Determining the Effective Surface Area of Minerals and the ...

Determining the Effective Surface Area of Minerals and the Implications for Near Wellbore Geochemical

Reservoir Stimulation

By

Syed Anas Ali

683989

httpsorcidorg0000-0003-0346-337X

Submitted for PhD Degree

March2018

Prof Ralf Haese (Supervisor)

Dr Jay Black (Co-supervisor)

School of Earth Sciences

Faculty of Sciences

University of Melbourne

i

ABSTRACT Sufficient CO2 injection capacity is a key criteria for a prospective CO2 storage site and has proven

to be a technical impediment for the development of a CO2 storage operation for example in case

of the ZeroGen project This study develops and applies geochemical reservoir stimulation

procedures involving pH-controlled solutions to promote mineral dissolution and increase

permeability of a siliciclastic reservoir to enhance CO2 injectivity Effective deployment of a

geochemical stimulation technique at field scale requires site-specific data and an understanding

of the underlying geochemical reactions coupled to fluid flow within a reservoir Thus laboratory

scale experiments are developed and experimental results are used in reactive transport

simulations using the TOUGHREACT code to assess the degree of mineral dissolution and

possible associated increase in porosity and permeability under variable conditions The surface

area of minerals is often one of the least well-constrained variables in porous rocks and therefore

introduces a large uncertainty in reactive-transport modelling results Weathering reaction rates in

natural systems have been shown to be orders of magnitude lower than predicted using models

involving assumptions regarding mineral surface area-to-mass ratios The discrepancy has been

explained by several reasons including mineral overgrowth poor pore-to-pore connectivity and

heterogeneous flow fields Therefore a new methodology has been developed to determine the

effective surface area of minerals using core flood experiments and applied to Catherine Sandstone

samples The derived mineral effective surface areas are incorporated into near-wellbore reactive

transport models evaluating the feasibility of enhancing permeability through geochemical

stimulation

ii

DECLARATION

bull The thesis comprises only my original work towards the PhD except where indicated in the

preface

bull Due acknowledgement has been made in the text to all other material used

bull The thesis is fewer than the maximum word limit in length exclusive of tables maps

bibliographies and appendices or that the thesis is 40000 words as approved by the

Research Higher Degrees Committee

Syed Anas Ali

iii

PREFACE This research was fully funded and supervised by Prof Ralf Haese (Director The Peter

Cook Centre for CCS Research) under the co-supervision of Dr Jay Black (Experimental

Geochemist School of Earth Sciences University of Melbourne) All the experimental and

modelling work was carried out by the author with assistance of Dr Jay Black and Prof Ralf Haese

at the environmental geochemistry laboratory facility at the School of Earth Sciences University

of Melbourne The outcome of the research was presented in the following conferences

Ali S Black J and Haese R (2017) ldquoDetermining the Effective Surface Area of Minerals and

the Implication for Near Wellbore Geochemical Reservoir Stimulationrdquo

Goldschmidt Conference Paris France 13-18 August 2017

Ali S Black J and Haese R (2015) ldquoGeochemical Stimulation in Siliciclastic Reservoirsrdquo

AAPGampSEG International Conference and Exhibition Melbourne Australia 13-16 September 2015 Ali S Black J and Haese R (2014) ldquoEnhanced Injectivity in Reservoirs by Geochemical

Stimulationrdquo CO2CRC Research Symposium Torquay Australia 25-26 November 2014

iv

ACKNOWLEDGMENTS This thesis has significantly contributed in my professional skills and there have been many

helping hands behind the successful completion I consider myself extremely lucky to end up under

the supervision of Prof Ralf Haese and Dr Jay Black The impressive leadership of Ralf and his

devotion to this project made the whole journey enormously smooth and delightful Furthermore

the problem-solving skills of Jay guided me at every step of my PhD Apart from their substantial

scientific contributions and guidance in this work they have proven to be a role model for me to

look up to as a scientist and more importantly as a human being I would also like to extend my

gratitude to other researchers including Dr Hong Phuc Vu (University of Melbourne) for his

valuable help throughout my PhD Dr Dirke Kirste (Simon Fraser University) for getting me

started in TOUGHREACT Mr Graham Hutchinson (University of Melbourne) for electron

microprobe analysis petrophysical laboratory (University of Melbourne) and my friends and

colleagues at the School of Earth Sciences the University of Melbourne

The completion of this thesis would not be possible without the support of my gorgeous

wife Marium who has been the most beautiful addition to my personal life Thanks Chappie Cat

for your inputs in my thesis and for always been there to give me moral support Also the immense

happiness I felt after hearing the news of our upcoming baby (DaneenMikael) gave me extra

strength to reach the completion Among my other family members who have been a great support

throughout my academic career I want to specially mention my uncle Parvez Muhammad for his

selfless contribution in my higher education Last but not the least my parents Syed Hassan Akhtar

and Rakhshindah Hassan I know your prayers are always with me and thatrsquos the reason I have

been successful

v

TABLE OF CONTENTS 1 Introduction and Literature Review 1

11 Relevance and Importance of the Study 1

12 Reactive Surface Area of Minerals 5

13 Enhanced Injectivity of CO2 for Storage 7

131 CO2 Injectivity 7

132 Geochemical Reservoir Stimulation 7

133 Dissolution of Rock Forming Minerals 9

134 ZeroGen Carbon Capture and Storage Project 12

135 Insufficient injectivity Reported in CO2 Storage Reservoirs Around the World 12

14 Groundwater Flow and Reactive Transport Modelling 13

141 Geological Model 14

142 Reactive Transport Modelling using TOUGHREACT 18

15 Porosity-Permeability Relations Described in Literature 23

151 Permeability 24

152 Porosity-Permeability Relationship 24

153 Predicting Permeability of Pure Quartz Sand 25

154 Predicting Permeability of Clays 26

155 Permeability of Sand and Clays Mixture 28

16 Deriving the Verma and Pruess Porosity-Permeability Relationship 31

17 Research Questions 33

2 Geology of the Northern Denison Trough and Core Characterization 34

21 Basin Evolution and Structure of the Denison Trough 34

22 Permian Lithostratigraphy and Facies of the Denison Trough Sediments 37

221 Reids Dome Beds 37

222 Cattle Creek Formation 38

223 Aldebaran Sandstone 39

224 Upper member of Aldebaran Sandstone amp Freitag Formation 40

225 Ingelara Formation 41

226 Catherine Sandstone 41

227 Peawaddy Formation 42

vi

228 Black Alley Shale 42

23 Reservoir Characterisation of the Aldebaran Freitag and Catherine Sandstones 43


232 Freitag Formation 45


24 Sampling of the Catherine Sandstone 47

241 Sampling Sites 48

25 Core Sample Characterisation 54

251 X-ray Diffraction 54

252 Porosity Analysis 56

253 Permeability Analysis 57

254 Thin Section Analysis 60

255 Electron Microprobe Analysis 70

3 Experimental Design and Methods 71

31 Single Phase Core-flood Design and Operation 71

32 Core-flooding Experiments Objectives and Sequence 73

321 Experiment 2 73

322 Experiment 3 77

323 Experiment 4 77

324 Experiment 5 78

325 Experiment 6a and 6b 80

326 Experiment 7a amp 7b 81

33 Fluid Sampling and Analysis 81

34 Aqueous Speciation Modelling 82

4 Results and Observations of Core Flooding Experiments 84

41 Experiment 2 84

42 Experiment 3 86

43 Experiment 4 89

44 Experiment 5 95

45 Experiment 6a 98

46 Experiment 6b 99

47 Experiment 7a 102

48 Experiment 7b 104

vii

5 DISCUSSION 106

51 Determining the Effective Surface Area (ESA) of Minerals 106

511 Core Flood Experiments with Low Flow Rate 110

512 Core Flood Experiments with High Flow Rate 115

513 Mineral Dissolution Near- and Far-from-equilibrium 117

514 Error Analysis 123

52 Determining the Intrinsic Porosity-Permeability Relationship 128

521 Fines Migration in High Permeability Sandstone 129

522 Initial Permeability Changes when Flooding at High and Low pH 130


61 Core Scale Modelling 133

611 Comparison of Experiment 7b to Model Results at pH 2 133

612 Comparison of Experiment 7a to Model Results at pH 12 136

613 Modelling Experimental Results to Determine the Effective Surface Area of Carbonates

137

62 Near Well Formation Scale Modelling 142

621 Background and Motivation 142

622 Model Setup 143

623 Reaction Kinetics 143

624 Reactive Surface Area 144

625 Grid Size Optimization 147

626 Reservoir Stimulation using Alkaline Reagents 149

627 Reservoir Stimulation using Acidic Reagents 160

63 Comparison of Porosity-Permeability Relationship 163

64 Feasibility Study 166

7 Conclusion and Recommendations 168

71 Conclusion 168

711 Core Flood Dissolution Experiments 168

712 Reactive Transport Modelling 169

72 Recommendations 171

viii

GLOSSARY

a Cross sectional area to flow (m2) A

o Initial reactive surface area (m2g or cm2) A Final reactive surface area (m2g or cm2g) Surface area implemented as function of mineral grain size (m2g) Am Surface area of minerals in units of (m2

mineralm3mineral)

An Final reactive surface area of minerals in units of (m2mineralkgwater)

Aprc Precursor surface area (optional) in units of (m2 surfacem3

medium)

C Final mineral concentration C0 Initial mineral concentration Ci Concentration of aqueous species lsquoirsquo (moles) Cw Wetted-surface conversion factor in units of (kgwaterm3

medium) Dr Dissolution rate (mgcm2sec) Ea Activation energy (kJ mol-1) Initial mineral volume fraction () Volume fraction of nonreactive rock ()

h Length of the core (cm) lowast Rate constant (molm2s or molcm2s) M Molecular weight (gmole) Empirical coefficientcementation exponent mi Mass per pore volume of lsquoirsquo species (mg) Power law exponent derived from a pore-body-and-throat model Mdry Mass of the dried sample (g) Msat Mass of the surface dried sample saturated by water (g) Msub Mass of the sample submerged under water (g) n Moles per pore volume nrsquo Power law exponent Pb Bore hole pressure (bars) Pi Formation pressure in (bars) Q Flow rate (mLmin or kgs) Qn Reaction quotient R Gas constant (J mol-1 K-1) r Radius of the core (cm) rn DissolutionPrecipitation Rt Residence time in (sec) Sa Effective surface area (cm2g) Sw Liquid saturation ranging from 1 to lt1 Sa Effective surface area (cm2g) T Absolute temperature (Kelvin) Vb Bulk volume of the sample (cm3) Vfrac Final mineral volume fraction () Vp Pore volume of the sample (cm3mLL)

ix

κ Final Permeability in (m2)

κi Initial Permeability in (m2) κsd Permeability of sand (m2) κcl Permeability of clay (m2) κcfs Permeability of sand with pore spaces filled by clay (m2)

Ω Kinetic mineral saturation ratio ratio of the volume of void spaces to the volume of solids λ Reactive fraction of the total surface area of the mineral ρw Density of water (gcm3) micro Viscosity (Pas) ∆P Differential Pressure (BarsPa) oslashsd Sand Porosity φv Volume of Shale () oslash Final Porosity oslashc Critical porosity value at which permeability goes to zero oslashi Initial Porosity Density of water (gcm3)

x

LIST OF FIGURES Figure 111 Large scale CCS projects worldwide (After Garnett et al 2013) 4

Figure 112 Distribution of prospective sedimentary basins around the world that could have potential for CO2 storage (After IPCC 2005)

5

Figure 131 Quartz dissolution rate as a function of pH and temperature points are the experimental data and lines are modelled fits to the data

11

Figure 132 Dissolution rate of albite at 25o C at acidic neutral and alkaline pH 11

Figure 133 Reservoir permeability of different CCS projects around the world (After Hosa et al 2011)

13

Figure 141 Rectangular hexahedron cells representing regular mesh type 16

Figure 142 Customize meshing option on the left allowing incremental grid density on the right

16

Figure 143 Polygonal mesh with irregular model boundaries 17

Figure 144 2D Radial mesh on the left Software representation of radial mesh on the right

18

Figure 151 A comparison of observed and calculated permeability using the Kozeny-Carman relation (After Luijendijk amp Gleeson 2015)

25

Figure 152 A comparison of calculated and observed permeabilities of Clays (After Luijendijk amp Gleeson 2015)

27

Figure 153 Permeability of mixed sand and clay through the ideal packing model (After Revil amp Cathles 1999)

39

Figure 154 Natural and experimental datasets of permeability with calculated values (After Luijendijk amp Gleeson 2015)

30

Figure 161 Injectivity index plotted against time solid lines represent modelled data while diamond shaped markers are field data (Xu et al 2004b)

32

Figure 21 Structural elements of Denison Trough marking approximate locations of ZeroGen exploration wells and core sampling sites (After Baker and de Caritat 1992)

