only for the mobility control but also to reduce the influence of adsorbed

polymer on the relative permeability to water. Perhaps, in some cases it is desired to

reduce the relative permeability to water, however, from this work this may reduce the

221

recovery by blocking the flow in some localities within the porous medium and divert it

to other localities.

7.2 Future Work

• Test Heterogeneity in Cores (Chapter 5)

Testing the effects of longitudinal heterogeneity on ASP flooding experiments in 3D

physical model (cores or thick sand layered sand packs) would probably produce

different outcome to that of the narrow sand packs (1D). The pressure response could be

different, the emulsion and the ASP slug could have more alternative flow paths in the

wider cores. In fact such tests were planned and several specially fabricated cores were

made, unfortunately, critical time limitation has led to cancel these core floods. Initial

evaluations of the use of specially fabricated cores to test the ASP performance in this

study was reported in Society of Petroleum Engineers (SPE) paper 129622. More cores

with definite boundaries were made but never used due to time limitations.

• Influence of the Pore Throats Size on the In-situ Generated Emulsion (Chapter 6)

The ASP process generated an emulsion and it was shown that some relationship exists

between the emulsion mean size and permeability (hence pore throats) as discussed in

Chapter 6. More work is needed to investigate this relationship. In order to investigate

this possible relationship pore throat size and the produced emulsion size in ASP

process, the following experimental procedure is suggested:

1- Make several short packs of sands or glass beads with known grain size

distribution.

2- Measure the pore throat size distribution by mercury injection or X-Ray CT, this

distribution should be compared to droplet size distribution of the emulsion

produced from the chemical floods.

3- Follow the injection sequence described in Section 5.6.2.

4- Collect the emulsion and determine its size by NMR-PFG-STE as described in

Chapter 6. Microscopy could be considered to estimate the size if the NMR-

PFG-STE limited of size is crossed.

5- Make a cross plot between the emulsion mean size and the pore throat size and

realise if any correlation exists.

222

6- Injection sequence and chemical concentration could have some effects on the

IFT and the size of the produced emulsion. Few runs to investigate these factors

will probably be fruitful.

• Investigation of the Two Coloured Emulsion (Chapter 4) In Chapter 4, it was reported the observation of two coloured emulsion (white and

brown) in one of the salinity scans test tubes. The emulsions persisted several months.

Towards the end of this PhD, the brownish emulsion disappeared while the white

emulsion persisted. It is suggested to check the reproducibility of such emulsions, and

conduct further characterisation of the two emulsions including finding the type of the

brown emulsion (o/w) or (w/o) as well as the droplet size distribution of these

emulsions.

• Further Work on the IFT cell (Chapter 4)

The design of in-house-made IFT cell which was described in Chapter 4 could be

improved to measure IFT of darker oils. Perhaps, a cell with shorter path length

between the glass windows and the use of brighter illumination could allow better

performance with darker oils.

• Improvement of Brilliant Green Method (Chapter 3)

In Chapter 3, the use of brilliant green to determine the surfactant concentration could

be improved by removing the emulsion with chloroform. The emulsion caused

interference with the method described in Chapter 3. Removal of the emulsion from

samples before mixing the surfactant samples with the reagent solution could eliminate

this unwanted interference.

223

8 References

Ahemd, T. (2001). Reservoir Engineering Handbook. 2nd Edition. Gulf Professional

Publishing; Texas, USA. Ahmed, M.S. and Arnold, F.C. (1989). Simulation of Alkaline Flooding Using

Fractional Flow Theory. Journal of Petroleum Science and Engineering, Vol. 3, pp. 137-145.

Aichele, C.P., Flaum, M., Jiang, T., Hirasaki, G.J. and Chapman, W.G. (2007). Water In Oil Emulsion Droplet Size Characterization Using a Pulsed Field Gradient with Diffusion Editing (PFG-DE) NMR Technique. Journal of Colloid and Interface Science, Vol. 315, No. 2, 15 November 2007, pp. 607-619.

Al-Adawy, M.S., Nandyal, M. (1991). Status and Scope for EOR Development in Oman. Presented at the SPE Middle East Oil Show, 16-19 November 1991, Bahrain. SPE paper 21407-MS.

Alexander, G.B., Heston, W.M. and Iler R.K., (1954). The Solubility of Amorphous Silica in Water. J. Physical Chemistry, Vol. 58, No. 6, pp. 453-455.

Al-Mutairi, S.M. and Kokal, S.K. (2011). EOR Potential in the Middle East: Current and Future Trends. Presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, 23-26 May, Vienna, Austria. SPE paper 143287-MS.

Arihara, N., Yoneyama, T., Akita, Y. and Lu, X.-G., (1999). Oil Recovery Mechanisms of Alkali-Surfactant-Polymer Flooding. Presented at SPE Asia Pacific Oil and Gas Conference and Exhibition, 20-22 April, Jakarta, Indonesia. SPE paper 54330-MS.

Arriola, A., Willhite, G.P. and Green, D. W., (1983). Trapping of Oil Drops in a Noncircular Pore Throat and Mobilization Upon Contact With a Surfactant. SPE Journal. Vol. 23, No. 1. pp. 99-114. SPE paper 9404-PA.

Atkinson, H. (1927). Recovery of Petroleum from Oil Bearing Sands,’” U.S. Patent No. 1,651,311. (Nov. 29, 1927).

Austad, T. and Milter, J., (2000). Surfactant Flooding in Enhanced Oil Recovery. In: Schramm, L. L. (Ed.). Surfactants Fundamentals and Applications in the Petroleum Industry. 1ed. Cambridge University Press; Cambridge, UK. pp. 203-249.

Austad, T. and Strand, S., (1996). Chemical Flooding of Oil Reservoirs: 4. Effects of Temperature and Pressure on the Middle Phase Solubilization Parameters Close to Optimum Flood Conditions. Colloid and Surfaces A: Physicochemical and Engineering Aspects, Vol. 108, pp. 243-252.

Austad, T., and Taugbøl, K., (1995). Chemical Flooding of Oil Reservoirs: 2.Dissociative Surfactant-Polymer Interaction with a Negative Effect on Oil Recovery. Colloid and Surfaces A: Physicochemical and Engineering Aspects, Vol. 103, pp. 73-81.

Bavière, M and Canselier, J.P., (1997). Microemulsion in the Chemical EOR Process. In: Solans, C. and Kunieda, H. (Eds.). Industrial Applications of Microemulsions. M. Dekker; New York, USA. pp. 331-354.

Baviere, M., Ruaux, E., Defives, D., (1993). Sulfonate Retention by Kaolinite at High pH - Effect of Inorganic Anions. Journal of SPE Reservoir Engineering, Vol. 8, No. 2. pp 123-127. SPE paper 21031-PA.

Beazley, P.M., (1985). Quantitative Determination of Partially Hydrolyzed Polyacrylamide Polymers in Oil Field Production Water. Analytical Chemistry, Vol. 57, No. 1, pp. 2098-2101.

224

Berger, P.D. and Lee C.H., (2006). Improved ASP Process Using Organic Alkali. Presented in SPE/DOE Symposium on Improved Oil Recovery, 22-26 April, Tulsa, Oklahoma, USA. SPE paper 99581-MS.

Bhuyan, D., (1989). Development of an Alkaline/Surfactant/Polymer Compositional Reservoir Simulator. PhD thesis, the University of Texas at Austin, USA.

Bhuyan, D., Lake, L.W., Pope, G.A. (1990).A Mathematical Modelling of High pH Chemical flooding. SPE Reservoir Engineering, Vol. 5, No. 2, pp. 213-220. SPE paper 17398-PA.

Bhuyan, D., Pope, G.A., and Lake, L.W., (1991).Simulation of High-pH Coreflood Experiments Using a Compositional Chemical Flood Simulator. Presented at the SPE International Symposium on Oilfield Chemistry, 20-22 February 1991, Anaheim, California, USA. SPE paper 21029-MS.

Blasco, J., Hampel, M. and Moreno-Carrido, I., (2003).Toxicity of Surfactants. In: Knepper, T. P., de Voogt, P., and Barcelo, D. (Eds.). Analysis and Fate of Surfactants in the Aquatic Environment. Elsevier Science; Amsterdam, Netherlands. pp. 827-925.

Bloch, F., (1946). Nuclear Induction. Physical Review, Vol. 70, No. 7 and 8, pp. 460. Borgund, A.E., Erstad, K., and Barth, T., (2007). Fractionation of Crude Oil Acids by

HPLC and Characterization of Their Properties and Effects on Gas Hydrate Surfaces. Energy & Fuels, Vol. 21, pp. 2816-2826.

Bourrel, M. and Schechter, R.S., (1988): Microemulsion and Related Systems. Marcel Dekker Inc; New York, USA.

Braithwaite, A. and Smith, F.J., (1996). Chromatographic Methods. Fifth Edition. Blackie Academic & Professional; London and Melbourne.

Breit, V.S., Mayer, E.H., and Carmichael, J.D., (1979).An Easily Applied Black Oil Model of Caustic Waterflooding. Presented at the SPE California Regional Meeting, 18-20 April, Ventura, California, USA. SPE paper 7999-MS.

Britton, L.N., (2000). Toxicity and Persistence of Surfactants Used in the Petroleum Industry. In: Schramm, L. L. (Ed.). Surfactants Fundamentals and Applications in the Petroleum Industry. 1ed. Cambridge University Press; Cambridge, UK. pp. 541-565.

Cao, G., Liu, H., Shi, G., Wang, G., Xiu, Z., Ren, H. and Zhang, Y. (2007). Technical Breakthrough in PCPs’ Scaling Issue of ASP Flooding in Daqing Oil Field. Presented at SPE Annual Conference and Exhibition, California, USA, 11-14 November. SPE paper 109165.

Carr, H.Y., and Purcell, E.M., (1954). Effects of Diffusion on Free Precession in Nuclear Magnetic Resonance Experiments. Physical Review, Vol. 94, No. 3, pp. 630.

Caulfield, M.J., Hao, X., Qiao, G.G., Solomon, D.H., (2003). Degradation on Polyacrylamides. Part II. Polyacrylamide gels. Polymer, Vol. 44, No. 14, pp. 3817-3826.

Chang, H.L., Zhang, Z.Q., Wang, Q.M., Xu, Z.S., Guo, Z.D., Sun, H.Q., Cao, X.L., and Qiao, Q. (2006). Advances in Polymer Flooding and Alkaline/Surfactant/Polymer Processes as Developed and Applied in the People’s Republic of China. Journal of Petroleum Technology. Vol. 58, No. 2, pp. 84-89. SPE paper 89175.

Chatzis, I., Morrow, N.R., and Lim, H.T. (1983). Magnitude and Detailed Structure of Residual Oil Saturation. SPE Journal, Vol. 23, No. 2. pp.311-326. SPE paper 10681-PA.

Chessick, J. H., Healey, F. H., and Zettlemoyer, A. C. (1956). Adsorption and Heat of Wetting Studies of Teflon. The Journal of Physical Chemistry, Vol. 60, No. 10, pp. 1345-1347.

225

Christian, G. D., (1994). Analytical Chemistry. Fifth Edition. John Wiley & Sons, Inc. USA.

Coates, G. R., Xiao, L., and, Prammer, M. G., (1999). NMR Logging Principles and Applications. Halliburton Energy Services, Houston, USA.

Dandekar, A.Y.,(2006). Petroleum Reservoir Rock and Fluid Properties. Taylor and Francis: Boca Raton, Florida, USA.

de Gennes, P-G., Brochard-Wyard, F., and Quere, D. (2004).Capillarity and Wetting Phenomena: Drops, Bubbles, Pearls, Waves. Axel Reisinger (Translator). Springer; New York, USA.

Dean, J. A. (1992). Lange's Handbook of Chemistry. Fourteenth Edition. McGraw-Hill, Inc; United States of America.

Delshad, M., Asakawa, K., Pope, G., Sepehrnoori, K., (2002). Simulations of Chemical and Microbial Enhanced Oil Recovery Methods. Presented in the SPE/DOE Improved Oil Recovery Symposium, 13-17 April, Tulsa, Oklahoma, USA. SPE paper 75237-MS.

Delshad, M., Han, C., Veedu, F.K., and Pope, G.P. (2011). A Simplified Model for Simulations of Alkaline-Surfactant-Polymer Floods. Presented in the SPE Reservoir Simulation Symposium, 21-23 February 2011, the Woodlands, Texas, USA. SPE paper 142105-MS.

Deng, S., Bai, R, Chen, J.P., Yu, G., Jiang, Z., Zhou, F. (2002). Effects of Alkaline/Surfactant/Polymer on Stability of Oil Droplets in Produced Water from ASP Flooding. Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol. 211, Issues 2-3, pp. 275-284.

Department of Industry and Resources; State of Western Australia. (2008), Western Australian Oil and Gas Review 2008, [online], Available from: http://www.dmp.wa.gov.au/documents/OilandGasReview2008t(1).pdf

DeZabala, E.F., Vislocky, J.M., Rubin, E. and Radke, C.J., (1982). A Chemical Theory for Linear Alkaline Flooding. SPE Journal, Vol. 22, No. 2, pp. 245-258. SPE Paper 8997-PA.

Dokla, M.E., (1981). Effect of Formation Heterogeneity and Temperature on Alkaline Waterflooding Performance. Presented at the Middle East Technical Conference and Exhibition of the SPE, 9-12 March, Manama, Bahrain. SPE paper 9646.

Doll, T. E., (1988).An Update of the Polymer-Augmented Alkaline Flood at the Isenhour Unit, Sublette County, Wyoming. Journal of SPE Reservoir Engineering, Vol. 3, No. 2, May 1988, pp. 604-608. SPE paper 14954.

Donaldson, E.C. and Alam, W., (2008).Wettability. Gulf Publishing Company; Texas, USA.

Duxbury, D.F., (1993). Photochemistry and Photophysics of Triphenylmethane Dyes in Solid and Liquid Media. Chemical Reviews. Vol. 93, No. 2, pp. 381-433.

Ehrenberg, S. N. and Nadeau, P. H., (2005). Sandstone vs. Carbonate Petroleum Reservoirs: A Global Perspective on Porosity-Depth and Porosity-Permeability Relationships. American Association of Petroleum Geologists Bulletin, Vol. 89, No. 4, pp. 435-445.

Epton, S. R., (1948). A New Method for the Rapid Titrimetric Analysis of Sodium Alkyl Sulphates and Related Compounds. Transactions of the Faraday Society, Vol. 44, pp. 226 – 230.

Esaki, T., Zhang, M., Takeshita, A., and Mitani, Y., (1996). Rigorous Theoretical Analysis of a Flow Pump Permeability Test. Geotechnical Testing Journal, GTJODJ, Vol. 19, No. 3, pp. 241-246.

Fan, T. and Buckley, J. S., (2007). Acid Number Measurements Revisited. SPE Journal, Vol. 12, No. 4, pp. 496-500. SPE paper 9988.

226

Farrar, T. C. and Becker, E.D., (1971). Pulse and Fourier Transform NMR: Introduction to Theory and Methods. Academic Press; New York, USA.

Flaaten, A.K., Nguyen, Q. P., Pope, G.A and Zhang, J., (2009). A Systematic Laboratory Approach to Low-Cost, High-Performance Chemical Flooding. SPE Reservoir Evaluation & Engineering. Vol. 12, No. 5. October 2009. pp. 713-723. SPE paper 113469-PA.

Flaaten, A.K., Nguyen, Q.P, Zhang, J., Mohammadi, H., and Pope, G.A., (2010). Alkaline/Surfactant/Polymer Chemical Flooding Without the Need for Soft Water. SPE Journal, Vol. 15, No. 1, pp. 184-196. SPE paper 116754-PA.

Fox, P. J., and Zhu, Y., (1999). Discussion on (‘Theoretical Evaluation of the Transient Response of Constant Head and Constant Flow-Rate Permeability Tests’ by M. Zhang, M. Takahashi, R. H. Morin, and T. Esaki), Geotechnical Testing Journal, GTJODJ, Vol. 22, No. 4, pp.356-357.

French, T.R., (1996). A Method for Simplifying Field Application of ASP Flooding. Presented in SPE/DOE Improved Oil Recovery Symposium, 21-24 April, Tulsa, Oklahoma, USA. SPE paper 354354-MS.

Gao, L. and McCarthy, T.J., (2008). Teflon is Hydrophilic Comments on Definitions of Hydrophobic, Shear versus Tensile Hydrophobicity, and Wettability Characterization. Langmuir. Vol. 24, No. 17, pp. 9183-9188.

Glover, C.J., Puerto, M.C., Maerker, J.M., and Sandvik, E.L., (1979). Surfactant Phase Behavior and Retention in Porous Media. SPE Journal, Vol. 19, No. 3, pp. 183-193.

Graham, B.F., May, E.F., and Trengove, R.D., (2008). Emulsion Inhibiting Components in Crude Oils. Energy and Fuels, vol.22, pp. 1093-1099.

Graue, D.J., Johnson Jr., C.E., (1974). Field Trial of Caustic Flooding Process. Journal of SPE Reservoir Engineering, Vol. 26, No. 12, December 1974, pp. 1353-1358. SPE paper 4740-PA.

Green, D.W. and Willhite G. P., (1998). Enhance Oil Recovery. Society of Petroleum Engineers Text Book Series. Richardson; Texas, United States of America.

Guo, B., Schechter, D.S., (1997). A Simple and Accurate Method for Determining Low IFT from Pendant Drop Measurements. International Symposium on Oilfield Chemistry, 18-21 February, Houston, Texas, USA. SPE paper 37216.

Guo, C., Wang, S., Liu, H., Feng, L., Song, Y. And Jiang, L., (2009). Wettability Alteration of Polymer Surfaces Produced by Scraping. In: Carre, A. and Mittal K.L. (Eds.) Superhydrophobic Surfaces. Koninklijke Brill NV; Leiden, The Netherlands.

Guo, J., Liu, Q., Li, M., Wu, Z. and Christy, A.A., (2006). The Effect of Alkali on Crude Oil/Water Interfacial Properties and the Stability of Crude Oil Emulsion. Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol. 273, pg 213-218.

Gupta, A. D., Pope, G. A., Sepehrnoori, K. and Shook, M. (1988). Effects of Reservoir Heterogeneity on Chemically Enhanced Oil Recovery. SPE Reservoir Engineering. Vol. 3, No. 2, pp. 479-488. SPE paper 14889-PA.

Hagel, L. and Janson, J.C., (1992).Size Exclusion Chromatography. In: Heftmann, E. (Ed.). Chromatography: fundamentals and applications of chromatography and related differential migration methods, Part A. Fundamentals and techniques. Fifth Edition. Journal of chromatography library; Vol. 51A-B. Elsevier; Amsterdam, the Netherlands. pp. A267-A307.

Hahn, E.L., (1950). Spin Echoes. Physical Review, Vol. 80, No. 4, pp.580. Hammond, C., Sasol North America Inc, (2011). Privat Communications. Haq, B., UWA, (2010). Private Communications.

227

Haskett, C. E., E.A. Polumbus, Jr. , Tartera, M., (1965). A Practical Solution to the Problem of Asphaltene Deposits-Hassi Messaoud Field, Algeria. Journal of Petroleum Technology. Vol. 17, No. 4. pp. 387-391. SPE paper 994-PA.

Healy, R. N. and Reed, R.L., (1974). Physicochemical Aspects of Microemulsion Flooding. SPE Journal, Vol. 14, No. 4, pp 491-501. SPE paper 4583-PA.

Healy, R.N., Reed, R.L. and Stenmark, D.G. (1976). Multiphase Microemulsion Systems. SPE Journal, June, pp. 147-160. SPE paper 5565.

Hill, H. J. and Lake, L.W., (1978). Cation Exchange in Chemical Flooding: Part 3-Experimental. SPE Journal, Vol. 18, No. 6, pp. 445-456. SPE paper 6770-PA.