36

Figure 22 Seismic section showing a cross section along ZeroGen 5 well in the Denison Trough (After Garnett et al 2013)

36

Figure 23 Stratigraphy of the Denison Trough (After McKellar 2013) 38

Figure 24 Wireline log correlation between ZeroGen wells showing depth and thickness of Catherine sandstone (After Baker 2009)

40

Figure 25 Satellite image of the sampling locations in the south of Springsure 47

Figure 26 Geological map showing sampling locations at Freitag Station (Modified after Mollan et al 1969)

48

Figure 27 Catherine Sandstone block at site F1 exposed in the creek adjacent to the road

49

Figure 28 Sampling site F4-1 amp F4-2 49

Figure 29 Catherine Sandstone exposure at site F2 several meters off the road in the south of Mount Catherine

50

Figure 210 Sandstone exposure at site F3 arrow indicates rock beneath weathered surface

51

xi

Figure 211 Sandstone beds exposed in the creek near Inglis Station (I1) 52

Figure 212 Geological map showing sampling location at Mt Inglis Station (Modified after Mollan et al 1969)

52

Figure 213 Clay rich sand exposed next to the Mount Ogg road at sampling site I2 53

Figure 214 Geological map showing sampling location at Nyanda Station (Modified after Mollan et al 1969)

53

Figure 215 Differential Pressure (∆P) building up because of varying injection rate (Q) for core F1-1

58


60

Figures 217 ndash 225 Thin Sections 61

Figure 311 Schematic diagram of Core Flooding System facility School of Earth Sciences University of Melbourne

72

Figure 321 Core sample F2-2a before flooding used in Experiment 2 75

Figure 322 Pressure build up against injection rate in a core of 6cm length at 60oC 75

Figure 323 Core sample F1-3a after flooding used in Experiment 3 amp 4 77

Figure 324 Core F1-3b1 and b2 before flooding used in Experiment 5 amp 6 79

Figure 325 Core F2-2 before flooding used in Experiment 7 80

Figure 411 Suspended fines in the fluid samples collected during Experiment 2 85

Figure 412 Permeability (left) and differential pressure (∆P) (right) plotted against injection rate in Experiment 2

85

Figure 413 Silica concentration in the fluid samples during Experiment 2 86

Figure 421 Dissolved cations concentrations and pH at the outflow during experiment 3 The breakthrough of injection pH is marked by vertical bar

88

Figure 422 Measured permeability (left) in response to change in ∆P (right) along the core during experiment 3

88

Figure 431 ICP-OES analysis of fluid samples in exp 4 stage 1 Vertical bars indicate the different stages of the experiment where the injection fluid was changed and the new composition being injected is labelled

90

Figure 432 ICP-OES analysis of fluid samples in exp 4 stage 2 Vertical bars indicate the different stages of the experiment

91

Figure 433 ICP-OES analysis of fluid samples in exp 4 stage 3 Vertical bar indicating beginning of acid injection

92

Figure 434 Permeability calculated in response to the ∆P fluctuation in experiment 4 The blue green and red bars indicate change in fluid composition from alkaline basic to acidic respectively

93

Figure 435 Saturation states of minerals at different pHrsquos from speciation modelling results using GWB Negative and positive values refer to dissolution and precipitation respectively

94

Figure 441 ICP-OES analysis of fluid samples during acid injection in experiment 5 Black bars indicate change of injection fluid

96

Figure 442 Permeability trend (left) during experiment 5 calculated using variation in ∆P (right)

96

Figure 443 Lithium and strontium tracer concentrations recovered at the outflow in Experiment 5 Black bars indicate points of tracer injection

97

xii

Figure 451 Dissolved cations in the fluid samples collected during experiment 6a The injection rate is kept constant to 003mLmin

98

Figure 461 Dissolved cations concentration response as a function of varying injection rates in experiment 6b Black lines indicate change of injection rate

100

Figure 462 Saturation states of minerals during different stages of experiment 6b from speciation modelling of the system using GWB and the lsquothermotdatrsquo database

101

Figure 463 Saturation states of minerals during different stages of experiment 6b from speciation modelling of the system using GWB and the lsquothermocomV8R6+tdatrsquo database

101

Figure 471 Dissolved cation concentration in response to varied injection rate Black bars indicate change of injection rate

103

Figure 472 Saturation states of minerals during Experiment 7a (Time intervals 75 27 and 285 hours) from speciation modelling of the system using GWB and the lsquothermocomV8R6+tdatrsquo database The legends represent injection rate and residence time

103


104

Figure 482 Saturation states of minerals during different stages of Experiment 7b (Time intervals (20 495 and 6825 hours) from speciation modelling of the system using GWB and the lsquothermocomV8R6+tdatrsquo database The legends represent injection rate and residence time

105

Figure 511 Residence time vs outflow silica concentration because of varying injection rates

118

Figure 512 Residence time vs outflow aluminium concentration because of varying injection rates

118

Figure 513 Residence time vs outflow aluminium concentration because of varying injection rates at pH 12

119

Figure 514 Residence time vs outflow silica concentration because of varying injection rates at pH 12

120

Figure 515 Residence time vs outflow potassium concentration because of varying injection rates at pH 12

121


121


122

Figure 518 Residence time vs outflow potassium concentration because of varying injection rates

122

Figure 519 Total silica assigned to quartz at pH 2 and 12 after accounting for error in the stoichiometry of muscovite and kaolinite

125

Figure 5110 Error propagation in the final value of quartz effective surface area at pH 2 amp 12 after accounting for error in the stoichiometry of muscovite and kaolinite

125

Figure 5111 Sensitivity analysis of final Sa values calculated from experiment 7 data lsquoXRDrsquo represents actual weight percent reported in Table 41

127

xiii

Figure 5112 Total aluminium assigned to kaolinite at pH 2 and 12 after accounting for error in the stoichiometry of muscovite and kaolinite

127

Figure 5113 Error propagation in the final value of kaolinite effective surface area at pH 2 amp 12 after accounting for error in the stoichiometry of muscovite and kaolinite

128

Figure 611 Dissolved silica trend of TR modelling versus experiment 7 pH 2 injection

135

Figure 612 Dissolved aluminium trend of TR modelling versus experiment 7 pH 2 injection

135


136


137

Figure 615 Experimental versus modelled calcium trend using experiment 5 data The predicted input parameters used for the calcite dolomite and magnesite effective surface area are 500 4000 and 500 cm2g The weight percentages are 0025 005 and 0150 for calcite dolomite and magnesite respectively

140

Figure 616 Experimental versus modelled magnesium trend using experiment 5 data The predicted input parameters used for calcite dolomite and magnesite effective surface area are 500 4000 and 500 cm2g The weight percentages are 0025 005 and 0150 for calcite dolomite and magnesite respectively

141

Figure 617 Experimental versus modelled calcium trend using experiment 5 data The predicted input parameters used for calcite dolomite and magnesite effective surface area are 500 4000 and 600 cm2g The weight percentages are 0025 005 and 0180 for calcite dolomite and magnesite respectively

141


142

Figure 621 Simplified conceptual model of the reservoir simulated in TOUGHREACT

145

Figure 622 Bromide tracer concentration curve with 50 radial grid cells 148

Figure 623 Bromid tracere concentration curve with 100 radial grid cells 148

Figure 624 The pH and permeability trends within closed radius of wellbore after 20 days of injection

150

Figure 625 The injected fluid pH trends after 20 days of NaOH solution of pH 12 injection and the plume penetration distance from the wellbore Vetical bar indicates initial drop in pH that resulted in permeability change in Figure 64

150

Figure 626 Saturation Indices of minerals contained in the reservoir after 20 days of NaOH (pH 12) injection Positive and negative values indicates precipitation and dissolution

151

xiv

Figure 627 Absolute volume fraction of ankertie and anorthite after 20 days of NaOH injection

152

Figure 628 Change in minerals volume fraction in the reservoir after 20 days of NaOH (pH 12) injection Negative sign indicates dissolution

152

Figure 629 Permeability changes within certain distance of the wellbore in response to the varying injection duration

154

Figure 6210 The injected fluid pH trends after varying total injection period and the plume penetration distance from the wellbore

154

Figure 6211 The injected fluid pH trends within closed radius of the wellbore after varying the injection period

155

Figure 6212 Permeability trends as a function of varying injection rates after 20 days of pH 12 injection

157

Figure 6213 The pH trends within close radius of the wellbore as a function of varying injection rates after 20 days of NaOH (pH 12) injection

157

Figure 6214 The pH trends as a function of varying injection rates after 20 days of NaOH (pH 12) injection showing complete plume penetration into the reservoir

158

Figure 6215 Saturation states of minerals in Catherine Sandstone reservoir after 20 days of injection at maximum injection rate of 10 kgs Positive and negative values indicates precipitation and dissolution

158

Figure 6216 Absolute volume fraction of ankertie and anorthite after 20 days of pH 12 injection at the rate of 10kgs

159

Figure 6217 Dissolved silica concentration in the near wellbore as a function of varying injection rates At 20 days

159

Figure 6218 Permeability and pH trends after 20 days of HCl (pH 2) injection and the radius of influence from the wellbore

161

Figure 6219 Saturation states of minerals contained in the reservoir after 20 days of HCl (pH 2) injection Positive and negative values indicates precipitation and dissolution

161

Figure 6220 Change in minerals volume fraction in the reservoir after 20 days of HCl (pH 2) injection Negative sign indicates dissolution

162

Figure 6221 Dissolved SiO2 in the reservoir after 20 days of NaOH (pH12) and HCl (pH 2) injection at a constant rate of 12 kgs

162

Figure 6222 Comparision of permeability enhancement within near wellbore after 20 days of NaOH and HCl injection at constant injection rate of 12 kgs

163

Figure 631 Core flood data of experiment 5 versus laboratorycore scale TOUGHREACT modelling using Verma amp Pruess pore-perm relation with n=30 16 and emptyc=008 01 015

164

Figure 632 Near well formation scale simulation of pH 2 injection using derived nand emptyc values of Verma amp Pruess equation by experiment 5 data Two different emptyc values are used keeping n constant which resulted in same permeability trend

165

Figure 641 Pressure build-up near the wellbore during CO2 injection as a function of different near wellbore permeabilities

167

xv

LIST OF TABLES Table 11 Reactive surface area values lsquoA rsquo of the minerals used in the initials

models taken from Xu et al 2009 defined for Eq 16 and range of reactive surface area (A) reported in different studies compiled by Black et al 2015

21

Table 12 Calibrated parameter values for the permeability equations of clays (Luijendijk amp Gleeson 2015)

27

Table 21 Petrophysical properties and mineralogical composition of Aldebaran Sandstone reported in Baker 2008

44

Table 22 Petrophysical properties and mineralogical composition of Freitag Formation reported in Baker 2008

45

Table 23 Petrophysical properties and mineralogical composition of Catherine Sandstone reported in Garnett et al 2013

46

Table 24 XRD data of core plug used in the CFS experiments acquired from MCFP University of Melbourne and ANFF

55

Table 25 XRD data of core plug used in the CFS experiments acquired from the Research School of Earth Sciences (Australian National University)

55

Table 26 Porosity and permeability of Catherine Sandstone cores estimated by using fluid saturation method and core flooding system

59

Table 321 Properties of Catherine Sandstone cores used in the experiments 74

Table 322 Experimental Conditions of core flooding 76

Table 323 Conditions of stage 1 2 and 3 in experiment 4 78

Table 324 Standards used in the ICP-OES for fluid sample analysis 82

Table 41 Typical changes in pH for solutions due to change in temperature 87

Table 42 Input concentrations of dissolved cations used in the GWB speciation modelling at pH 12 (Figure 435) The pH of the system is given as a molar concentration of Na+ and H+ used in experiment 4 which adjust the pH to the in-situ pH of the experiment at 60oC

94

Table 51 Dissolution rates of minerals at pH 2 and 12 reported in literatures 110

Table 52 Average steady state concentrations of total dissolved cations in the low flow ratelong residence time experiments used in Eq 52 amp 53 to calculate effective surface areas

114

Table 53 Effective surface area calculated using Eq 53 and range of specific surface area from the literature measured by BET and geometric analysis (Beckingham et al 2016 Black et al 2015)

114

Table 54 Average steady state concentrations of total dissolved cations in high flow rateshort residence time experiments used in Eq 52 amp 53 to calculate effective surface areas

116

Table 55 The average effective surface area calculated using Eq 53 and data from experiments 7a amp 7b The range of reported surface areas from the literature are also tabulated (Beckingham et al 2016 Black et al 2015)

117

xvi

Table 611 The predicted effective surface areas used in the core scale reactive transport model The weight percentage of carbonates used in the model are estimated from experiment 5 data using a mass balance approach

140

Table 621 Hydrogeological parameters for a one-dimensional radial flow model (Garnett et al 2013)

145

Table 622 Initial mineralogical composition used in the model (Garnett et al 2013)