Hirasaki, G. and Zhang, D.L., (2004). Surface Chemistry of Oil Recovery from Fractured, Oil-Wet, Carbonate Formations. SPE Journal, Vol. 9, No. 2, pp. 151-162.

Hirasaki, G. J., Miller, C. A. and Pope, G. A., (2006). Surfactant Based Enhanced Oil Recovery and Foam Mobility Control. 3rd Annual & Final Technical Report, July. DOE contract number FC26-03NT15406, DOI: 10.2172/901729, DOE, Washington, DC, US.

Hirasaki, G.J., Miller, C.A. and Puerto, M., (2008). Recent Advances in Surfactant EOR. Presented at the SPE Annual Technical Conference and Exhibition, Colorado, USA. SPE paper 115386-MS-P.

Hollingsworth, K.G. and Johns, M.L., (2003). Measurement of Emulsion Droplet Sizes Using PFG NMR and Regularization Methods. Journal of Colloid and Interface Science, Vol. 258, No. 2, pp. 383-389.

Holtzhauer, M., (2006). Basic Methods for Biochemical Lab. Springer- Verlag Berlin and Heidelberg GmbH & Co. KG; Dordrecht, Germany.

Hornof, V., Neale, G.H. and Gholam-Hosseini, M., (2000). Effects of Flow Rate and Alkali-to-Acid Ratio on the Displacement of Acidic Oil by Alkaline Solutions in Radial Porous Media. Journal of Colloid and Interface Science, Vol. 231, pp. 196–198.

Hou, J.-R., Zhang, S.-f., Yang, J.-Z., and Liu, Z.-C., (2005). Chemical Agent Loss and Effective Distance Of Ultra Low IFT In ASP Flooding. Journal- Dalian University of Technology. Vol. 45; No. 4. pp. 496-500. (in Chinese)

Huh, C. and Reed, R.L., (1982). A Method for Estimating Interfacial Tensions and Contact Angles from Sessile and Pendant Drop Shapes. Journal of Colloid and Interface Science, Vol. 91, No. 2, pp.472-484.

Huh, C., (1979). Interfacial Tensions And Solubilizing Ability of a Microemulsion Phase That Coexists with Oil and Brine. Journal of Colloid and Interface Science, Vol. 71, No. 2, pp. 408-426.

Iglauer, S., Wu, Y., Shuler, P., Tang, Y., Goddard, W.A., (2010). New Surfactant Classes for Enhanced Oil Recovery and Their Tertiary Oil Recovery Potential. Journal of Petroleum Science and Engineering, Vol. 71, pp. 23–29.

Jennings, H.Y. Jr., (1975). A Study of Caustic Solution/Crude Oil Interfacial Tensions. SPE Journal, Vol. 15, No. 3, pp. 197-202., SPE paper 5049-PA.

Jensen, J.L. and Lake, L. W., (1988). The Influence of Sample Size and Permeability Distribution on Heterogeneity Measures. Journal of SPE Reservoir Engineering. Vol. 3, No. 2. pp. 629-637.

Johns, M.L., (2009). NMR studies of emulsion. Current Opinion in Colloid and Interface Science, Vol. 14, pp. 178-183.

Johnson, C.E. Jr., (1976). Status of Caustic and Emulsion Methods. Journal of Petroleum Technology. Vol. 28, No. 1, pp. 85-92. SPE paper 5561-PA.

Kang, W., Liu, Y., Qi, B. , Liao, G., Yang, Z., and Hong, J., (2000). Interactions Between Alkali/Surfactant/Polymer and Their Effects on Emulsion Stability.

228

Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol. 175, pp.243-247.

Karukstis, K.K. and Gulledge, A.V., (1998). Analysis of the Solvatochromic Behavior of the Disubstituted Triphenylmethane Dye Brilliant Green. Analytical Chemistry. Vol. 70, No. 19, pp. 4212-4217.

Kay-Shoemake J.L.; Watwood M.E.; Sojka R.E.; Lentz R.D., (1998). Polyacrylamide as a Substrate for Microbial Amidase in Culture and Soil. Soil Biology and Biochemistry, Vol. 30, No. 13, pp. 1647-1654(8).

Kennedy, G.C., (1950). A Portion of the System Water-Silica. Economic Geology. Vol. 45, No. 7, pp. 629-653.

Kokal, S.I., Maini, B.B. and Woo, R., (1992). Flow of Emulsions in Porous Media. In: Schramm, L. L. (Ed.). Emulsions Fundamentals and Applications in the Petroleum Industry. 1ed. American Chemical Society; Washington, D.C., USA. pp. 219-262.

Koning, E.J.L, Mentzer, E., and, Heemskerk, J., (1989). Evaluation of a Pilot Polymer Flood in the Marmul Field, Oman. SPE Annual Technical Conference and Exhibition, 2-5 October, Houston, Texas. SPE paper 18092-MS.

Krauskopf, K. B., and Bird, D., K., (1995). Introduction to Geochemistry. Third Edition. McGraw-Hill, inc; New York, USA.

Lake, L. W. and Jensen, J.L., (1989). A Review of Heterogeneity Measures Used in Reservoir Characterization. Unsolicited at SPE eLibrary. SPE paper 20156. [online], Available from: http://www.onepetro.org/mslib/app/Preview.do?paperNumber=00020156&societyCode=SPE

Lake, L.W., (1989). Enhanced Oil Recovery. Prentice-Hall; Englewood Cliffs, New Jersey, USA.

Lambert, J. L., (1951A). Linear Starch Reagent: Cadmium Iodide-Linear Starch Reagent. Analytical. Chemistry, Vol. 23, pp.1247.

Lambert, J. L., (1951B). Linear Starch Reagent: Linear Starch-Iodate Reagent Selective for Iodide Ion. Analytical. Chemistry, Vol. 23, pp. 1251.

Lei, Z.-D., Yuan, S.-Y., Song, J., (2008). Rheological Behavior of Alkali-Surfactant-Polymer/Oil Emulsion in Porous Media. Journal of Central South University of Technology. Vol. 15, No. 1, pp. 462-466.

Levitt, D.B., Jackson, A.C., Heinson, C., Britton, L.N., Malik, T., Dwarakanath, V., and Pope, G.A. (2009). Identification and Evaluation of High-Performance EOR Surfactants. SPE Reservoir Evaluation and Engineering, Vol. 12, No. 2. pp. 243-253. SPE paper 100089-PA.

Levitt, D.B., Pope, G.A., and Jouenne, S. (2011). Chemical Degradation of Polyacrylamide Polymers Under Alkaline Conditions. SPE Reservoir Evaluation and Engineering. Vol. 14, No. 3. pp. 281-286. SPE paper 129878-PA.

Li, D., Shi, M, Wang, D., and Li, Z., (2009). Chromatographic Separation of Chemicals in Alkaline Surfactant Polymer Flooding in Reservoir Rocks in the Daqing Oil Field. Presented in SPE International Symposium on Oilfield Chemistry, 20-22 April 2009, The Woodlands, Texas, USA. SPE paper 121598-MS.

Li, Y., Abriola, L.M., Phelan, T.J., Ramsburg, C.A., and Pennell, K.D. (2007). Experimental and Numerical Validation of the Total Trapping Number for Prediction of DNAPL Mobilization. Environmental Science & Technology, Vol. 41, No. 23, pp. 8135-8141.

Liauh, W.C., Duda, J.L. and Klaus, E.E., (1982). An Investigation of the Inaccessible Pore Vol. Phenomena. In: Wasan, D. and Payatakes. (Eds.). Interfacial Phenomena in Enhanced Oil Recovery. American Institute of Chemical Engineering Symposium Series 212, Vol. 78, pp. 70-76.

229

Lin, F-M. C., (1995). Size Exclusion Chromatography of Acrylamide Homopolymer and Copolymers. In: Chi-san Wu (Ed.). Handbook of size exclusion chromatography. Dekker: New York, USA. pp. 270.

Lin, S-Y., and Hwang, H-F., (1994). Measurement of Low Interfacial Tension by Pendant Drop Digitization. Langmuir, Vol. 10, No. 12, pp. 4703-4709.

Liu, S., (2007). Alkaline Surfactant Polymer Enhanced Oil Recovery Process. PhD Thesis, Rice University, USA.

Liu, S., Li, R.F, Miller, C.A., Hirasaki, G.J., (2010). Alkaline/ Surfactant/ Polymer Processes: Wide Range of Conditions for Good Recovery. SPE Journal, Vol. 15, No. 2. pp. 282-293. SPE paper 113936-PA.

Liu, S., Zhang, D. L., Yan, W., Puerto, M., Hirasaki, G. J. and Miller, C.A. (2008). Favorable Attributes of Alkaline-Surfactant-Polymer Flooding. SPE Journal. Vol. 13, No. 1. March 2008. pp. 5-16. SPE paper 99477-PA.

Liu, Z., Yue, X., Hou, J., and Zhang, L., (2002). Comparison of Displacement Oil Mechanism of Polymer, ASP and Foam of ASP in Micro Pores and Dead Ends of Pores. Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, 8-10 October, Melbourne, Australia. SPE paper 77876.

Longman, G. F., (1975). The Analysis of Detergents and Detergent Products. Wiley & Sons. UK.

Lu, J., Wu, L., and Gan, J., (2003). Determination of Polyacrylamide in Soil Waters by Size Exclusion Chromatography. Journal of Environmental Quality Vol. 32, pp. 1922-1926.

Ma, S., Dong, M., Li, Z. and Shirif, E., (2007). Evaluation of the Effectiveness of Chemical Flooding Using Heterogeneous Sandpack Flood Test. Journal of Petroleum Science and Engineering, Vol. 55, pp. pp. 294-300.

Maachou, A. (1982).OAPEC and Arab Petroleum. Antony Melville (translator). Berger-Levrault; Paris, France.

Malcolm, J.D., and Elliott, C.D., (1980). Interfacial Tension from Height and Diameter of a Single Sessile Drop or Captive Bubble. Canadian Journal of Chemical Engineering. Vol. 58, pp. 151-153.

Masahiko, A., (1997). Microemulsion in Enhanced Oil Recovery: Middle-Phase Microemulsion Formation with Some Typical Anionic Surfactants. In: Solans, C. and Kunieda, H. (Eds.). Industrial Applications of Microemulsions. M. Dekker; New York, USA. pp. 279-304.

McAuliffe, C.D., (1973). Oil-in-Water Emulsions and Their Flow Properties in Porous Media. Journal of Petroleum Technology, Vol. 25, No. 6, pp. 727-733. SPE paper 4369.

Minssieux, L., (1997). Core Damage from Crude Asphaltene Deposition. Presented at the SPE International Symposium on Oilfield Chemistry, 18-21 February, SPE paper 0037250-MS.

Mitani, S. and Sakai, K., (2002). Measurement of Ultralow Interfacial Tension with a Laser Interface Manipulation Technique. Physical Review E, Vol. 66, No. 3, pp. 031604.

Mohammadi, H., Delshad, M., and Pope, G.A., (2009). Mechanistic Modeling of Alkaline/Surfactant/Polymer Floods. SPE Reservoir Evaluation & Engineering, Vol. 12, No. 4. SPE paper 110212-PA.

Moore, R.B. and Martin, C.R., (1986). Procedure for Preparing Solution-Cast Perfluorosulfonate Ionomer Films and Membranes. Analytical Chemistry, Vol. 58, pp. 2569-2570.

Morrow, N.M., (1991). Introduction to Interfacial Phenomena in Oil Recovery. In: Morrow, N.M (Ed). Interfacial Phenomena in Petroleum Recovery. 1ed. Marcel Dekker, Inc; New York, United States of America. pp. 1-21.

230

Mucci, L A, Dickman, P W, Steineck, G, Adami, H.-O., Augustsson, K., (2003). Dietary Acrylamide and Cancer of the Large Bowel, Kidney, and Bladder: Absence of an Association in a Population-Based Study in Sweden. British Journal of Cancer, Vol. 88, No. 1. pp. 84-89.

Murday, J.S. and Cott, R.M. (1968). Self-Diffusion Coefficient of Liquid Lithium. Journal of chemical Physics, Vol. 48, pp. 4938.

Murugan, R., Mohan, S., and Bigotto, A., (1998). FTIR and Polarised Raman Spectra of Acrylamide and Polyacrylamide. Journal of Korean Physical Society, Vol. 32, No. 4, pp. 505.

Nasr-El-Din, H.A., (1992). Fluid Dynamics of Oil-Water- Sand Systems. In: Schramm, L. L. (Ed.). Emulsions Fundamentals and Applications in the Petroleum Industry. 1ed. American Chemical Society; Washington, D.C., USA. pp. 171-217.

Nasr-El-Din, H.A., Hawkins, B.F. and Green, K.A., (1992). Recovery of Residual Oil Using Alkali/Surfactant/Polymer Process: Effect of Alkali Concentration. Journal of Petroleum Science and Engineering, Vol. 6, pp. 381-401.

Needham, R.B. and Doe, P.H., (1987). Polymer Flooding Review. Journal of Petroleum Technology. Vol. 39, No. 12. pp. 1503-1507. SPE paper 17140-PA.

Nelson, R.C and Pope, G.A., (1978). Phase Relationships in Chemical Flooding. SPE Journal. Vol. 18, No. 5., pp. 325-338. SPE paper 6773-PA.

Neumann, C.H., (1974). Spin Echo of Spins Diffusing in a Bounded Medium. Journal of chemical Physics, Vol. 60, pp. 4508.

Nguyen, D., Sadeghi, N., and Houston, C., (2011). Emulsion Characteristics and Novel Demulsifiers for Treating Chemical EOR Induced Emulsions. Presented in the SPE Enhanced Oil Recovery Conference, 19-21 July, Kuala Lumpur, Malaysia. SPE paper 143987-MS.

O’Rourke, A. and MacLoughlin, P.F., (2005). A Comparison of Measurement Techniques Used in the Analysis of Evolving Liquid-Liquid Dispersions. Chemical Engineering and Processing, Vol 44, pp. 885–894.

Ogunberu, L., and Asghari, K., (2005).Water Permeability Reduction Under Flow-Induced Polymer Adsorption. Journal of Canadian Petroleum Technology, Vol. 44, No. 11., pp. 56-61.

Packer, K.J. and Rees, C., (1972). Pulsed NMR studies of restricted diffusion. I. Droplet size distributions in emulsions. Journal of Colloid and Interface Science, Vol. 40, No. 2, pp. 206-218.

Padday, J. F., (1969). Surface Tension. Part I. The Theory of Surface Tension. In Matijević, E. (Ed) Surface and Colloid Science, Vol. 1. John Wiley & Sons. USA. pp. 101-251.

Palma, R. J., Barad, J. and Palkowetz, J. M., (1984). Field Assay of Polyacrylamide in Drilling Fluids. J. Analytical Letters, Vol. 17, pp. 897 - 904.

Park, Y.S. (1976). Oil Money and the World Economy. Wilton House Publications; London, UK.

Pena, A.A. and Hirasaki, G. J., (2006). NMR Characterization of Emulsions. In: Sjöblom, J. (Ed.). Emulsions and emulsion stability. 2nd Edition. Taylor & Francis; Boca Raton, USA. pp. 283-309.

Pobiner, H. and Hoffman Jr, H.T., (1982). Spectrophotometric Determination of Anionic Surfactants by Conversion of the Leuco-Base of Triphenylmethane Dyes. Analytica Chimica Acta, Vol. 141, pp. 419-425.

Pois, P.D., Agterof, W.G.M., (1985). Qualitative and Quantitative Analysis by High-Performance Liquid Chromatography of Synthetic Alkylaryl Sulfonates: Surfactants for Potential Application in Enhanced Oil Recovery. Presented at

231

SPE Oilfield and Geothermal Chemistry Symposium, 9-11 March, Phoenix, Arizona. SPE paper 13571-MS.

Poteau, S. and Argillier, J-F., Langevin, D., Pincet, F. and Perez, E., (2005). Influence of pH on Stability and Dynamic Properties of Asphaltenes and Other Amphiphilic Molecules at the Oil-Water Interface. Energy & Fuels, Vol. 19, pp. 1337-1341.

Preston, W.J., (1948). Some Correlating Principles of Detergent Action. The Journal of Physical and Colloid Chemistry B, Vol. 52, No. 1, pp. 84-97.

Qian, K., Winston K. Robbins, W.R., Hughey, C.A., Cooper, H.J., Rodgers, R.P. and Marshall, A.G., (2001). Resolution and Identification of Elemental Compositions for More than 3000 Crude Acids in Heavy Petroleum by Negative-Ion Microelectrospray High-Field Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Energy & Fuels, Vol. 15, pp. 1505-1511.

Qu, Z., Zhang, Y., Zhang, X., Dai, J., (1998). A Successful ASP Flooding Pilot in Gudong Oil Field. Presented at SPE/DOE Improved Oil Recovery Symposium, 19-22 April, Tulsa, Oklahoma, USA. SPE paper 39613.

Ramakrishnan, T.S. and Wasan, D.T., (1983). A Model for Interfacial Activity of Acidic Crude Oil/Caustic Systems for Alkaline Flooding. SPE Journal, Vol. 23, No. 4, pp. 602-612. SPE paper 10716-PA.

Rivas, H., Gutiérrez, X., Ziritt, J.L., Antón, R.E., and Salager, J. (1997). Microemulsion and Optimum Formulation Occurrence in pH- Dependent Systems as Found in Alkaline-Enhanced Oil Recovery. In: Solans, C. and Kunieda, H. (Eds,). Industrial Applications of Microemulsions. M. Dekker; New York, USA. pp. 305-330.

Rosen, M.J, Wang, H., Shen, P and Zhu, Y., (2005). Ultralow Interfacial Tension for Enhanced Oil Recovery at Very Low Surfactant Concentrations. Langmuir, Vol. 21, pp. 374-3756.

Rosen, M.J., (1989). Surfactants and Interfacial Phenomena. Second Edition. John Wily & Sons. New York, USA.

Rosen, M.J., (2004). Surfactants and Interfacial Phenomena. Third Edition. John Wily & Sons; Hoboken, USA.

Saneie, S. and Yanis C. Y., (1993). Alkaline Assisted Thermal Oil Recovery: Kinetic and Displacement Studies. Topical Report prepared for the USA Department of Energy, Assistant Secretary for Fossil Energy, June 1993, Work Performed Under Contract No. DE-FG22-90BC14600. Prepared by the University of Southern California.

Santos., (2011). Stag Oil Assay Data. [online], Available from: http://www.santos.com/library/stag_crude.pdf

Scheuing, D.R., (1991). Fourier Transform Infrared Spectroscopy in Colloid and Interface Science: An Overview. In: Scheuing, D.R. (Ed.). Fourier Transform Infrared Spectroscopy in Colloid and Interface Science, ACS Symposium Series 447. American Chemical Society, Washington, USA. pp. 1-21.

Schmitt, T.M., (2001). Analysis of Surfactants. Marcel Decker Inc; Hoboken, USA. Schramm, L. L., (1992). Petroleum Emulsion Basic Principles. In: Schramm, L. L. (Ed.)

Emulsions Fundamentals and Applications in the Petroleum Industry. 1ed. American Chemical Society; Washington, D.C., USA. pp. 1-49.

Schramm, L. L., (2005). Emulsions, Foams, and Suspensions: Fundamentals and Applications. John Wiley & Sons, Inc: Hoboken, USA. [Online], Available from: http://uwa.eblib.com.au.ezproxy.library.uwa.edu.au/patron/FullRecord.aspx?p=258875>

232

Schramm, L. L., and Marangoni, D.G., (2000). Surfactant and Their Solutions: Basic Principles. In: Schramm, L. L. (Ed.). Surfactants Fundamentals and Applications in the Petroleum Industry. 1ed. Cambridge University Press; Cambridge, UK. pp. 3-50.