146

Table 623 Kinetic parameters for calculating mineral dissolutionprecipitation rates (Palandri and Kharaka 2004 Xu et al 2009)

146

1

CHAPTER 1

1 Introduction and Literature Review

The following sections (Section 11 amp 12) describe the research problem with an

introduction to the carbon capture and storage (CCS) technology and the role of reactive surface

area in the modelling studies Section 13 gives an insight into the CO2 injectivity issues during

CCS operations and present the concept of geochemical reservoir stimulation to overcome the

problem This is followed by a brief review of the existing literature on the dissolution of rock

forming minerals and an introduction to the ZeroGen and other CCS projects worldwide which

have had CO2 injection limitation Section 14 introduces the reactive transport modelling

methodology used in the current study

11 Relevance and Importance of the Study

The fast-growing industrial uprising and energy consumption since the beginning of the 20th

century is responsible for countless distresses associated with the stability of Earthrsquos natural

environment Among the hazardous bi-products of industrialization CO2 emission in the

atmosphere is a globally accepted environmental concern Tackling the problem of rising CO2

emissions from anthropogenic sources is receiving increased attention by scientists CCS (Carbon

Capture and Storage) is a technology being considered as one of the options for reducing the

emissions of CO2 from industrial sources emitting large volumes of greenhouse gases such as

power station flue gases including coal and hydrocarbon fired boilers blast furnace gas and IGCC

(Integrated Gasification Combined Cycle) (IPCC 2005) The process of CCS involves the capture

of anthropogenic CO2 emitted by the industry followed by its transportation to a site where it is

injected into deep sedimentary formations acting as permanent storage reservoirs At present most

of the active CO2 injection sites are associated with oil and gas production fields as a part of

Enhanced Oil Recovery (EOR) (Figure 111) A fair number of CO2 injection sites are also

currently operational targeting deep saline formations (Figure 111) Although such reservoirs

sum up a significant number in terms of storage volume there are numerous other sedimentary

basins around the globe with a prospective capacity for large scale CO2 storage (Figure 112) An

early assessment suggests sedimentary basins around the globe have the technical potential of

2

storing approx 2000 Gt of CO2 (IPCC 2005) The future of CCS lies in the adequate utilization

of such unexplored sedimentary formations The major challenge in utilising unexplored

sedimentary basins is the in-depth reservoir characterization and managing the resources within

One of the key concerns for the development of a CO2 storage site is to maintain sufficient

CO2 injectivity without exceeding the hydraulic fracturing pressure within a geologic formation

(Lamy-Chappuis et al 2014 Peysson et al 2014 Andre et al 2014 Garnett et al 2013 Lucier

and Zoback 2008) Those sandstone reservoirs that are characterized as having large storage

volumes could in fact have a limited potential for storing CO2 due to restricted reservoir flow

impacting gas injectivity Thus it is necessary to account for the rate of gas injection in CO2 storage

capacity estimations (Lucier and Zoback 2008) An example of such a case in Australia was the

ZeroGen CCS project in Queensland Despite having a massive storage capacity the project was

not able to proceed further with one of the major shortcomings being a low permeability of the

storage units in the Northern Denison Trough causing limitations for the projected industrial scale

CO2 injection (Garnett et al 2013)

In order to utilise such significant subsurface storage reservoirs for CCS the issue of

insufficient permeability shall be addressed through the development of new techniques or

technologies There are various reasons for low permeability in porous sandstone reservoirs

(Byrnes 1996 Peysson et al 2014) but low permeability is often associated with

lithologicmineral variables and matrix cementation reducing the connectivity of pore space within

a formation There are certain minerals such as feldspar chert and other lithic rock fragments that

influence petrophysical properties of sandstone as a consequence of mineral diagenesis and

alteration (Byrnes 1996) Other than natural factors different mechanisms such as secondary

mineral salt precipitation and the mobilization of fines can alter rock permeability around the

wellbore during CO2 injection (Peysson et al 2014 Lamy-Chappuis et al 2013)

Geochemical reservoir stimulation by injecting acids (acidization) and other pH-controlled

solutions has the potential to promote mineral dissolution and thus increase permeability of the

reservoir facilitating higher injection rates of CO2 The technique of geochemical stimulation by

acids has been applied in the oil and gas industry to remediate with reservoir damage and scaling

around wellbores (Zaman et al 2013 Shafiq et al 2013 Portier 2010 Kalfayan 2008 Portier et

al 2007 Thomas et al 2002) This study focuses on the feasibility of geochemical reservoir

3

stimulation in undamaged siliciclastic rocks to enhance their permeability without formation

damage The approach will be tested at laboratory scale using the most suitable reagents to observe

pore scale changes in the petro-physical properties of the rock samples and to minimize unwanted

environmental impact Furthermore the efficiency and feasibility of geochemical reservoir scale

will be tested using the coupled reactive-transport model under variable conditions with the help

of TOUGHREACT code

4

Figure 111 Large scale CCS projects worldwide (After Garnett et al 2013)

5

Figure 112 Distribution of prospective sedimentary basins around the world that could have

potential for CO2 storage (After IPCC 2005)

12 Reactive Surface Area of Minerals

Flooding a 5 ndash 10cm long core in the laboratory with highly reactive fluids is a practical way

to demonstrate the effect of geochemical stimulation at a laboratory scale To plan and design a

field scale experiment it is necessary to demonstrate the impact of frequently dissolving minerals

due to flooding with reactive fluids on the rockrsquos petrophysical properties at a larger field scale

Groundwater modelling tools can play a vital role in studying the feasibility of geochemical

stimulation at field scale Before going towards actual field experiments it is essential to

demonstrate the injected fluid penetration and the radius of influence around a wellbore in order

to evaluate the efficiency of the technology This geochemical stimulation technique requires a

thorough study of both fluid transport in the porous media and fluid-rock interaction to modify the

rockrsquos pore geometry A coupled reactive transport model is necessary to achieve the goal of this

project A reactive transport model is capable of demonstrating and predicting the evolution of

porous media due to physical and chemical changes occurring in the natural system (Steefel et al

2005 Beckingham et al 2016) For an accurate prediction of the extent of mineral dissolution it

is necessary to choose the right kinetic parameters that control these processes The dissolution

rates of quartz and various other minerals have been derived and compiled by several authors

(Stober 1967 Rimstitdt and Barnesh 1980 Chou and Wollast 1985 Knauss and Wolery 1987

6

Carrol amp Walther 1990 Bennett 1991 House and Orr 1992 Ganor et al 1994 Palandri and

Kharaka 2004 Oelkers et al 2008 Crundwell 2015) The most uncertain parameter to this date

is the reactive surface area of individual minerals in a consolidated rock which is also referred as

specific effective and accessible surface area in different publications (Helgeson et al 1984

Velbel 1985 Haggerty and Gorelick 1995 Kieffer et al 1999 Colon et al 2004 Noiriel et al

2009 Luquot and Gouze 2009 Peters 2009 Phan et al 2011 Gouze and Luquot 2011 Landrot

et al 2012 Golab et al 2013 Navarre-Sitchler et al 2013 Hellevang et al 2013 Bolourinejad

et al 2014 Lai et al 2015 Black et al 2015 Beckingham et al 2016 Beckingham et al 2017)

There is a broad range of reactive surface area values for individual minerals used in the reactive

transport modelling studies that are mostly calculated from geometric and BET (Brunauer Emmett

and Teller) analysis (Audigane et al 2007 Noiriel et al 2009 Xu et al 2009 2010 Hellevang

et al 2013 Yang et al 2014 Black et al 2015 Beckingham et al 2016) The rate of mineral

dissolution is directly proportional to its reactive surface area (Eq12 Section 14 for mathematical

definition) Therefore an unconstrained value of reactive surface area in the reactive transport

models is likely to result in unrealistic results related to mineral dissolution and subsequent

changes in porosity and permeability Also the reactive surface area estimates from BET analysis

is not the most accurate representation of rock minerals contained in a natural reservoir (Black et

al 2015) Keeping in view the sensitivity of this parameter in the current study there is a need to

develop a methodology through which the reactive surface area of minerals contained in a

consolidated rock can be estimated This will represent the site-specific surface area of minerals

in the targeted reservoir rock In this project we developed core-flooding experiments to estimate

the surface area of quartz kaolinite and K-bearing mica in a consolidated Catherine Sandstone

samples from a prospective CO2 storage site The calculated surface area of individual minerals

will be referred as effective surface area (ESA) Our approach is based on the classic reactive-

transport equation far-from-equilibrium standard mineral dissolution rates as well as the

experiment specific fluid residence time and the cation concentrations in the outflow solution The

results will be applied in reactive-transport simulations near the wellbore of a prospective CO2

storage reservoir to determine whether CO2 injectivity can be improved through geochemical

reservoir stimulation

7

13 Enhanced Injectivity of CO2 for Storage

131 CO2 Injectivity

One of the primary concerns in the selection of a CO2 storage site is the presence of

sufficient porosity and permeability of the prospective storage reservoir This is so that the capacity

of injecting CO2 is at a sufficiently high rate also referred to as CO2 injectivity An adequate fluid

flow within the geological formation depends on the connectivity of natural pore spaces contained

in the rock which is represented as permeability The connected network of pore

spacespermeability can be clogged by natural processes such as mineral diagenesis and alteration

as well as by mobilization of fines and precipitation triggered by CO2 injection Insufficient

injectivity due to clogged pore spaces may lead to risks associated with safety and economics of

the CCS project (Lucier and Zoback 2008 Garnett et al 2013 Lamy-Chappuis et al 2014

Peysson et al 2014 Andre et al 2014) Furthermore reservoir rock failure to bear a high injection

rate can initiate formation damage An industry scale CO2 storage project typically has an

anticipated injection rate of CO2 greater than one million tonnes per year (Lucier and Zoback

2008 Garnett et al 2013) The rate at which CO2 is injected into the reservoir affects the cost per

ton of CO2 stored These parameters are essential to assess the feasibility of a geological formation

for CO2 storage (Lucier and Zoback 2008) To improve injection rates with an increase in the

number of injection wells to avoid formation damage bring about growth in the cost of storage

Enhancing injectivity with the help of micro seismic activity can result in severe environmental

problems giving rise to concerns from the community as well as difficulties in public acceptance

for CCS

132 Geochemical Reservoir Stimulation

Geochemical reservoir stimulation refers to the technique that enhances the flow properties of

a rock by injecting reactive fluids into its reservoir The basic mechanism involves dissolution of

the minerals that occupy the fluid pathways within the rock limiting its natural permeability due

to overgrowth The fluid is injected below the formation fracturing pressure point thus enhancing

the permeability without any mechanical deformation or micro seismic activity The history of

geochemical stimulation by acids in the oil and gas industry begins in the early 20th century Wells

were stimulated by injecting concentrated hydrochloric acid (HCl) to remove scaling around the

8

wellbore and increase the productivity of hydrocarbon (Kalfayan 2008) The methodology was

improvised upon later by using different combinations of acids as chemical reagents to stimulate

reservoirs (Zaman et al 2013 Shafiq et al 2013 Portier and Vuataz 2010) The selection of the

chemical reagent depends on the type of rock and dominant mineralogy For quartz dominated

sandstone reservoirs a combination of HCl and mud acid (HF) is commonly used Similarly

carbonate reservoirs such as limestone and dolomite are usually acidized by using concentrated

hydrochloric acid that creates worm holes enhancing the fluid flow pathways (Kalfayan 2008)

This technique is also successfully implemented in the geothermal energy sector to increase

geothermal well production rates up to commercial levels Enhanced fluid flow in geothermal

systems can be established by using a combination of hydrochloric and hydrofluoric acid also

known as regular mud acid (RMA) to stimulate deep seated igneous and metamorphic rocks

(Portier and Vuataz 2010) Fluid flow within granitic rocks depends on the rockrsquos internal fracture

networks that are in most cases clogged by hydrothermal vein deposits Mud-acid is used to

dissolve these secondary minerals that are precipitated in the fractures of granitic rocks therefore

enhancing fluid circulation According to Kalfayan (2008) acidizing can be categorized into three

different categories based on technique Depending on the purpose of stimulation and type of rock

needing to be treated one can employ acid washing matrix acidizing or fracture acidizing

methods

bull Acid washing is the basic procedure for wellbore cleaning Its main target is to treat the

clogging that is causing flow restriction around the wellbore Hydrochloric acid used to

wash out scaling rust and other debris that limit flow within the wellbore

bull Matrix acidizing is applicable for both carbonate and sandstone reservoirs In the case of

sandstone the technique is designed to remove formation damage that is causing plugging

in the perforation and the pore network of the formation around the wellbore When acid

is injected it flows through the pore spaces allowing for the dissolution of the fines within

the pore network that cause flow restriction As the acid flows further it cleans fine

particles stuck in pore throats and along the pore wall On the other hand matrix acidizing

in carbonates forms fluid conductive pathways known as wormholes through the rock (Liu

et al 2012 Cohen et al 2008) Wormholes are caused by acids following a path of least

resistance in a sandstone which is governed by heterogeneity in the permeability of the

rock The wormholes can spread beyond the wellbore environment and form structures that