Schramm, L.L, Fisher, D.B., Schürch, S. and Cameron, A., (1995). A Captive Drop Instrument for Surface or Interfacial Tension Measurements at Elevated Temperatures and Pressures, Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol. 94, No. 2-3, pp. 145-159.

Schramm, L.L, Smith, R.G., Stone, J.A., (1984). The Influence of Natural Surfactant Concentration on the Hot Water Process for Recovering Bitumen from the Athabasca Oil Sands; AOSTRA Journal of Research, Vol. 1, pp. 5-13.

Scoggins, M.W. and Miller, J.W., (1975). Spectrophotometric Determination of Water Soluble Organic Amides. Analytical. Chemistry, Vol. 47, No. 1, pp. 152–154.

Scoggins, M.W. and Miller, J.W., (1979). Determination of Water Soluble Polymers Containing Primary Amide Groups Using the Starch-Triiodide Method. SPE Journal, Vol. 19, No. 3, pp. 151-154.

Scriven, L.E., (1976). Equilibrium Bicontinuous Structure. Nature, Vol. 263, pp. 123 - 125 (Issue 09 September 1976).

Shen, P., Wang, J., Yuan, S., Zhong, T. and Jia, X., (2009). Study of Enhanced-Oil-Recovery Mechanism of Alkali/Surfactant/Polymer Flooding in Porous Media From Experiments. SPE Journal. Vol. 14, No. 2. June 2009. pp. 237- 244. SPE paper 126128-PA.

Sheng, J.J., (2010). Modern Chemical Enhanced Oil Recovery: Theory and Practice, Gulf Professional Publishing; USA.

Shepherd, R. G., (1989). Correlations of Permeability and Grain Size Correlations of Permeability and Grain Size. Ground Water, Vol. 27, No. 5, pp. 633-638.

Sherbiny, N.A. and Tessler, M.A.,(1976). Arab Oil Impact on the Arab Counters and Global Implication. In: Sherbiny, N.A. and Tessler. (Eds.). Arab Oil Impact on the Arab Counters and Global Implication. Praeger Publishers; New York, USA, pp. 3-18.

Shook, G.M. and Forsmann, J.H., (2005). Tracer Interpretation Using Temporal Moments on a Spreadsheet. Prepared for the U.S. Department of Energy Assistance Secretary for Energy Efficiency and Renewable Energy, Contract DE-AC07-05ID14517, Geothermal Technologies Program, Idaho National Laboratory, June 2005. [online], Available from both: http://geothermal.inel.gov/software/docs/tracerinterpretation.pdf or http://www.osti.gov/bridge/basicsearch.jsp

Siever, R., (1962). Silica Solubility, 0°-200° C., and the Diagenesis of Siliceous Sediments. The Journal of Geology. Vol. 70, No. 2, pp. 127-150.

Snyder, L.R., (1992). Theory of Chromatography. In: Heftmann, E. (Ed.). Chromatography: fundamentals and applications of chromatography and related differential migration methods, Part A. Fundamentals and techniques. Fifth Edition. Journal of chromatography library; Vol. 51A-B. Elsevier; Amsterdam, the Netherlands. pp. A1-A68

Song, B. and Springer, J., (1996A). Determination of Interfacial Tension from the Profile of Pendant Drop Using Computer-Aided Image Processing: Theoretical. Journal of Colloid and Interface Science, Vol. 184, pp. 46-76. Article No. 0597.

Song, B. and Springer, J., (1996B). Determination of Interfacial Tension from the Profile of Pendant Drop Using Computer-Aided Image Processing: Experimental. Journal of Colloid and Interface Science, Vol. 184, pp. 77-91. Article No. 0598.

233

Soo, H. and Radke, C.J., (1986). A Filtration Model for the Flow of Dilute Stable Emulsions in Porous Media-I. Journal of Chemical Engineering Science, Vol. 41, No. 2, pp. 263-272.

Sorbie, K. S., (1991). Polymer Improved Oil Recovery. Blackie and Sons Ltd; Glasgow, Scotland.

Spiegel, M.R., (1974). Schaum's Outline of Theory And Problems of Fourier Analysis: With Applications to Boundary Value Problems. McGraw-Hill; New York, USA.

Spilker, R., (2005). Determination of Anionic Surfactants. In: Waldhoff, H. and Spilker, R. (Eds.). Handbook of Detergents, Part C: Analysis. CRC Press; USA.

Spinler, E. A. and Baldwin, B.A., (2000).Surfactant Induced Wettability Alteration in Porous Media. In: Schramm, L. L. (Ed.). Surfactants Fundamentals and Applications in the Petroleum Industry. 1ed. Cambridge University Press; Cambridge, UK. pp. 159-202.

Squires, F., (1916).Method of Recovering Oil and Gas, USA Patent 1238355. Steber, J. and Berger, H., (1995). Biodegradability of Anionic Surfactants. In: Karsa,

D.R. and Porter, M. R. (Eds.), Biodegradability of Anionic Surfactants. Blackie Academic & Professional; London, UK. pp. 134-182.

Stepanow, S., bauerschäfer, U. and Sommer, J.-U., (1996) Adsorption of Polymers at Interfaces and Extended Defects. Physical Review E, 54, No. 4, pp. 3899-3905.

Stoll, W. M., Al Shureqi, H., Finol, J. , Al-Harthy, S. A., Oyemade, S., de Kruijf, A., Wunnik, J-v, Arkesteijn, F., Bouwmeester, R., and Faber, M. J., (2010). Alkaline-Surfactant-Polymer Flood: From the Laboratory to the Field. Presented at the SPE EOR Conference at Oil & Gas West Asia, 11-13 April, Muscat, Oman. SPE paper 129164-MS.

Subkow, P., (1942). Process for the Removal of Bitumen from Bituminous Deposits,’” U.S. Patent No. 2288857.

Sun, J, Sun, L., Liu, W., Liu, X., Li X., Shen, Q., (2008). Alkaline Consumption Mechanisms by Crude Oil: A Comparison of Sodium Carbonate and Sodium Hydroxide. Colloids and Surfaces A: Physicochemical Engineering Aspects, Vol. 315, pp. 38–43.

Taber, J.J., Martin, F.D., and Seright, R.S., (1997A). EOR Screening Criteria Revisited - Part 1: Introduction to Screening Criteria and Enhanced Recovery Field Projects. SPE Reservoir Engineering. Vol. 12, No. 3. pp. 189-198. SPE paper 35385-PA.

Taber, J.J., Martin, F.D., and Seright, R.S., (1997B). EOR Screening Criteria Revisited—Part 2: Applications and Impact of Oil Prices. SPE Reservoir Engineering. Vol. 12, No. 3. pp. 199-206. SPE paper 39234-PA.

Tadros, T., (2005). Applied Surfactants: Principles and Applications. Wiley-VCH; Hoboken, USA.

Takigami, H., Taniguchi, N., Shimizu, Y., and Matsui, S., (1998). Toxicity Assays and Their Evaluation on Organic Polymer Flocculants Used for Municipal Sludge Dewatering, Water Science and Technology, Vol. 38, No. 7, 1998, pp. 207-215.

Tally, L. D.,, (1988). Hydrolytic Stability of Alkylethoxy Sulfates. SPE Reservoir Engineering. Vol. 3, No. 1, pp. 235-242. SPE paper 14912-PA.

Tanner , J. E. and Stejskal, E. O. (1965). Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field. Journal of chemical Physics, Vol. 42, pp. 288.

Tanner , J. E. and Stejskal, E. O., (1968). Restricted Self-Diffusion of Protons in Colloidal Systems by the Pulsed-Gradient, Spin-Echo Method. Journal of chemical Physics, Vol. 49, pp. 1768.

Tanner, J.E., (1970). Use of Stimulated Echo in NMR Diffusion Studies. The Journal of Chemical Physics. Vol. 52, No. 5, pp. 2523-2526.

234

Taugbøl, K., Ly, T.V., and Austad, T., (1995). Chemical Flooding of Oil Reservoirs: 3. Dissociative Surfactant-Polymer Interaction with a Positive Effect On oil Recovery. Colloid and Surfaces A: Physicochemical and Engineering Aspects, Vol. 103, pp. 83-90.

Taylor, H. E.,(2001). Inductively Coupled Plasma-Mass Spectrometry: Practices and Techniques. Academic Press, San Diego, California. ; London.

Taylor, K.C. and Hawkins, B., F., (1992). Emulsion in Enhanced Oil Recovery. In: Schramm, L. L. (Ed.). Emulsions Fundamentals and Applications in the Petroleum Industry. 1ed. American Chemical Society; Washington, D.C., USA. pp. 263-293.

Taylor, K.C. and Nasr-El-Din, H.A., (1994). Acrylamide Copolymers: A Review of Methods for the Determination of Concentration and Degree Of Hydrolysis. Journal of Petroleum Science and Engineering, Vol. 12, pp. 9–23.

Taylor, K.C., (1993). Spectrophotometric Determination of Acrylamide Polymers by Flow Injection Analysis. SPE Advanced Technology. Series, Vol. 1, No. 2, pp. 130–133. SPE paper 21007-PA.

Taylor, K.C., Burke, R.A., Nasr-El-Din, H.A. and Schramm, L.L., (1998). Development of a Flow Injection Analysis Method for Determination of Acrylamide Copolymers in Brines. Journal of Petroleum. Science and Engineering, Vol. 21, pp. 129–139.

Thomas, S., (2008). Enhanced Oil Recovery- An Overview. Oil and Gas Science and Technology. Vol. 63, No. 1, pp. 9-19.

Tiab, D. and Donaldson, E. D. (2004). Petrophysics: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties. Second Edition. Gulf Professional Publications; Boston, USA.

Tong, Z-S., Yang, C-Z, Wu, G-Q., Yuan, H., Yu, L., and Tian, G., (1998). A Study of Microscopic Flooding Mechanism of Surfactant/Alkali/Polymer. Presented at SPE/DOE Improved Oil Recovery Symposium, 19-22 April, Tulsa, Oklahoma. SPE paper 39662-MS.

Trushenski, S.P., Dauben, D.L., and Parrish, D.R., (1974). Micellar Flooding - Fluid Propagation, Interaction, and Mobility. SPE Journal, Vol. 14, No. 6. pp. 633-645. SPE paper 4582-PA.