9

mirror the holes made by earthworms within the soil The structure further extends from

perforations in small branches connected to the main preferential flow pathway In case of

strong acids such as HCl the fluid generates a single wormhole without any branches

Weaker reagents such as carboxylic acids tend to create more branches coming out of the

main flow pathway (Kalfayan 2008) There are certain retarded acid systems such as

polymer surfactant-gelled acids and emulsified and foamed acids that produce features

similar to those of weak acids in carbonate reservoirs Furthermore the formation of

wormholes also depends on the temperature and the rate at which an acid is being injected

bull Fracture acidizing is only applicable in carbonate formations The main purpose is to

bypass formation damage and stimulate undamaged fromation in vugular and naturally

fractured chalks limestone and dolomite Fracture acidizing is intended to penetrate deeper

into the carbonate formation Acid is injected into the fractures causing dissolution etching

along the fracture wall The conductivity is retained by asperities that hold the conductive

channel open (Kalfayan 2008)

133 Dissolution of Rock Forming Minerals

The current research is focused on the permeability enhancement of siliciclastic

sedimentary rocks Among the reservoir stimulation techniques described in the previous section

matrix acidizing is more relevant to the aim of this project Since an increase in permeability

depends on mineral dissolution in the rock the selection of the dissolution reagent will be based

on the mineralogical composition of sandstone accordingly As silicate mineral weathering is an

important process within the Earthrsquos crust the dissolution mechanism of various silicate minerals

have been extensively studied in literature (Stober 1967 Rimstitdt and Barnesh 1980 Chou and

Wollast 1985 Knauss and Wolery 1987 Carrol amp Walther 1990 Bennett 1991 House and Orr

1992 Ganor et al 1994 Palandri and Kharaka 2004 Mitra 2008 Oelkers et al 2008

Crundwell 2015) Several polymorphs of pure SiO2 exist in nature such as quartz cristobalite and

amorphous silica Quartz has been reported as the most common and stable rock forming silica

mineral in the Earthrsquos crust It is made up of a continuous framework of silicon and oxygen

tetrahedra with an overall formula of SiO2 There are numerous studies regarding the dissolution

rate of quartz being a function of pH and temperature (of the solution) (Van Lier et al 1960

Stober 1967 Rimstidt and Barnes 1980 Knauss and Wolery 1987 House and Orr 1992)

10

Quartz dissolution rates are enhanced at a high pH in a mechanism controlled by the nucleophilic

attack of OH- ligands on a silica atom (Mitra 2008) Studies have shown a strong positive

correlation between the increasing dissolution rate of quartz and the rising pH level of the solution

whereas the effect of a low pH on the dissolution rate of quartz is not significant (Figure 131)

An experimental study by Mitra and Rimstidt (2009) has demonstrated exceptionally high

dissolution rate of quartz in the presence of fluoride at a pH of 3 Another study by Bennett et al

(1988) has also shown the dissolution rate enhancement of quartz at neutral pH in the presence of

organic acids Similarly feldspar dissolution has been studied extensively by various authors

(Black et al 2015 Crundwell 2015 Palandri and Kharaka 2004 Stoessel and Pittman 1990

Knauss and Wolery 1986 Chou and Wollast 1985) Feldspar forms a group of solid solution

minerals with the end members K-feldspar (KAlSi3O8) albite (NaAlSi3O8) and anorthite

(CaAl2Si2O8) Increase in the rate of feldspar dissolution under acidic conditions have also been

reported in various literatures (Figure 132) A combination of HCl KCl and organic acids such

as acetic and oxalic acids has been used in these studies as a dissolution reagent The above cited

literature is used in this research project to identify the most suitable mineral specific chemical

reagent

11

Figure 131 Quartz dissolution rate as a function of pH and temperature points are the

experimental data and lines are modelled fits to the data

Figure 132 Dissolution rate of albite at 25o C at acidic neutral and alkaline pH

12

134 ZeroGen Carbon Capture and Storage Project

The ZeroGen Carbon Capture and Storage (CCS) project was initiated by the Queensland

government in 2008 to develop a 500 MW Integrated Gasification Combined Cycle (IGCC) CCS

power plant and storage facility in Central Queensland Australia The project aimed to store 60-

90 million tonnes of CO2 in the subsurface throughout its lifetime to help reduce the net emission

of greenhouse gas in Australia (Garnett et al 2013) The main targeted storage reservoir in the

ZeroGen project phase was the Catherine Sandstone in the Northern Denison Trough within the

Bowen Basin due to its comparatively high porosity and permeability (gt100 md) and the proximity

to the Stanwell coal fire power plant The storage unit is situated at the depth of approx 850 metres

with a total areal extent of 886 km2 out of which 481 km2 has an availability of supercritical

conditions The project was terminated later due to the combination of economic and technical

problems Apart from financial shortcomings the major technical limitation that caused the project

shutdown was the predicted failure of the storage units to sustain the desired CO2 injection rate of

2 to 3 million tonnesyear during several injection tests The reason is highly heterogeneous nature

of Catherine sandstone with variable permeability due to sedimentary facies variation As a

consequence the project did not progress beyond the prefeasibility stage despite of having a large

reservoir storage capacity The geology of the ZeroGenrsquos targeted reservoir-seal play is used in

this research project as a case study to develop strategies to mitigate insufficient injectivity and

study the feasibility of geochemical stimulation at field scale Initial experimental and modelling

work will be based on the petro-physical and mineralogical properties of the Catherine sandstone

135 Insufficient Injectivity Reported in CO2 Storage Reservoirs Around the World

CO2 storage projects which have experienced injectivity problems due to low permeability

of the sandstone storage reservoir include In Salah (Krechba) Algeria In Salah was an industrial

scale CCS project The CO2 was injected into 20 meters thick Krechba sandstone reservoir with

porosity of 10-17 and average permeability of 10 mD (Oye et al 2013 Verdon et al 2013)

Due to tight nature of reservoir rock three horizontal injection wells were drilled to maximize the

gas injectivity Furthermore the permeability enhancement was induced by micro seismic activity

Similar injectivity limitations were observed at Snoslashhvit field Barent Sea The CO2 was injected

into Tubaringen formation which consists of sand deposited in fluvial to tidal sediments with highly

variable horizontal permeability (Hansen et al 2013) A high reservoir pressure builds up due to

13

CO2 gas injection was experienced due to low permeability of sandstone caused by quartz

diagenesis and cementation Figure 133 summarizes the permeability of different CO2 storage

reservoirs around the globe The sandstone storage reservoirs of MRCSP RE Burger and

WESTCARB Cholla (Figure 133) had also been reported as unfeasible due to insufficient

injectivity (Hosa et al 2011) The commercially productive oil amp gas fields consist of reservoirs

with permeability in the range of 100-800 mD (Hosa et al 2011) The reservoirs below a 100 mD

permeability are more likely to encounter inadequate injection and productivity Among the listed

storage projects in Figure 133 approximately 50 of the storage reservoirs falls in the category

of low permeability below the range of 100 mD Thus it is necessary to build an effective

geochemical reservoir stimulation (field operation) setup that can be implemented as a basic

operational tool in CCS projects


14 Groundwater Flow and Reactive Transport Modelling

Groundwater flow and reactive transport modelling is a vital tool in simulating the combined

effects of physical chemical and biological processes within a geological porous media The fluid

flow in porous media is described by Darcyrsquos Law as reported in Steefel et al (2005)

14

=minus ( minus ) (11)

where Q is the flow velocity vector k is the absolute permeability represents viscosity P is the

pressure is density and g is the gravity vector

Coupling fluid flow and solute transport with chemical reactions is referred to as reactive transport

modelling It is a useful technique that can be applied to solve several problems related to fluid

rock interaction in a natural geological system (Xu et al 2012) Most groundwater modelling

codes can simulate multiphase flow problems that modify Darcyrsquos law by including a relative

permeability variable in the equation (Pruess et al 1999) However since it is not required in the

current project it is not discussed in the chapter Furthermore groundwater transport modelling

consists of mass and energy balance equations that describe fluid and heat flow in the system

(Konikow amp Mercer 1988 Pruess et al 1999 Steefel et al 2005) The masssolute transport in

these models is mainly governed by advection or hydrodynamic dispersion and diffusion

The primary goal of this research is to develop a reactive transport model simulating mineral

dissolution and associated changes in porosity and permeability at field scale The first immediate

phase is to build a reactive transport model that can simulate the effects of geochemical reservoir

stimulation on the flow properties of a sandstone reservoir As a case study petrophysical and

mineralogical data of the Catherine Sandstone in the area of the ZeroGen CCS project is being

used in the preliminary models A coupled reactive transport code TOUGHREACT has been used

to simulate the effects of geochemical stimulation at field scale with varying fluid composition

and initial conditions A preliminary understanding of the geochemical reactions between rock and

the injected fluid of varying pH and temperature can be achieved through such modelling

141 Geological Model

Building a conceptual geological model is the first step in constructing a laboratoryfield

scale reactive transport model The reservoir dimensions (the extent of the reservoir and thickness)

boundary conditions (constant flow or no flow) rock types and petrophysical properties of the

rock is assigned to the modelled domain For the current project a 1D (one dimensional) field

scale radial flow model was built through a graphic user interface software called PetraSim It is

15

coupled with the TOUGH codes that can generate input files and execute reactive transport

simulations through the same graphical interface (Yamamoto 2008 Alcott et al 2006)

1411 Types of Grids in PetraSim

The software package lsquoPetraSimrsquo provides tools to build two- and three-dimensional grids

with complex boundary and initial conditions in a convenient way There are multiple ways to

indirectly assign the boundary conditions using grid cells The edge of the geological model is by

default assigned as a no flow boundary Each grid cell contains a lsquofixed statersquo option that will keep

the pressure temperature and other variables constant in that specific cell Likewise in order to

assign a constant flow boundary around a reservoir the volume of the boundary cells can be

increased to a large infinite number As a result the cells will remain unaffected from the

surrounding variation in temperature and pressure The pressure and temperature can be fixed

independently by changing the material of the boundary cells so that the thermal conductivity is

zero Similarly the permeability of the boundary cells can be reduced to zero which in turn will

fix the temperature The software package comprises of three different types of meshing options

that are described in detail below

1412 Regular Mesh

A regular mesh consists of rectangular boxes that are made up of six-sided cells (Figure

141) The cells are designed in a way that fit the bounding box of the model The cells outside

the model boundary are automatically disabled to represent the irregular shaped natural geological

layers Cell size is defined by the length of the x and y values and can be constant in both directions

or vary in either direction using customised cell sizes (Figure 142)

16

Figure 141 Rectangular hexahedron cells representing regular mesh type

Figure 142 Customize meshing option on the left allowing incremental grid density on the

right

1413 Polygonal Mesh

A polygonal mesh consists of cells that can conform to any boundary and provide

automatic refinement around the wells (Figure 143) The cell size is defined in terms of area in

m2 with additional options to provide the cell area around the wellbore The cells around a wellbore

17

can be further refined by giving a minimum refinement angle Polygonal mesh provides a

convenient way to represent a 3D geological model with injection and production wells

Figure 143 Polygonal mesh with irregular model boundaries

1414 Radial Mesh

Radial meshes are based on a regular mesh but only allow for a 2D representation of the

grid The 2D grid represents a slice of an axisymmetric cylindrical mesh centred at (0 0 0) as

shown in Figure 144 (Left) The X-division in the radial mesh represents the radial division and

there will always be a maximum of 1 Y-division But all cell data is displayed and written to the

TOUGH input file with the correct cell volumes and connection areas to represent the cell revolve

around the centre of the cylinder In Figure 144 the red line represents the portion of the cylinder

that is modelled by the radial mesh The minimum and maximum lsquoXrsquo in Figure 144 (Left)

represents the total length of the model illustrated in the Figure 144 (Right) It allows to save

computational time It can also be very useful to create a quick and simple 2D sub-reservoir scale

model accounting for the effects of fluid rock interaction around the wellbore

18


142 Reactive Transport Modelling using TOUGHREACT

TOUGHREACT is a coupled reactive transport code to model subsurface multiphase fluid

and heat flow solute transport and chemical reactions in a geological system (Xu et al 2012) The

code was developed by Xu and Pruess (1998) as an extension of the multiphase fluid and heat flow

code TOUGH2 (Pruess 1991) and includes reactive geochemistry in its framework It has a

widespread application in non-isothermal multi-component reactive fluid flow and geochemical

transport problems such as large-scale CCS modelling nuclear waste emplacement acid gas

injection mineral trapping and geothermal and environmental problems (Sonnenthal et al 2005

Audigane et al 2007 Xu et al 2007 Sengor et al 2007 Xu et al 2012) TOUGHREACT is

capable of generating three dimensional porous and fractured geological models with physical and

chemical heterogeneity The code can accommodate a large number of chemical species present

in liquid gas and solid phases More importantly it considers chemical reactions such as

dissolution and precipitation depending on local equilibrium and kinetic controls This allows the

model to calculate changes in porosity and permeability as a result of mineral precipitation and

dissolution using different empirical relationships incorporated in the code (Xu et al 2012) The

porosity and permeability changes due to mineral precipitation and dissolution can be modelled

using several equations built into the code

19

1421 Modelling Reaction Kinetics

The rate law for mineral dissolution and precipitation kinetics used in TOUGHREACT is

stated below (Lasaga et al 1994 Xu et al 2004)

$ = plusmnamp$lowast$|1 minus Ω$| (12)

where n denotes kinetic mineral index (positive values of rn indicate dissolution and negative

values precipitation) amp$lowast is the rate constant (moles per unit mineral surface area and unit time)

which is temperature-dependent An is the final reactive surface area of the mineral in contact with

one kilogram of H2O and Ω$is the mineral saturation index (Xu et al 2004) For many minerals

the rate constant k can be calculated from a combination of three mechanisms defining reactivity

under different pH regimes (Lasaga et al 1994 Palandri and Kharaka 2004)

amplowast = amp+$exp[123456 789 minus

88+=] (13)

amplowast = amp+exp[123

6 789 minus8

8+=]A$ (14)

amplowast = amp+Bexp[123C

6 789 minus8

8+=]AB$C (15)

where superscripts or subscripts nu H and OH indicate neutral (H2O) acid and base mechanisms

respectively Ea is the activation energy in J mol-1 amp+ is the rate constant in molem2s at 25oC R

is the gas constant in J mol-1 K-1 T is absolute temperature in Kelvin a is the activity of the

subscripted species and ni is an exponent constant

1422 Modelling Surface Area

In TOUGHREACT the reactive surface area of the minerals to be used in the above

equation (Eq 12) is calculated by the general relationship

An = (Vfrac Am + Aprc) Cw (16)

Where the value An represents the final reactive surface area of the minerals in the unit

m2mineralkgwater Am is the surface area of the mineral in the units m2

mineralm3mineral calculated from

the initial surface area value (D ) which is defined by the user (Eq 18) Aprc is an optional

parameter that represents the precursor surface area in units m2surfacem3

medium Vfrac is the volume

20

fraction of the minerals already present in the model in units of m3 mineralm3

solids and Cw is the wetted

surface conversion factor in units of kgwaterm3medium (Xu et al 2004)

D is the initial surface area of the mineral input by the user In the current simulations the surface

area of the minerals (D ) is taken from Xu et al (2009) (Eq 18 Table 11) It is the mineral

surface area in the rock matrix estimated by using the geometric area of cubic array of spheres

(Sonnenthal et al 2005) Since the reactive surface area of the minerals are highly uncertain the

calculated surface areas could be overestimated by this method In Sonnenthal et al (2005) the

calculated reactive surface areas have been further reduced by an order of magnitude to increase

its reliability The values of Am Vfrac and Cw vary throughout the passage of simulation as a result

of mineral dissolution and precipitation also due to the change in liquid saturation of the medium

The value of Vfrac is calculated from the initial mineral volume fraction fm in m3mineralm3

solids and

porosity of the medium

Vfrac = fm (1ndashoslash) (17)

The value of Am is calculated by the surface area input given by the user (Eq 18) while Aprc remains

constant in the course of simulation

Am = E + $GH (18) E is the initial reactive surface area input by the user and $GH is the surface area to approximate

the nucleation effects which is implemented as function of mineral grain radius (r) The value of

$GH is initially set to zero It can be calculated by using (Eq 19) if the grain radius (r) is provided

in the model

$GH=05r (19)

The wetted surface conversion factor Cw is defined as

Cw = ρw Oslashmed Sw (191)

Where ρw is the density of water in kgL Oslashmed is the porosity of the medium and Sw is liquid

saturation

21

Table 11 Reactive surface area values lsquoD rsquo of the minerals used in the initial models taken from

Xu et al 2009 and defined in Eq 16 and range of reactive surface area (A) reported in different

studies compiled by Black et al 2015

Mineral I (m2g) A (m2g)

Albite 00098 0007 ndash 1

Anorthite 00098 0007 ndash 1

K-feldspar 00098 0007 ndash 1

Quartz 00098 0008 ndash 1

Chlorite 015 0001 ndash 10

Illite 015 005 ndash 100

Different approaches have been applied by researchers (Noiriel et al 2009 Pham et al

2011 Hellevang et al 2013) to incorporate the change in surface area with

dissolutionprecipitation of the minerals One way is to put the molar weight of the mineral in the

surface area equation

A=λ n M Ao (110)

Where A is the final reactive surface area in m2g M is the molecular weight n is the number of

moles λ is the reactive fraction of the total surface area of the mineral and Ao is the specific surface

area of the mineral by BET analysis (Pham et al 2011 Hellevang et al 2013) Another equation

used by Noiriel et al (2009) to estimate the increase in reactive surface area with dissolution is by

using the initial and final concentration of minerals

$ = D 7 JJK=1M

(111)

Where C0 and C are the initial and final mineral concentrations and Am is the initial reactive surface

area (Noiriel et al 2009) Comparing all the above surface area equations Eq 16 which is

integrated in TOUGHREACT contains several additional parameters That includes wetted

surface conversion factor (Cw Eq 16) containing porosity oslashmed and water saturation (Sw) For a

fully saturated system Sw = 1 and for unsaturated system Sw lt 1 A very low liquid saturation

22

leads to very small surface area that is contacted by water Furthermore the mineral surface area

parameter Am can also be computed by $GH (Eq 19) which is implemented as a function of

grain radius that makes Eq 16 more refined (Xu et al 2012)

1423 Modelling Porosity

The matrix porosity of the reservoir is directly affected by the variation in the mineral

volume fraction because of dissolution and precipitation Such changes in the porosity influence

fluid flow in the reservoir Porosity changes in TOUGHREACT are considered in the code by the

following equation

empty = 1 minus sum OD$DDP8 minus O (112)

Where nm is the number of minerals ODis the volume fraction of mineral m in the rock and O is

the volume fraction of nonreactive rock As the value of OD changes the matrix porosity is

recalculated at each time step The porosity in the code is not allowed to go below zero

1424 Permeability Equations Incorporated in TOUGHREACT

The matrix permeability of the reservoir varies as a result of changes to the porosity value

during the simulation This change is incorporated in the TOUGHREACT code using three

different relationships Current simulations are performed by using ratios of permeability

calculated from the Kozeny-Carman relationship (Bear 1972) below

Q = QR (81emptyS)T

(81empty)T 7emptyemptyS=M (113)

Where oslashi and oslash are the initial and final porosities respectively and κi and κ are the initial and final

permeability respectively Changes in the grain size tortuosity and specific surface area are

ignored in the above relationship Kozeny-Carman relationship is the most common way of

extrapolating permeability from porosity The Kozeny-Carman relationship is originally derived

for a medium with pipe conduits rather than for a granular medium Other than Kozeny-Carman

a cubic law can be used in the code to simulate a fractured medium which is not relevant for this

study therefore has not been discussed The porosity and permeability of a geological media

depends on several other factors such as the pore size distribution pore shapes and connectivity

23

These factors are not considered in the Kozeny-Carman and cubic law (Taylor 1948 Michaels amp

Lin 1954 Freeze amp Cherry 1977 Revil amp Cathles 1999 Luijendijki amp Gleeson 2015) Thus

both of the relationships described above may not be representative of a more complex geological

system (Verma amp Pruess 1988) Laboratory and field experiments have shown that minimal

variation in porosity can cause significant change in the permeability values (Vaughan 1987 Pape

et al 1999) Verma and Pruess (1988) described a relationship to couple porosity and permeability

that can be used for a more complex geological system below

S= 7empty1emptyUemptyS1emptyU

=$V

(114)

Where most parameters are defined in Equation 113 above emptyc is the critical porosity value at

which permeability goes to zero and Wis a power law exponent derived from a pore-body-and-

throat model in which permeability can reduce to zero with a limiting (ldquocriticalrdquo) porosity

remaining Verma amp Pruess (1986) highlighted the experimentally derived value of emptyc to be

constant in all sandstones whereas W varies from 07 to 2 Xu et al (2012) derived values ranging

from 08 to 092 for emptyc and 2-13 for W by combining experimental results from various field

studies Xu et al (2004b) also show that lower emptyc requires a larger W value to match the

experimental data Both parameters depend on the geological medium Xu et al (2012) concluded

that the Verma and Pruess relationship (Eq 114) with a more sensitive coupling of permeability

to porosity than the KozenyndashCarman relationship is found to better capture permeability at the

field scale

15 Porosity-Permeability Relations Described in Literature

The following section (Section 15) discusses the complex relationship between porosity and

permeability and various techniques described in the literature to extrapolate the change in

permeability as a function of porosity in different siliciclastic rocks To predict the permeability

enhancement by geochemical reservoir stimulation with the help of reactive transport modelling

it is essential to understand and choose the most appropriate porosity-permeability relationship

Section 16 introduces a methodology which is applied in the current modelling study to

extrapolate the permeability due to change in porosity of Catherine Sandstone

24

151 Permeability

Permeability is a basic flow property of the rock that depends on interconnectivity of the

pore spaces and is generally denoted by κ (units of m2) It is most commonly measured in the

laboratory by conducting core flooding experiments It can be defined as the measure of the

capacity of the porous medium to transmit fluid (Dandekar 2013) The mathematical expression

for permeability was developed by Henry Darcy in the 19th century and is still being used by the

petroleum industry The mathematical equation was derived by investigating the flow of water

through sand filters which is analogous to the flow of a fluid through a cylindrical core plug The

petroleum industry adopted the unit ldquodarcyrdquo for the same permeability parameter (k) One darcy

(D) is equal to 9869 x 10-13 m2 which represents a relatively high permeability because most

reservoir rocks have a permeability below 1 darcy Permeability is often reported in millidarcy

(mD) for convenience of scale

152 Porosity-Permeability Relationship

The permeability of a sandstone is a function of porosity but their relationship varies in

different reservoirs around the world A number of porosity-permeability relationships acquired

from core data of different sandstone reservoirs indicate that the logarithm of permeability is

linearly proportional to porosity (Nelson 1994) However the slope of porosity-permeability

curve and uniformity of the data when plotted against each other differs from reservoir to reservoir

(Bourbie amp Zinszner 1985 Vaughan 1987 Pape et al 1999 Luijendijk amp Gleeson 2015) Such

variations are due to environmental and depositional factors for instance changes in the grain size

distribution of sandstone sorting and digenetic history of the reservoir (Nelson 1994) Even in the

same formation there is no defined porosity-permeability trend line It is possible to have very

high porosity in sediments such as clays and shale that have low permeability (Nelson 1994 Revil

amp Cathles 1999 Luijendijk amp Gleeson 2015) In sandstone the increase in grain size from sand

to gravel causes a rise in permeability while the porosity is reduced However digenetic minerals

that cement the pore space of sandstone reduce the porosity as well as permeability in an equal

proportion (Nelson 1994)

25

153 Predicting Permeability of Pure Quartz Sand

There are a number of models that predict the permeability of pure sandstone and clays

using a porosity-permeability relationship These equations are then calibrated by experimental

data for more realistic results One of the earliest works done in this regard includes the Kozeny-

Carman equation that is incorporated in TOUGHREACT to calculate the permeability of pure

granular sand The equation considers connected pore spaces represented by a series of cylindrical

pipes and calculating flow through them Studies have shown that the Kozeny-Carman equation

gives realistic results when applied to calculate the permeability of high porosity sandstones but

overestimates the permeability of low porosity mediums such as clays (Bourbie amp Zinszner 1985

Luijendijk amp Gleeson 2015) Figure 151 shows permeability as a function of increasing porosity

calculated by using the Kozeny-Carman equation The modelled permeability fits well with the

experimental permeability of pure quartz sand after calibrating the model with the experimental

data using a specific surface parameter in the equation (Bourbie amp Zinszner 1985)


26

154 Predicting Permeability of Clays

The Kozeny-Carman equation when applied to extremely low permeability rocks such as

clay gives a less realistic estimation of permeability (Figure 172) Similar observations have

been reported in various other studies (Taylor 1948 Michaels amp Lin 1954 Freeze amp Cherry 1977

Luijendijk amp Gleeson 2015) In order to predict the porosity-permeability relationship of clays

accurately an empirical power law equation was introduced by researchers in which the

permeability of a rock is calculated as a power law function of porosity (Eq 115) This law is

reported in Revil amp Cathles (1999) and Tokunaga et al (1998) calculating the permeability as

follows

Q = QR(emptyemptyS)DV

(115)

Where ki is the initial permeability at reference porosity emptyR (m2) and X is an empirical

coefficientcementation exponent that can be obtained from electrical conductivity measurements