Urbissinova, T.S., Trivedi, J. and Kuru, E., (2010). Effect of Elasticity During Viscoelastic Polymer Flooding: A Possible Mechanism of Increasing the Sweep Efficiency. Journal of Canadian Petroleum Technology. Vol. 49, No. 12. pp. 49-56.

Van Den Enden, J.C., Waddington, D., Van Aalst, H., Van Kralingen, C.G., and Packer, K.J., (1990). Rapid Determination of Water Droplet Size Distributions by PFG-NMR. Journal of Colloid and Interface Science. Vol. 140, No. 2, pp. 105-113.

Wang, D. Wang, G., Xia, H., Yang, S., and Wu, W., (2011). Incremental Recoveries in the Field of Large Scale High Viscous-Elastic Fluid Flooding are Double that of Conventional Polymer Flooding. Presented at the SPE Annual Technical Conference and Exhibition, 30 October-2 November, Denver, Colorado, USA. SPE paper 146473-MS.

Wang, D., Zhang, Z., Cheng, J., Yang, J., Gao, S., and Li, L., (1997). Pilot Tests of Alkaline/Surfactant/Polymer Flooding in Daqing Oil Field. SPE Reservoir Engineering, Vol. 12, No. 4, pp. 229-233. SPE paper 36748-PA.

Wang, J. and Dong, M., (2010). Simulation of o/w Emulsion Flow in Alkaline/ Surfactant Flood for Heavy Oil Recovery. SPE paper 138969-PA. Journal of Canadian Petroleum Technology. Vol. 49, No. 6. pp. 46-52.

235

Wang, J-L., Yuan, S-Y., Shi, F-S., and Jia, X., (2009). Experimental Study of Chemical Concentration Variation of ASP Flooding. Science in China Series E: Technological Sciences, July, Vol. 52, No. 7, pp. 1882-1890.

Wang, Y., Liu, J., Liu, B, Liu, Y., Wang, H. and Chen, G., (2004). Why Does Scale Form in ASP Flood? How to Prevent from It?--A Case Study of the Technology and Application of Scaling Mechanism and Inhibition in ASP Flood Pilot Area of N-1DX Block in Daqing. Presented at the SPE International Symposium on Oilfield Scale, 26-27 May, Aberdeen, Scotland. SPE paper 87469.

Weatherill, A., (2009). Surface Development Aspects of Alkaline-Surfactant-polymer (ASP) Flooding. Presented at International Petroleum Technology Conference, held in Doha, Qatar, 7-9 December. IPTC paper 13397.

Weers, J.G. and Scheuing, D.R., (1991). Micellar Sphere to Rod Transitions. In: Scheuing, D.R. (Ed.). Fourier Transform Infrared Spectroscopy in Colloid and Interface Science, ACS Symposium Series 447. American Chemical Society, Washington, USA. pp. 87-122.

Wen, Q., Chen, Z., Zhao, Y., Zhang, H. and Feng, Y., (2010). Biodegradation of Polyacrylamide by Bacteria Isolated from Activated Sludge and Oil-contaminated Soil. Journal of Hazardous Materials, Vol. 175, Issues 1-3, pp. 955-959.

Wesson, L.L and Harwell J.H., (2000). Surfactant Adsorption in Porous Media. In: Schramm, L. L. (Ed.). Surfactants Fundamentals and Applications in the Petroleum Industry. 1ed. Cambridge University Press; Cambridge, UK. pp. 121-158.

White, J.L., Goddard, J.E. and Phillips, H.M., (1973). Use of Polymers To Control Water Production in Oil Wells. Journal of Petroleum Technology, Vol. 25, No. 2. pp. 143-150. SPE paper 3672-PA.

Winsor, P.A. (1954). Solvent Properties of Amphiphilic Compounds. Butterworths Scientific Publications; London, U.K.

Wright, R.J., Wheat, M.R. and Dawe, R.A. (1987). Slug Size and Mobility Requirements for Chemically Enhanced Oil Recovery within Heterogeneous Reservoirs. SPE Reservoir Engineering. Vol. 2, No. 1, pp. 92-102. SPE paper 13704-PA.

Wu, Y., Dong, M., and Shirif, E., (2010). Study of Alkaline/Polymer Flooding for Heavy Oil Recovery Using Channelled Sandpacks. Presented in Canadian Unconventional Resources and International Petroleum Conference, 19-21 October, Calgary, Alberta, Canada. SPE paper 137460-MS.

Wyatt, K., Pitts, M.J., H. and Surkalo, H. (2004). Field Chemical Flood Performance Comparison with Laboratory Displacement in Reservoir Core. Presented at the SPE/DOE Symposium on Improved Oil Recovery, 17-21 April, Tulsa, Oklahoma, USA. SPE paper 89385-MS.

Wyatt, K., Pitts, M.J., Surkalo, H. (2002). Mature Waterfloods Renew Oil Production by Alkaline-Surfactant-Polymer Flooding. Presented at the SPE Eastern Regional Meeting, 23-26 October, Lexington, Kentucky, USA. SPE paper 78711-MS.

Zhang, M., Takahashi, M., Morin, R.H. and Esaki, T., (1998). Theoretical Evaluation of the Transient Response of Constant Head and Constant Flow-Rate Permeability Tests. Geotechnical Testing Journal, GTJODJ, Vol. 21, No. 1, pp. 52-57.

Zheng, C.G., Gall, B.L., Gao, H.W., Miller, A.E. and Bryant, R.S., (2000). Effects of Polymer Adsorption and Flow Behavior on Two-Phase Flow in Porous Media. SPE Reservoir Evaluation & Engineering. Vol. 3, No. 3, pp. 216-223. SPE paper 64270-PA.

236

9 Appendix A Chemical Analysis

9.1 Appendix A1: Statistical Tables Related to Bril liant Green Analytical Method

Table 9-1: Absorbance of BGRS with different surfactant and polymer concentrations

Surfactant concentration (% w/v) Polymer (ppm)

0.1 0.003 0.012 0.005 0.03 0.05 0.075

0 0.2147 0.0033 0.0307 0.0119 0.0760 0.1406 0.2097

5 0.2229 0.0036 0.0288 0.0120 0.0891 0.1528 0.2101

10 0.2094 0.0043 0.0271 0.0133 0.0895 0.1500 0.2132

20 0.2260 0.0038 0.0275 0.0125 0.0871 0.1539 0.2150

25 0.2375 0.0048 N.D. 0.0134 0.0883 0.1441 0.2012

50 0.2346 N.D 0.0284 0.0136 0.0779 0.1394 0.2196

100 0.2353 0.0045 0.0279 0.0121 0.0855 0.1430 0.2101

200 0.2386 0.0049 0.0286 0.0138 0.0893 0.1525 0.2194 Table 9-2: Statistical processing of the data in Table 9-1 Surfactant Concentration (%) 0.1 0.003 0.012 0.005 0.03 0.05 0.075

Mean absorbance 0.227 0.004 0.028 0.013 0.085 0.147 0.212

Number of Samples 8 7 7 8 8 8 8

95% confidence interval t value 2.365 2.447 2.447 2.365 2.365 2.365 2.365

STDEV 0.0110 0.0006 0.0012 0.0008 0.0054 0.0059 0.0060

± 95% confidence range 0.0092 0.0006 0.0011 0.0007 0.0045 0.0049 0.0050

±95% Confidence as a % of the mean absorbance

4 14 4 5 5 3 2

Minimum concentration of surfactant based on lower confidence limit

0.096 0.003 0.012 0.005 0.028 0.048 0.073

Maximum concentration of surfactant based on upper confidence limit

0.109 0.004 0.013 0.006 0.034 0.055 0.080

237

Table 9-3: Absorbance of BGRS with different surfactant and polymer concentrations

Surfactant Concentration

(%wt)

0.1 0.05 0.005 0.05 0.035

Samples 1 0.369 0.248 0.026 0.212 0.131 2 0.387 0.229 0.026 0.226 0.120 3 0.371 0.227 0.027 0.221 0.129 4 0.389 0.237 0.026 0.218 0.121 5 0.378 0.228 0.025 0.226 0.123 6 0.400 0.220 0.026 0.222 0.118 7 0.361 0.235 0.220 0.118

Table 9-4: Statistical processing of the data in Table 9-3

Concentration 0.05 0.005 0.05 0.035 0.1

Mean absorbance 0.232 0.026 0.221 0.123 0.379 Number of Samples 7 6 7 7 7

95% confidence interval t value 2.447 2.571 2.447 2.447 2.447 STDEV 0.0088 0.0053 0.0081 0.0051 0.0135

± 95% confidence range 0.0081 0.0055 0.0081 0.0051 0.0125 95% Confidence as a % of the

mean 3.50 2.08 3.65 4 3.30

Minimum concentration of surfactant based on lower

confidence limit 0.048 0.005 0.048 0.034 0.097

Maximum concentration of surfactant based on upper

confidence limit 0.058 0.006 0.058 0.040 0.113

238

9.2 Appendix A2: Reagents and Procedures of the N-Bromination Method

9.2.1.1 The polyacrylamide was determined by the classical the N-bromination

method (iodide/starch). The material, the procedure of preparing the reagents

and the process of measuring the absorbance is reported in this appendix.

Materials:

Sodium acetate trihydrate: Ajax chemical, analytical grad.

Glacial acid (pure acetic acid): Ajax Finechem, assay 99.7%

Bromine: Sigma-Aldrich, Reagent Grad, assay 99-101%

Sodium format: Ajax chemical, assay 98%

Iodometry grade-potato starch: (T.J. Backer Starch) Mallinckrodt Backer, Inc. water

soluble 90-100%.

Cadmium iodide: Fulka, Fulka Analytical grad, assay 99% .

Preparation of the Reagent:

A)-Preparing the buffer:

i. Dissolve 3.014 (g) of sodium acetate anhydrous in 160 mL of D.W.

ii. Add 3 mL of 1000 ppm acetamide solution/ this step was omitted in this work as

it is used to bring the intercept to zero and not crucial to this work.

iii. Add 30 mL of glacial acetic acid (this is to adjust to pH value of 3.5 )

iv. Add 0.292 g of Aluminium sulfate octadecahydrate

v. Dilute to 200±10 mL

B) Saturated Bromine Water

Equilibrate bromine (30 cc) for two days with 300cc of D.W., this will results in

saturating the water with the bromine.

C) Sodium Format Solution (1% w/v)

Dissolve 1 (g) of sodium format in 100 (mL)

D) Starch/ CdI2 colour reagent

i. Boil 150 mL of D.I. water

ii. Slurry 0.5 (g) of iodometry grade-potato starch in about 3 mL of cold water

239

iii. Add the slurry to the boiling water

iv. Boil gently for five minutes

v. Cool to room temperature

vi. Filter through No 42 Whatman filter paper

vii. Add 0.25 g of cadmium iodide

viii. Add 0.2 g (active base) of Neodol 25-3S/ was omitted in this work as it is

important only for the injection flow method by (Taylor ,1998)

ix. Stir until dissolved

x. Dilute to 170 mL

xi. Discard if reagent becomes yellow (due to starch degradation)

Procedure of polyacrylamide concentration determination Steps:

1. Take 1 mL of buffer ( keeps pH at 3.5 to eliminate chloride ion interference)

2. Add 6 mL of diluted sample and gently apply some mixing.

3. Add 1 mL of saturated bromine water to the 6 mL of Sample.

4. Wait 15 min

5. Add 1 mL of sodium format (eliminate excess bromine) and gently apply some

mixing.

6. Wait exactly 5 min ( other waiting time could be used, but must keep the

waiting time same for all samples)

7. Add 1 mL of starch cadmium iodide and gently apply some mixing.

8. Wait 10 min, and gently apply some mixing.

9. Pour some of this to a cuvette cell.

10. Scan in the range 400-750 nm with spectrophotometer at exactly 15 min.

240

9.3

App

endi

x A

3: IC

P-A

ES

Ana

lysi

s S

ever

al s

am

ple

s o

bta

ined

fro

m w

ater

flo

ods

and

AS

P f

loo

ds in

the

san

d pa

cks

wer

e an

aly

sed

usin

g I

CP

-AE

S t

o ch

eck

for

diva

lent

ele

me

nts

to e

nsur

e

the

div

ale

nt e

lem

ent

s co

ncen

trat

ions

wer

e no

t la

rge

to c

ause

sur

fact

ant

conc

ent

ratio

n. I

n ad

diti

on,

the

co

nce

ntra

tion

of

silic

on

wa

s ch

ecke

d to

ens

ure

that

no

sig

nific

ant

sili

ca d

isso

lutio

n to

ok

plac

e. T

he A

lum

iniu

m c

onc

ent

ratio

n w

as

als

o c

heck

ed b

eca

use

of

its a

bilit

y to

initi

ate

the

gela

tion

pro

cess

in

poly

acry

lam

ide

poly

mer

. T

he r

esu

lts a

re r

elat

ed t

o C

hapt

ers

3, 4

, 5

and

6 an

d ar

e sh

ow

n in

T

able

9-5

.

Tab

le 9

-5:

ICP

-AE

S A

naly

sis

of s

eve

ral s

am

ple

s fr

om

wat

er a

nd A

SP

flo

ods

*

S

odiu

m

Mag

nesi

um

Alu

min

ium

S

ilico

n S

ulfu

r C

alci

um

Man

gane

se

Iron

C

oppe

r

Sam

ple

Sou

rce

µg/m

L µg

/mL

µg/m

L µg

/mL

µg/m

L µg

/mL

µg/m

L µg

/mL

µg/m

L S

P16

Sam

ple

8 S

P16

wat

er fl

ood

7.61

1.

12

0.75

9 <

0.1

00

< 0

.100

1.

85

< 0

.100

1.

24

< 0

.100

SP

16 S

ampl

e 9

SP

16 w

ater

floo

d 9.

18

1.32

<

0.1

00

< 0

.100

<

0.1

00

2.03

<

0.1

00

1.26

<

0.1

00

SP

16 S

ampl

e 11

S

P16

wat

er fl

ood

8.71

1.

42

1.02

0.

970

< 0

.100

2.

15

< 0

.100

0.

653

< 0

.100

SP

16 S

ampl

e 12

S

P16

wat

er fl

ood

5.52

0.

751

< 0

.100

<

0.1

00

< 0

.100

1.

22

< 0

.100

<

0.1

00

< 0

.100

SP

16 S

ampl

e 13

S

P16

wat

er fl

ood

7.66

1.

46

< 0

.100

0.

766

< 0

.100

2.

66

< 0

.100

<

0.1

00

< 0

.100

SP

16 S

ampl

e 14

S

P16

AS

P fl

ood

26.2

1.

72

< 0

.100

1.

92

12.2

4.

61

< 0

.100

0.

186

0.82

2

SP

16 S

ampl

e 15

S

P16

AS

P fl

ood

104

0.42

8 0.

944

7.82

27

.4

1.13

<

0.1

00

2.13

1.

30

SP

16 S

ampl

e 16

S

P16

AS

P fl

ood

222

0.30

7 1.

51

12.0

43

.2

1.08

<

0.1

00

< 0

.100

1.

41

SP

16 S

ampl

e 18

S

P16

AS

P fl

ood

361

0.19

6 3.

89

13.7

62

.8

0.52

2 <

0.1

00

0.89

8 0.

469

deio

nise

d w

ater

D

W fr

om L

abor

ator

y ta

p 67

.5

7.69

<

0.1

00

3.82

2.

22

15.3

<

0.1

00

< 0

.100

0.

228

AS

P s

lug

AS

P s

lug

mix

ed w

ith th

e -3

00 µ

m s

and

and

then

ex

trac

ted

619

0.15

3 1.

69

5.44

10

6 0.

414

< 0

.100

0.

300

0.23

0

tria

l AS

P fl

ood

tria

l AS

P fl

ood

70.4

2.

25

1.01

4.

36

25.0

5.

74

< 0

.100

1.

09

2.49

tria

l AS

P fl

ood

tria

l AS

P fl

ood

265

0.38

0 5.

91

7.84

49

.5

1.10

<

0.1

00

0.99

9 1.

52

tria

l AS

P fl

ood

tria

l AS

P fl

ood

297

0.21

5 3.

38

8.34

52

.0

0.48

4 <

0.1

00

< 0

.100

1.

11

tria

l AS

P fl

ood

tria

l AS

P fl

ood

253

0.46

1 2.

41

7.93

31

.6

0.18

6 <

0.1

00

0.94

1 0.

362

*The

se m

easu

rem

ent

s w

ere

done

on

calib

rate

d IC

P-A

ES

Ins

tru

me

nt a

t TS

W A

nal

ytic

al P

ty L

td labo

rato

ries.

241

10 Appendix B

10.1 Appendix B1: Derivation of the Mass Balance Eq uation Used for the Determination of Water and Oil Saturations

The equation derived below was used to calculate the saturations of the water and oil

inside cores or sand packs (SP) using mass changes. This equation was taught in lecture

notes of the master of petroleum engineering course in Curtin University in 2005. The

derivation of the equation is simple and based on the assumption that there is no air in

the pores as follows:

SP of massDry -SP of mass Saturated MSP of volumepore inside massNet N ==

waterof mass oil of mass MSP of volumepore inside massNet N +==

woN MMM +=

wwooN SPVSPVM ρρ +=

wo SS +=1 (No air assumption)

woooN SPVSPVM ρρ )1( −+=

ow

oN

oPV

M

Sρρ

ρ

−

−−=1 and ow SS −=1

where: SP abbreviation for sand pack PV is the pore volume of the sand pack (mL)

Mo and Mw are the masses of oil and water inside the SP, respectively (g)

ρo is the oil density (g/mL)

ρw is the water or ASP slug density (g/mL)

So is the oil saturation in the SP Sw is the water saturation in the SP

242

10.2 Appendix B2: Image Processing for the Measurem ents of Liquids Production Rates

Production rates of the oil, water and microemulsion phases are essential to characterise

the impact of heterogeneity on ASP process. They were determined by using a fraction

collector and applying image analysis techniques. The produced fluids were collected in

3.5 mL cylindrical vials, with a record of the dry mass of each vial. The fraction

collector can be adjusted to control the time of collection. For a vial containing only one

phase, the volume can simply be calculated by dividing the mass by density. When

there is multiphase in the vial, image analysis can be applied to find the fractions of

each phase in the vial. Image analysis techniques were performed using image

processing softwares such as GIMP and ImagJ.

In order to find the liquid volumes in vials and the liquid height, a relationship between

the liquid height and volume was need. To establish this relationship, precisely known

volumes of water were poured in few pre-weight vials by a micropipette. Image of these

vials were taken against a reference ruler. Care was taken to ensure that the plan of

focus of the camera included both the vial and the length reference. From the distance

reference, the distance per pixel can be calculated by using one of the abovementioned

softwares. The height of the water was then found by measuring the number of pixels

from the base of the vial to the water surface and converting it in real distance. These

resulting real heights were then plotted against the known volumes. A straight line

relationship between the height and volumes was found as shown in Figure 10-1.

Correlation of Liquid Height and Volume in 3.5 mL v ials

y = 0.9995x - 0.0728

R2 = 0.9998

0

0.5

1

1.5

2

2.5

3

3.5

0 1 1 2 2 3 3 4 4

Liquid Height inside Vial (cm)

Vol

ume

insi

de V

ial (

cm3)

Figure 10-1: Correlation line between liquid volume and liquid height in the 3.5 mL glass vials which were used to collect produced fluids.

243

The calibration line takes into account the mass of the base of the vial and the extra

water meniscus. Images of real samples were processed to find the heights of water/oil

and volumes were then determined using the calibration line.

244

10.3 Appendix B3: Tables of Chapter 5

This table was obtained from the experimental work described in Section 6.2.1. The

table shows the relative concentration of several metals detected in the sample of the

residues found in the container of Stag Crude. Note, the ICP-AES instrument was not

calibrated when these measurements were done, thus, the shown concentrations are only

qualitative.