The value of X varies with the pore geometry of the clay in the range of 1-4 Small values of X(lt

25) represent reservoirs where pores are well interconnected and most of the pore space is filled

with fluid A high cementation exponent (X gt 25) indicates large pore spaces that are not well

interconnected (Revil amp Cathles 1999) Similarly this relation can be modified to estimate

permeability by using void ratios where porosity lsquoemptyrsquo is replaced by lsquovrsquo (Eq 116) Void ratio lsquovrsquo is

the ratio of the volume of void spaces to the volume of solids (Mesri amp Olson 1971 Tavenas et

al 1983 Al-Tabbaa amp Wood 1987 Vasseur et al 1995 Luijendijk amp Gleeson 2015)

Q = QRYDV (116)

In Figure 152 porosity is plotted against permeability obtained from the experimental data

The numerically modelled permeability predicted by equations 115 and 116 fit nicely with the

experimental data (Luijendijk amp Gleeson 2015) The calibrated ampR and X values used in Figure

152 are listed in Table 12

27

Table 12 Calibrated parameter values for the permeability equations of clays (Luijendijk amp

Gleeson 2015)

Equation Equation

Number

Parameters Units Calibrated Parameter Values

Kaolinite Illite Smectite

Power

Law

Porosity

16 ampR m2 765e-17 153e-19 844e-23

X Dimensionless 682 965 1702

Power

Law void

ratio

17 ampR m2 616e-17 154e-19 118e-21



28

155 Permeability of Sand and Clays Mixture

The porosity and permeability relationship in sand and clay mixtures cannot be accurately

derived by the previously described models (Figure 152) The porosities of pure sand and clay

are always higher than their mixtures (Revil amp Cathles 1999) The deviation in permeability in

response to the porosity change in sand and clay mixtures can vary extensively as shown in Figure

152 A minor increase in the clay content can clog pore spaces bringing a large reduction in the

permeability of the rock To predict the permeability in sand and clay mixtures Revil amp Cathles

(1999) build a model that considers the homogenous dispersion of clay between sand grains

known as an ideal packing model (Eq 117 118 and 119)

Q = QZ[lowast81$]^QG_Z`]^ 0 le w leoslashsd (117)

Q =QGHlowastaM w gt oslashsd (118)

QG_Z = QGHlowastbZ[M (119)

Where w is the fraction of clay κcl and κsd are the permeabilities of the sand and clay

fractions from the sediment Here κsd can be calculated by using the Kozeny-Carman relation

while κcl can be calculated by using the empirical power law equation (Eq 115) κcfs is the

permeability of sand with pore spaces completely filled by clay and oslashsd is the theoretical porosity of a pure sand fraction without any clay sediments in the pore spaces

29

Figure 153 Permeability of mixed sand and clay through the ideal packing model (After Revil amp

Cathles 1999)

The permeability calculated by the ideal packing model is plotted in Figure 153 Three

different shale volumes (φv) are plotted from clean sand (φv =0) to pure shale (φv =1) where

permeability is a function of porosity and shale content Figure 153 shows a rapid decrease in

permeability and porosity with increasing clay content Figure 154 shows the permeability of

sand and clay fraction plotted with the experimental permeability reported in Luijendijk amp Gleeson

(2015) The permeability dataset consisted of two natural sand-clay mixtures reported by Heederik

(1988) and Dewhurst et al (1999a) and one experimental dataset of a kaolinite and quartz mixture

with a uniform grain size (Knoll 1996) is depicted The observed and modelled permeability of

the individual sand and clay fraction shows a difference of approximately six orders of magnitude

difference Each dataset of clay and sand natural permeability is close to their respective modelled

permeability of sand and clay fraction The observed value of sand and clay mixture (kaolinite amp

quartz) is closer to the modelled permeability of the clay fraction This indicates that the clay

fraction is a dominating factor in determining the permeability of sand and clay mixtures

(Dewhurst et al 1999b Luijendijk amp Gleeson 2015

30

Figure 154 Natural and experimental datasets of permeability with calculated values (After

Luijendijk amp Gleeson 2015)

Another way of estimating the permeability of sand and clay mixtures is by taking the

arithmetic geometric and harmonic mean of the clay and sand components Eq 120 (Luijendijk

amp Gleeson 2015)

Log (k) = w log (kcl) + (1-w) log (ksd) (120)

Where w is the clay fraction and κcl and κsd are the permeability of the sand and clay

fraction of the sediment in m2 Considering a sandstone rock containing clay in the matrix that

spreads in a laminar fashion the effective permeability parallel to the layers can be calculated by

taking the arithmetic mean while the flow perpendicular to the layers can be estimated by the

harmonic mean of the clay and sand components (Luijendijk amp Gleeson 2015) The three-

different means define varying relationship of clay content with permeability

In case of a clean quartz dominated sandstone with minor amount of clays the

permeability of a sandstone is directly proportional to its porosity as described previously in

31

Section 153 The porosity-permeability relationship gets complex in a sandstone with significant

amount of clays in it There is no absolute correlation of increasing porosity with permeability in

a clay-sand mix medium as observed in several studies (Heederik 1988 Knoll 1996 Dewhurst

et al 1999a Dewhurst et al 1999b Revil amp Cathles 1999 Luijendijk amp Gleeson 2015) In order

to model the enhanced permeability of a reservoir by using geochemical stimulation technique the

Kozeny-Carman porosity-permeability relationship (Eq 113) alone may not be sufficient It is

likely that the Catherine Sandstone reservoir consists of a complex minerology with varying

petrophysical properties (See Chapter 2) Therefore it is essential to derive a site-specific porosity-

permeability relationship such as Verma and Pruess (1988) (Eq 114) for a realistic prediction of

permeability changes in a reservoir due to modification in porosity

16 Deriving the Verma and Pruess Porosity-Permeability Relationship

In order to apply the Verma and Pruess porosity-permeability relationship in the reactive

transport models there are two unknown variables emptyc (critical porosity) and W(power law

exponent) that must be defined for the TOUGHREACT simulation (Eq 114) These two variables

are affected by the pore geometry of different rock type that varies from one reservoir to another

Xu et al (2004) indirectly derived the two site-specific parameters as a function of injectivity

index which is defined in Eq 121

Injectivity Index = c

de1dS (121)

In the above equation Q is the flowrate in units of kgs Q is also referred to as injection rate in

the case of field scale modelling f minus R is the differential pressure (∆P) in bars which is defined

as borehole and formation pressure respectively In a laboratory scale core flooding experiment

setup (See Section 31 Chapter 3) flowinjection rate lsquoQrsquo is defined in units of ccmin It is the

rate at which the fluid is injected in the core The differential pressure f minus R in a laboratory scale

core flood experiment can be defined as the pressure difference between the fluid inlet and outlet

point of the core denoted by ∆P (See Section 31 Chapter 3) Large pressure differentials are the

consequence of lower permeability of the rock which in turn lowers the injectivity index In Xu

et al (2004) 12 years of injectivity index data from a geothermal site is plotted against time which

follows a gradual decreasing trend over the period of site operation The decrease in permeability

32

was due to clogging of pore space by silica precipitation over time TOUGREACT modelling was

used to simulate the same scenario by applying the Verma amp Pruess porosity-permeability relation

(Eq 114) Multiple numbers were used for critical porosity and the power law exponent that

resulted in different injectivity index trends which were plotted against the injectivity index

derived from the field data (Figure 161) The modelled trend giving the best fit against field data

is used to define emptyc and W for the reservoir at the specified geothermal site (Xu et al 2004b) A

similar approach to Xu et al (2004) will be followed on a laboratory scale by using a core flood

system (See Section 52 Chapter 5) to derive the two unknowns of the Verma and Pruess porosity-

permeability equation for Catherine Sandstone core used in the experiments (See Section 24

Chapter 2)

Figure 161 Injectivity index plotted against time solid lines represents modelled data while

diamond shaped markers are field data (Xu et al 2004b)

33

17 Research Questions

As discussed in detail in the introductory sections 11 and 12 the current research project

aimed to develop a new methodology to characterize the site-specific effective surface area of

minerals in the Catherine Sandstone The effective surface area values will be incorporated in the

near well formation reactive transport models to study the feasibility of geochemical reservoir

stimulation to enhance the Catherine Sandstone permeability and CO2 injectivity This PhD project

will address the following research objectives utilising available samples experimental and

modelling resources

bull Run core flooding experiments to determine the site-specific effective surface area of

minerals in the samples of Catherine Sandstone cores

bull Build a reactive transport model to simulate mineral dissolution and associated

permeability changes near the wellbore

bull Optimize model conditions to maximise permeability enhancement by studying the

differences in reagent injection rate and period

bull Determine the feasibility of geochemical reservoir stimulation at the field scale

In order to attain the above objectives Catherine Sandstone core samples were collected from

Central Queensland Australia (Section 24 Chapter 2) which were used in the core flooding

experiments (Chapter 3) The acquired experimental data (Chapter 4) helped in developing the

methodology to determine the effective surface area of minerals in the Catherine Sandstone core

samples (Section 51 Chapter 5) The feasibility study of geochemical reservoir stimulation using

reactive transport modelling is done in Section 64 Chapter 6

34

CHAPTER 2

2 Geology of the Northern Denison Trough and Core

Characterization

The targeted unit for CO2 storage in the ZeroGen CCS project was Catherine Sandstone

(Section 134 Chapter 1) The Catherine Sandstone reservoir is a part of Permo-Triassic basin

known as Northern Denison Trough located in the Central Queensland Australia The geological

history of the Northern Denison Trough is described in the subsequent sections

21 Basin Evolution and Structure of the Denison Trough

The Denison Trough is an elongated Permo-Triassic basin with an approximate maximum

length of 300 km and a width of 50 km it is oriented north to south along the western margin of

the Bowen Basin adjacent to the Springsure Shelf (Figure 21) The Denison Trough is bound by

the Springsure Shelf and the Comet Ridge in the west and east respectively The Springsure Shelf

and the Comet Ridge form structural highs with a series of normal faults trending north-south The

normal faults were active throughout the beginning of Bowen Basin formation resulting in half

grabens (Figure 21) Moreover there are series of anticlines and synclines within the Denison

Trough that are arranged in a set of en echelon folds with increasing amplitude from east to west

(Paten and McDonagh 1976 Jackson et al 1980 Baker and Caritat 1992)

The structural changes within the Permo-Triassic sequences of the Denison Trough are due

to compression from the east resulting in three main anticlines trending towards the north The

anticlines are known as Springsure Sercold and Consuelo anticlines which formed during the

Upper Permian and Late Triassic period The basin evolution history of the Denison Trough can

be divided into three separate phases as described in some references (Ziolkowski amp Taylor 1985

Murray 1990 Fielding et al 1990a Anthony 2004) The first phase consisted of back arc

extension on pre-existing basement structure causing north-south oriented graben and half grabens

in the Early Permian time generating space for the deposition of sediment The second phase is the

passive thermal subsidence followed by extensive sediment cover in the Denison Trough during

late Early Permian to early Late Permian (Anthony 2004) The third phase involved the formation

of anticlines due to east-west compression and reactivation of normal faults in the Late Permian to

35

Middle Triassic time Today the Denison Trough accommodates approximately more than 3500

meters thick Early to Late Permian sediments made up of interbedded marine and non-marine

sediments largely clastic (Mollan et al 1969) The basement consists of Lower Permian volcanic

rocks overlain by thick sediments predominantly of Permian age (Figure 22) The basal

sedimentary unit of the Denison Trough contains thick sequences of sandstone mudrocks

conglomerates and coal of Reids Dome Beds (Baker and de Caritat 1992) The Reids Dome Beds

are overlain by Cattle Creek Formation deposited in the deep marine environment consisting of

alternating beds of mudstone and sandstone (McClung 1981) (Figure 23) The overlying fluvio-

deltaic sediments of Aldebaran and Freitag Sandstone were considered as potential storage

reservoirs of lower priority by the ZeroGen project and are capped by the marine mudrocks of

Ingelara Formation followed by a deposition of the primary reservoir unit of Catherine Sandstone

The Catherine Sandstone is a well sorted fine to medium grain clastic wedge that extends

throughout the Denison Trough (Figure 23) The sediments were deposited in shallow marine to

paralic environment with a maximum thickness of up to 150 meters in well logs acquired by the

ZeroGen project (Baker 2009) (Figure 24) Being the targeted reservoir for CO2 storage the

Catherine Sandstone is overlain by a number of sealing layers from fine grained sediments of the

Peawaddy and Mantuan Formation to offshore deep marine Black Alley Shale (Figures 23 and

24) with a cumulative thickness of more than 250 meters (Garnett et al 2013)

36

Figure 21 Structural elements of Denison Trough marking approximate locations of ZeroGen

exploration wells and core sampling sites (After Baker and de Caritat 1992)

Figure 22 Seismic section showing a cross section along ZeroGen 5 well in the Denison Trough

(After Garnett et al 2013)