Table 10-1: Relative concentration of metals which were detected in the sample of residues collected from the container of Stag Crude using ICP-AES *

Element Relative Concentration (µg/L)

Al 2520

As 660

B 180

Ba 96

Be <1

Cd <2

Co 85

Cr 170

Cu 50

Fe 900000

Mn 1500

Mo 15

Ni 200

Pb 100

V 40

Zn 1700

* These numbers are only qualitative to reflect the relative amounts of the detected metals.

245

11 Appendix C MATLAB ® files for NMR-Pulsed Field Gradient

11.1 Appendix C1: MATLAB ® Code to Model the Attenuation of NMR Signal in Spherical Cavities/ Emulsion Droplets

This appendix comes in conjunction with Appendix C2 and C3. More instruction on

using the code and making the nonlinear fitting are provided in Appendix C3. The code

in this appendix models Equation 2-38 for restricted diffusion in spherical cavity

(droplets). This code is used to make the fitting between the observed decay in NMR

signal and the theoretical model (Equation 2-38) as the field gradient is increased.

Save the code under the name NMRf.m % start of code function Robs =NMRf(fp,g) % to use the function you need to define g (number of gradient points) %and it should be in vertical array. % the fitting parameters are dav and sigma, input is in micrometer for %dav and dimensionless for sigma, and these need to be given an %initial geuss for example, fp is given initial guess like fp =[1 0.5], %that is dav=1 micrometer and sigma= 0.5., then command the function dav=1e-4*fp(1);% fitting parameter for the mean radius of the ognormal %distribution sigma= fp(2);% distribution width i=0;sl=0;j=0;su=0; jamma=26751; % gyromagnetic ratio of hydrogen (1H); change if use other %NMR signal ( for now mainly hydrogen is followed for the %determination of o/w or w/o emulsion droplet size distribution %because both water and oil have hydrogen nuclei) DO=3.752e-11;% oil diffusion coefficient in m2/s DW=2.197e-9;% water diffusion coefficient in m2/s %change the values of DO and DW for the specific oil and water in use deltas=0.0036;%small delta in seconds deltab= 0.3;%big delta in seconds %change little delta and big delta and the diffusion coefficients %according to the experimental input of the NMR machine. D=DO; %diffusion coefficient; type in DO when following oil peak and %use DW when following water peaks, in this case D=DO because oil peak %is followed DD=10000*D; % feed in diffusion coefficient, multiplied here by 10000 % to change units from m2/s to cm2/s r=[0.00001:0.00001:0.00700];% scanned droplets radius range in %centimetres and this is equivalent to range of 0.1 to 70 micrometre, %the range and step of r could be changed, adjust i to take account %for changes in %the range of r

246

for j=1:8 for i=1:700 ;% change j to the number of points ( in this %case 8 points) and change i to the accommodate changes to r su(i) = lognpdf(2*r(i),log(dav),sigma)*(r(i))^3*exp(-2*1*jamma^2*g(j)^2*[[(1/((2.0816/r(i))^2*((2.0816)^2-2))) *(( 2*deltas/((2.0816/r(i))^2*DD))-( ((2+ exp(-1*(2.0816/r(i))^2*DD*(deltab-deltas))- 2*exp(-1*(2.0816/r(i))^2*DD*deltas)-2*exp(-1*(2.0816/r(i))^2*DD*deltab)+ exp(-1*(2.0816/r(i))^2*DD*(deltab+deltas)))/((2.0816/r(i))^2* DD)^2)))]+[(1/((5.9404/r(i))^2*((5.9404)^2-2))) *(( 2*deltas/((5.9404/r(i))^2*DD))-( ((2+ exp(-1*(5.9404/r(i))^2*DD*(deltab-deltas))- 2*exp(-1*(5.9404/r(i))^2*DD*deltas)-2*exp(-1*(5.9404/r(i))^2*DD*deltab)+ exp(-1*(5.9404/r(i))^2*DD*(deltab+deltas)))/((5.9404/r(i))^2* DD)^2)))]+[(1/((9.2058/r(i))^2*((9.2058)^2-2))) *(( 2*deltas/((9.2058/r(i))^2*DD))-( ((2+ exp(-1*(9.2058/r(i))^2*DD*(deltab-deltas))- 2*exp(-1*(9.2058/r(i))^2*DD*deltas)-2*exp(-1*(9.2058/r(i))^2*DD*deltab)+ exp(-1*(9.2058/r(i))^2*DD*(deltab+deltas)))/((9.2058/r(i))^2* DD)^2)))]+[(1/((12.4044/r(i))^2*((12.4044)^2-2))) *(( 2*deltas/((12.4044/r(i))^2*DD))-( ((2+ exp(-1*(12.4044/r(i))^2*DD*(deltab-deltas))- 2*exp(-1*(12.4044/r(i))^2*DD*deltas)-2*exp(-1*(12.4044/r(i))^2*DD*deltab)+ exp(-1*(12.4044/r(i))^2*DD*(deltab+deltas)))/((12.4044/r(i))^2* DD)^2)))]+[(1/((12.4044/r(i))^2*((12.4044)^2-2))) *(( 2*deltas/((12.4044/r(i))^2*DD))-( ((2+ exp(-1*(12.4044/r(i))^2*DD*(deltab-deltas))- 2*exp(-1*(12.4044/r(i))^2*DD*deltas)-2*exp(-1*(12.4044/r(i))^2*DD*deltab)+ exp(-1*(12.4044/r(i))^2*DD*(deltab+deltas)))/((12.4044/r(i))^2* DD)^2)))]+[(1/((15.5792/r(i))^2*((15.5792)^2-2))) *(( 2*deltas/((15.5792/r(i))^2*DD))-( ((2+ exp(-1*(15.5792/r(i))^2*DD*(deltab-deltas))- 2*exp(-1*(15.5792/r(i))^2*DD*deltas)-2*exp(-1*(15.5792/r(i))^2*DD*deltab)+ exp(-1*(15.5792/r(i))^2*DD*(deltab+deltas)))/((15.5792/r(i))^2* DD)^2)))]]); sl(i)=(r(i))^3*lognpdf(2*r(i),log(dav),sigma);end; Robs(j)=sum(sum(su))/sum(sum(sl));end; % end of code, instruction on using the code are in Appendix C3

247

11.2 Appendix C2: Roots of the Bessel Function This appendix provides the necessary roots of Bessel’s function (Equation 2-37) which

are needed to use Equation 2-38 and finding the EDSD. The author included these

roots and the below codes to obtain the roots because, these ,despite the efforts to find

them, were not found in publically available resources, so providing them here may safe

the energy of interested readers.

These below are MATLAB® functions and commands made to get the roots of the

Bessel function defined in Equation 2-37. The code manipulates the fact that the

spacing between the roots of this type of Bessel function is slightly bigger than π

(=3.14159) for small roots. This spacing approaches π as roots increase in value

(Spiegel, 1974, pp. 101).

1. Make m file and place in the following code in the m file and save under the

name broots.m; function Br = broots(x); Br= besselj(3/2, x)-x.*besselj(5/2, x);

2. Make another m file under the name Brzeroget.m and place the following lines: function Brzero= Brzeroget(i); x=0;i=0; for i=1:7;x=3*i ;n(i)=x-3; Brzero(i) =fzero(@broots, n(i));end

3. Run the command Brzeroget to get the roots.

Both files should be saved in the same directory and the directory should be made the

current directory for MATLAB®. If more roots are needed change i (current value i=7).

The following are the first 7 roots. The positive roots are only required. A cut off could

be used after the third root. Adding more roots may not change the fitting function. We

used the first five roots, underlined below:

0 2.0816 5.9404 9.2058 12.4044 15.5792 18.7426 This code gave up to the first 67 roots correctly, beyond this user need to check the

spacing and make sure it is close to π. There is room of improvement in this code, but it

is sufficient for this PhD work.

248

11.3 Appendix C3: Instructions on Using MATLAB ® Function lqcurvefit for the Determination Size Emulsion Dropl et Size

Distribution

This appendix describes a procedure to use the MATLAB® code provided in Appendix

C1 to find EDSD. In this study, the EDSD was determined using NMR-PFG-STE

method. This method requires fitting of theoretical curves to the experimental results

using the restricted diffusion model (Equation 2-38). This model has two fitting

parameters for curve matching and these are the average droplet size and the

distribution width. A successful matching is that gives low sum of the least squares. The

following procedure was conducted using the lqcurvefit function in MATLAB®:

1- Create m file and give it the name NMRf as described in Appendix C1. 2- Define the number of gradient used (vertical array) call it g. 3- Define the observed NMR signal (make it horizontal array), call it irh. 4- Define the fitting parameters fp =[ dav sigma] where dav is the anticipated

mean diameter of the emulsion in micrometers and sigma is distribution variance (distribution width).

5- Then recall options for the lqcurvefit function by typing the following in MATLAB ® command line:

options=optimset('lsqcurvefit');

6- Define the upper and lower limits of the expected values of dav and sigma: ub=[ x y];

lb=[ b d];

Where b and x are in micrometers, y and d are dimensionless

7- run the function by the following line:

[z,resnorm,residual]=lsqcurvefit(@NMRf, fp,g,irh,lb,ub,options)

At the end of the computation, the program returns two values of z and these are the values of dav and sigma which gave the best fit. To make more iteration replace fp by z and run the command again as follows: [z,resnorm,residual]=lsqcurvefit(@NMRf, z,g,irh,lb,ub,options) The resnorm is the sum of the least squares and residual are the values of each least difference between the fit and the experimental data

8- Use the values of dav and sigma, which gave the best fit, as inputs into

Equation 2-39. Plot the distribution over the anticipated size range. This should give the EDSD.

University of Western Australia€¦ · Hamid Ahmed Mohammed Ghafram Al Shahri BSc. (Hons.,...

Documents

Transcript of University of Western Australia€¦ · Hamid Ahmed Mohammed Ghafram Al Shahri BSc. (Hons.,...