37

22 Permian Lithostratigraphy and Facies of the Denison Trough Sediments

In Springsure the Lower Permian sedimentation occurred in two diverse structural provinces

namely Denison Trough and Springsure Shelf (Mollan 1972) Denison Trough is situated in the

eastern part of Springsure marked by typical transgressive and regressive marine cycles with

minute disruption in sedimentation (Figure 21) On the other hand the Springsure Shelf in the

west consists of extensive non-depositional phases due to slow fluviatile deposition (Mollan 1972)

The Denison Trough is a structural element of the Gebbie Subgroup deposited mostly in the deltaic

to marine environments The sedimentation started in the Early Perm with the deposition of the

Reids Dome Beds

221 Reids Dome Beds

The Lower Permian in the Denison Trough starts with more than 2000 m thick sediments

of Reids Dome Beds (Mollan 1972 Dickins amp Malone 1973) They comprise of mostly alluvial

and lacustrine deposits with interbedded basaltic and felsic igneous rocks non-marine arenite

lutile and coal seams The exposure of Reids Dome Beds is the Orion Formation located on the

eastern margin of the Springsure Anticline (Mollan 1972) Similarly a few tens of metres of Reids

Dome Beds are exposed on the western margin of the Springsure Shelf Structurally it forms

grabens and half-grabens that are filled with continental sandstone mudrocks conglomerates and

coals (Baker amp Caritat 1992 Baker et al 1993) Most of the sediments comprise of interbedded

sandstone and siltstone with thick beds of shale The depositional environment then changed from

transitional to marine thus resulting in the deposition of Stanleigh and Cattle Creek Formations in

the northern and southern regions of the Denison Trough respectively (Mollan 1972 Dickins amp

Malone 1973) The upper Reids Dome Beds Cattle Creek Group and Aldebaran Sandstone were

formed during the second phase of deposition in the Bowen Basin (Anthony 2004)

38

Figure 23 Stratigraphy of the Denison Trough (After Baker 2009)

222 Cattle Creek Formation

The Cattle Creek Formation consists of mainly conglomeratic silty sandstone in the type

section reported near the western flank of Reids Dome The thickness is reported between 100 to

450 meters in the Reids Dome The section also contains interbedded limestone calcareous

sandstone and dominantly deep marine mudrocks (Mollan 1972 Baker amp Caritat 1992 Baker et

al 1993) The silty sandstone is dark grey in colour and filled with mica and carbonaceous

materials There is thick bed of lithic quartz sandstone consisting predominantly of quartz grain

with an argillaceous matrix The base of the formation is in transition with Reids Dome Beds and

it is not exposed (Mollan 1972) The Cattle Creek Formation has a similar lithology as the

Stanleigh and Sirius sequences located in the Springsure Sheet area and is considered their

equivalent in the Reids Dome The sediments are generally poorly sorted and were deposited under

marine conditions

39

223 Aldebaran Sandstone

The Aldebaran Sandstone is a massive siliceous sandstone unit that is exposed in the

Springsure Serocold and Consuelo Anticlines The Aldebaran Sandstone is deposited as thick

delta and fan delta sediments followed by barriers bars and tidal channels running from the

eastern to western margin of Denison Trough (Baker et al 1993) It forms noticeable

geomorphology such as cuesta and ridges and is well exposed throughout the area It is often

identified in air-photographs as dark coloured patches due to a dense tree growth During the

depositional period a shallow marine environment prevailed in the Denison Trough resulting in

the deposition of shelf to coastal plain sediments of the Aldebaran Sandstone As a consequence

of sea level variations several sequences have been reported in the Aldebaran Sandstone due to

which it has been divided into three distinctive members on the basis of depositional environment

(Mollan 1972 Dickins amp Malone 1973) The basal member consists of deltaic sandstone

deposited in the transition from marine to brackish environments The sediment supply was

reduced occasionally resulting in inter-bedded siltstone and mudstone together with localize coal

seams The sediments consist of medium grained feldspathic sandstone with interbedded

carbonaceous siltstone and mudstone (Dickins amp Malone 1973) The bedding has been identified

as being contorted in some parts of the member It also contains intervals of lutite that are found

in the north of Springsure Anticline (Mollan et al 1969) Later the deltaic sediments prevail over

the marine thus depositing the middle member of Aldebaran Sandstone The middle member is

marked by the transition in the sediment type from sand to conglomerates The unit contains cross-

bedded conglomeratic sand with individual conglomerate beds deposited due to rapid supply of

sediments caused by uplift and erosion the adjacent provenance The unit was deposited at the

same time as the Colinlea Sandstone and mostly consists of arenite and coarser detritus (Dickins

amp Molane 1973) The conglomerates are made up of sandstone siltstone and milky quartz with

chert and volcanic rocks The maximum thickness of the lower member is more than 300 m

(Mollan et al 1969) while the thickness of the conglomeratic member ranges from 80 to 150 m in

Reids Dome and 150 to 240 m in the Springsure Anticline (Mollan et al 1969)

40

Figure 24 Wireline log correlation between ZeroGen wells showing depth and thickness of

Catherine Sandstone (After Baker 2009)

224 Upper member of Aldebaran Sandstone amp Freitag Formation

The environment later transitions from deltaic to brackish depositing the upper member of

Aldebaran Sandstone and Freitag Formation During the deposition period the shallow marine

environment ceases in the Denison Trough In older literature the Freitag Formation is considered

as a part of top most Aldebaran member (Dickins amp Molane 1973 Mollan et al 1969) Therefore

it is hard to distinguish between the two The Sandstone of Freitag Formation and upper Aldebaran

41

member are exposed at the Reids Dome and Springsure Anticline The upper member of Aldebaran

comprises of thin layered interbedded quartzose sandstone and siltstone that are micaceous with

hardly any conglomerates Sedimentary structures include worm tubes and oscillation ripples

throughout the upper most part of the Aldebaran Sandstone (Mollan et al 1969 Dickins amp

Molane 1973) The overlaying Freitag Formation predominantly consists of sandstone and it

marks the transition from shallow to deep marine environments (McClung 1981) The thickness

of the quartzose sandstone interval ranges from 90 to 300 metres (Mollan et al 1069)

225 Ingelara Formation

Later in Permian the increased subsidence of the basin resulted in greater depth of water

depositing poorly sorted sand and siltstone of Ingelara Formation The change in the water depth

is represented by the presence of shelly fossils within the sediments The Ingelara Formation is the

interval between Catherine Sandstone and Freitag Formation It is exposed at the Springsure

Consuelo and Sercold anticlines It consists of poorly sorted siltstone and mudstone (Mollan et

al 1969 Dickins amp Molane 1973) It lacks a sharp boundary with the underlying Freitag The

top of the quartzose sandstone marks the boundary between the two formations (Hill amp Denmead

1960 Mollan et al 1969) Ingelara Formation is a result of dominantly marine sedimentation that

is rich in shelly fauna of Lower Permian age There is an unusual presence of massive igneous and

metamorphic rocks within Ingelara Formation these fragments are possibly transported by

icebergs (Mollan et al 1969) The formation is more than 30 metres thick in the type area while a

maximum thickness of more than 150 metres has been reported for Mount Catherine (Mollan et

al 1969)

226 Catherine Sandstone

The Catherine Sandstone is a quartz-dominated sandstone unit that forms narrow ridges on

the flanks of Springsure Serocold and Consuelo anticlines in the northern Denison Trough

(Mollan et al 1969) It is mostly buried under Tertiary Basalts in the Springsure area The

sediments mostly contain quartz with 5 to 10 of potassium feldspar (Bastian 1965a Mollan

et al 1969) Other minor minerals are muscovite sericite and chert with traces of glauconite

tourmaline and zircon (Bastian 1964) Similar mineral composition has been stated in ZeroGen

reports (Baker 2009 amp Garnett et al 2013) from the XRD data of Catherine sandstone samples

42

from a depth of 850 to 950 metres These studies have reported more than 80 Quartz and 5 to

15 K-feldspar on average from 6 samples of varying depth intervals The sandstone is medium

to fine grain and well sorted with a thickness of approximately 80 metres in the type area The

general thickness of the unit ranges from 6 to more than 100 metres Several fossiliferous horizons

have been reported in the Catherine Sandstone by Mollan et al (1969) The sediments were

deposited in shallow marine and paralic environments marking the final stages of deposition in the

Denison Trough It has a sharp boundary with overlying Peawaddy Formation while the contact

with underlying Ingelara Formation is transitional in some areas (Mollan et al 1969)

227 Peawaddy Formation

The Peawaddy Formation is a thick sand and siltstone unit containing siltstone

carbonaceous shale and lithic quartz sandstone The sediments started accumulating in the deltaic

conditions that later shifted to marine (Baker et al 1993) It is underlain by the Colinlea Sandstone

in the western and the Catherine Sandstone in the eastern part of the Springsure area It contains

a highly fossiliferous unit known as Mantuan Productus Bed that also consist of brachiopods

pelecypods gastropods corals and bryozoans These fossil rich coquinitic lenses are all part of

Mantuan Productus Beds The Formation is mostly weathered and poorly exposed in the area The

beds are only visible in the creeks and gullies The outcrops mostly consist of calcareous and lithic

sandstone containing feldspar and trace amounts of mica and glauconite The lithic sandstone

comprises of volcanic rock fragments with chloritic and calcareous cement The interbedded

carbonaceous shale and siltstone within the formation contains plant debris The Peawaddy

Formation is bound by unconformities with the above and below lying formations The formation

is approximately 150 metres thick in the Springsure area The top sediments were deposited in a

marine environment resulting in rich fossiliferous units while the sandstone is characterised by a

high amount of feldspar (Mollan et al 1969)

228 Black Alley Shale

The deposition of Catherine and Peawaddy Formations occurred during frequent sea level

fluctuation Consequently sediments were deposited under alluvial to fluvio-deltaic to shallow

marine conditions The shallow marine environment turned sediments into well sorted medium

grained quartz sandstone Later increase sea levels caused by foreland thrust loading along the

43

eastern margin of the Bowen Basin resulted in basin wide transgression depositing Black Alley

Shale in the deep marine environment (Fielding et al 2000 Anthony 2004) The Black Alley

Shale marks the final stage of the Perm in the Denison Trough The Formation is exposed in the

Serocold and Consuelo anticlines as well as in the Wealwandangie Syncline (Mollan et al 1969)

Due to soft shaly sediments the formation is poorly exposed in the area and is overlain by dark

coloured soil Black Alley Shale comprises of dark shale containing montmorillonite that shows

bentonitic characteristics (Thompson amp Duff 1965 Mollan et al 1969) A noticeable feature of

Black Alley Shale are the small mounds in the soil cover caused by the swelling of bentonitic clay

It has a distinct boundary with underlying sandstone of Peawaddy Formation due to difference in

colour and sediment grain size The sediments were deposited in the transitional environment that

consists of Deltaic Tidal Lagoonal and lake deposits while the lower basal part represents former

marine deposition The maximum thickness of Black Alley Shale is measured to be more than 140

metres on the western part of Early Storms Dome (Mollan et al 1969) The marine environment

is marked by planar bedding with well sorted sediments the presence of marine fossils and

abundant heavy minerals (Dickins amp Malone 1973) The sediments deposited after Black Alley

Shale were entirely non-marine alluvial sandstone and siltstone of Bandanna Formation followed

by the alluvial Rewan Group in the Early Triassic

23 Reservoir Characterisation of the Aldebaran Freitag and Catherine

Sandstones

The reservoir properties of the Denison Trough vary as the sequences were deposited in a

range of paleoenvironments Starting from the younger sequences of Mantuan and Freitag

Formations they are fluvio-deltaic dominant distributary channel and tidal deposits (Garside

1990 Anthony 2004) The Catherine Sandstone was mostly deposited in the shallow marine

conditions followed by near shore marginal marine and strandplain deposits of Lower Aldebaran

and Cattle Creek Group The following section is a characterisation of the three reservoirs of the

Denison Trough considered for CO2 storage (Aldebaran Freitag and Catherine sandstones) as

described in Garnett et al (2013) They were selected on the basis of their comparatively better

reservoir quality in terms of porosity and permeability

44


The Aldebaran Sandstone is known to be a productive hydrocarbon reservoir in the

Denison Trough (Baker 1989 Anthony 2004 Marsh amp Scott 2005) It shows complex

depositional and diagenetic history due to lateral and vertical reservoir heterogeneity (Martin 1982

Wilkinson 1983 Baker 1989 Anthony 2004) The porosity and permeability vary depending upon

the facies and diagenetic alterations within each unit It contains a maximum porosity of above

20 in tidal delta channel facies with a permeability of over 2000 millidarcies (mD) However

that is not generally the case The reservoir properties of Aldebaran in the gas pay zones show

porosity in the range of 9-15 together with low permeability of 01 ndash 25 mD (Baker 1989 Shield

2001 Anthony 2001 2004) There is little core data available on the Early Permian reservoir units

but the wireline logs and other available data indicate porosity does not exceed 15 with

permeability less than 10 mD (Martin 1986 Baker et al 1993 Anthony 2004) There is a range

of post depositional diagenetic factors that control the reservoir quality of the Aldebaran

Sandstone It was mostly affected by intense silicification during the early to middle Triassic when

the maximum burial depth of 5000 to 6000 m was reached The lowest porosity is reported to be

32 (Table 21) with permeability values dropping to 016 mD (Garnett et al 2013)

Table 21 Petrophysical properties and mineralogical composition of Aldebaran Sandstone

reported in Baker (2008)

Depth 105060 106230 106680 127500

Porosity () 32 65 86 61

Permeability(mD) lt1 20-25 25-35 lt2

Quart + Chert () 863 913 906 793

K-feldspar () 64 51 63 78

Plagioclase () 28 07 03 46

Mica () 03 - - -

Authigenic Kaolin () 28 20 11 -

Rock Fragments 14 09 17 83

45

232 Freitag Formation

The Freitag Formation consists of a 100 to 150 m thick medium to coarse grain sandstone

wedge that represents a progradational facies The sandstone is predominantly deposited in a

fluvio-deltaic distributary and tidal channel environment (Garside 1990 Anthony 2004) The

sample analysis of the Freitag Formation conducted by Baker (2008) indicated clean

conglomeratic sandstone with minute intergranular porosity preservation The primary porosity is

mostly destroyed by the quartz overgrowth cementation between the grains There is also some

pseudo matrix reported in the sediments due to localised authigenic clay precipitation resulting in

porosity reduction (Table 22) As a consequence despite coarse grain sediments the pores have

very limited interconnectivity effecting the reservoir permeability

Table 22 Petrophysical properties and mineralogical composition of Freitag Formation reported

in Baker 2008

Depth (m) 58888 94645

Porosity () 125 94

Permeability(mD) - 4-10

Quart + Chert () 757 907

K-feldspar () 155 56

Plagioclase () 11 03

Mica () 03 03

Authigenic Kaolin () - 14

Rock Fragments 74 17


The Catherine Sandstone is an elongated north to south trending clastic wedge that is

interpreted to be a marginal marine deposit (John and Fielding 1993 Marsh amp Scott 2005) It is

a hydrocarbon bearing reservoir with reasonable connectivity at field scale Similar to the

Aldebaran Sandstone the reservoir quality of the Catherine Sandstone is controlled by the facies

changes and depositional environment The highest porosity and permeability values are reported

46

in the high energy shoreface facies with a porosity of up to 24 with high permeability of 850 mD

(Marsh amp Scott 2004) The shoreface facie extends tens of kilometres in the basin with tabular

external geometry The clean sandstones were subjected to intense silicification that severely

impacted the petrophysical properties of the original deposits (Garnett et al 2013 Marsh amp Scott

2004) The other facies such as distributary channels consisted of poorly sorted immature sand

were better able to preserve the porosity and permeability of Catherine Sandstone Moderate to

high permeability has been reported in exploration wells (Table 23) These sediments are coarser

in grain size and relatively richer in feldspar (up to 15 Garnett et al 2013) Furthermore

samples from these exploration wells showed the presence of authigenic kaolin and illite resulting

from alteration of micaceousargillaceous grains and feldspar Porosity and permeability reduction

in the Catherine Sandstone (Table 23) by diagenetic mechanisms are due to quartz overgrowth

cementation and authigenic clay formation and compaction forming a pseudomatrix (Baker 2008

Garnett et al 2013)

Table 23 Petrophysical properties and mineralogical composition of Catherine Sandstone

reported in Garnett et al 2013

Depth 85454 91535 92022 94321 94376 94510

Porosity () 177 123 134 131 126 117

Permeability(mD) 330 520 322 321 121 080

Quart + Chert

()

881 757 751 849 817 806

K-feldspar () 50 149 130 78 107 88

Plagioclase () 07 39 45 21 27 33

Mica () - 03 - - - 03

Authigenic

Kaolin ()

27 11 07 50 51 28

Rock Fragments 35 41 67 02 - 42

47

24 Sampling of the Catherine Sandstone

Rock samples from the Catherine Sandstone were collected by me together with my

supervisors from outcrops south of Springsure (Figures 21 and 25) in early May 2015 which

were used in the analytical and experimental studies Geographically the northern Denison Trough

is situated in central Queensland of Australia The subsurface depth of the Catherine Formation

increases moving towards the north of the Denison Trough near a large mining town known as

Emerald This is where the actual ZeroGen CO2 storage site was located with exploration wells in

the south of Emerald (Figure 21) In general the Catherine Sandstone was poorly exposed in the

northern region as most of it is concealed by the Tertiary Basalt also forming a large ridge known

as Mount Catherine (Figure 26) Most of the Catherine Sandstone outcrops were found in the

south of a small town known as Springsure The Formation was exposed in the form of dissected

ridges on the flanks of the Springsure Consuelo and Sercold anticlines (Mollan et al 1969) It

cropped out in the Springsure and Emerald sheet area in the west and north of the Springsure

Anticline Catherine Sandstone overlied Ingelara Formation near the Mount Catherine area with a

gradational contact boundary

Figure 25 Satellite image of the sampling locations in the south of Springsure

48

241 Sampling Sites

The sampling sites were located on private properties known as Freitag (F) Inglis (I) and

Nyanda (NY) Stations (Figure 25) The Freitag Station was situated next to Springsure Anticline

at the western edge The outcrop was at the base of Mount Catherine in the dry creek next to the

road here referred to as station F1 (Figures 25 and 26) The sandstone at that location was

yellowish to grey in colour with bends of orange layers which indicate the presence of iron oxides

as a result of chemical weathering (Figure 27) The rocks were well consolidated medium to fine

grained sandstone A total of seven core samples were drilled from three different spots (F1-1 2

amp 3) at Freitag Station Walking down the same creek further south of the sampling location F1

two more core samples were drilled (F4-1 amp F4-2) These samples were greyish in colour showing

signs of extreme surface weathering (Figure 28) Another exposure of the Catherine Sandstone

was found a few metres away from the road and further south of Mount Catherine A total of eight

cores were drilled into the ridge at three different spots (F2-1 2 amp 3) The samples were light

yellow to white in tone medium to fine grained and well consolidated sandstone (Figure 29)

Figure 26 Geological map showing sampling locations at Freitag Station (Modified after

Mollan et al 1969)

49


Figure 28 Sampling site F4-1 amp F4-2

50

Figure 29 Catherine Sandstone exposure at site F2 several meters off the road in the south of

Mount Catherine

The entire area at site F2 was densely covered by dry shrubs Walking along the section of

Catherine Sandstone at site F2 there were a couple more blocks of sandstone exposed at sampling

site location site F3 (Figure 210) They were subjected to some degree of surface weathering and

showed different coloration compared to the homogenous light-coloured medium to fine grain

semi-consolidated sandstone beneath the surface The other potential site where the Catherine

Sandstone was exposed was situated next to Mount Inglis approximately 20 km south of Mount

Catherine (Figure 25) Structurally the area consisted of a dome (Serocold Dome) where the

outcrop was poorly visible due to dense vegetation Limited exposures of weathered sandstone

beds (Figure 211) were present inside the creek (Peawaddy Creek) west of a swamp further south

of the Mount Ogg road (Figure 212) The beds consisted of fine grained clay rich unconsolidated

sediments Continuing on the road along the Peawaddy Creek another small exposure of rock was

present next to the Mount Ogg road This small section was exposed due to manmade excavation

51

which consisted of light coloured clay rich very fine-grained sand comprised of clay rich

sediments (Figure 213) Two core samples were drilled on the site I2


The last sampling site was located approximately 70 km south of Springsure next to Rewan

Road The area was called Nyanda Station represented as NY 1 amp 2 in Figure 25 The Catherine

Sandstone section as reported in Mollan et al (1969) was exposed through the small ridge with

up to 40-50 metres in relief (Figure 214) Geologically the outcrop was situated on the eastern

flank of Reids Dome covered by trees and shrubs (Figures 214 and 215) Core samples were

drilled into massive deformed blocks of sandstone The samples were medium to coarse grained

friable and semi unconsolidated grey coloured sandstone

52

Figure 211 Sandstone beds exposed in the creek near Inglis Station (I1)

Figure 212 Geological map showing sampling location at Mt Inglis Station (Modified after Mollan et

al 1969)

53

Figure 213 Clay rich sand exposed next to the Mount Ogg road at sampling site I2

Figure 214 Geological map showing sampling location at Nyanda Station (Modified after Mollan et al

1969)

54

25 Core Sample Characterisation

251 X-ray Diffraction

Catherine Sandstone samples collected during field work were characterized for their

petro-physical properties and minerology X-ray diffraction (XRD) analysis on the powdered

samples of the Catherine Sandstone cores were conducted to quantify the major minerals contained

in the core A batch of nine samples from different cores tabulated in Table 24 were analysed at

the Materials Characterisation and Fabrication Platform (MCFP) at the University of Melbourne

and the Victorian Node of the Australian National Fabrication Facility (ANFF) The samples were

back-loaded into a standard sample holder (without any additional sample preparation) for analysis

by X-ray diffraction (XRD) Each sample was then spiked with a known quantity of Alumina and

re-analysed by XRD Diffraction data were collected using a Bruker D8 Advance X-ray

diffractometer with Ni-filtered Cu kα radiation (154 Aring) Data were collected between 5 ndash 85deg 2θ

with a step size of 002deg and a scan rate of 05 seconds per step An anti-scatter blade was used to

reduce the diffracted background intensity at low angles An incident beam divergence of 026deg

was used with a 25deg soller slit in the diffracted beam The sample was spun at 15 revolutions per

minute Phase identification was completed using Materials Data Inc Jade 93 software with the

ICDD PDF2 database and Quantitative Rietveld analysis for the quantification of identified

crystalline phases that were carried out using Bruker Diffracplus Topas software

Table 25 shows XRD analysis of two core samples carried out later to cross examine the

quantification of minerals in the previous dataset (Table 24) The two cores selected (F1-3 F2-2)

for re-analysis which were later used in the core flood dissolution experiments (see Chapter 3 and

4) The XRD analysis was performed at the Research School of Earth Sciences (Australian

National University) with a SIEMENS D5005 Bragg-Brentano diffractometer equipped with a

graphite monochromator and scintillation detector using CoKα radiation Samples were milled in

ethanol in a McCrone Micronizing Mill for 5 minutes dried at 40degC and loaded in side-packed

sample holders The scan range was 4 to 84deg 2θ at a step width of 002deg and a scan speed of 2

seconds per step The results were interpreted using the SIEMENS software Diffracplus Eva

(2003) for mineral identification and quantification programs Rietica (sampleYSA-01 only) or

Siroquant V3 were used

55

Table 24 XRD data of core plug used in the CFS experiments acquired from MCFP University

of Melbourne and ANFF

Sample Quartz

Wt

plusmn1

Kaolinite

Wt

plusmn1

Orthoclase

Wt plusmn1

Albite

Low

Wt

plusmn1

Muscovite

Wt plusmn1

Ammonio-

-Jarosite

Wt plusmn1

F1-1 81 7 1 2 9

F1-4 81 7 1 2 9

F4-2 81 7 1 2 9

F2-1 81 7 1 2 9

F2-3 81 7 1 2 9

I 1 63 9 5 4 18 2

I 2-1 62 6 3 4 24

NY-3 78 5 4 2 11

NY-4 72 10 5 1 12

Table 25 XRD data of core plug used in the CFS experiments acquired from the Research School

of Earth Sciences (Australian National University)

Sample F1-3c

F2-1

F2-2b

(Fines)

wt sd wt sd wt sd

amorphous material 76 16 151 26 171 27

Quartz 652 1 672 04 - -

Plagioclase - - Trace - - -

K-feldspar - - - - - -

Hematite trace - - - - -

Kaolinite 227 03 139 02 81 55

Mica 45 05 37 0 18 12

56

The XRD data in Table 24 shows equal quantity of major minerals in all the Catherine

samples collected from the Freitag location Comparing the two-different data sets Table 25

shows a smaller percentage of quartz mica and higher amount of kaolinite in both the cores Table

25 quantifies amorphous phase in the sample unlike Table 24 Since the amorphous phase in the

core mostly consisted of silica it could sum up to equal percentage of quartz as in Table 24

Overall the results differed from the Catherine Sandstone mineral composition described in the

literature in Table 23 Minerology of the samples tabulated in Table 23 shows significant

percentage of feldspars and trace amount of mica in the Catherine Sandstone Since core samples

in the current study were drilled from the surface outcrops they might be subjected to extreme

chemical weathering Large percentages of kaolinite and mica in the surface samples may have

been formed by the chemical weathering of feldspars in the Catherine Sandstone potentially via

the mechanisms shown in Equations 21 amp 22 Therefore feldspars were not detected in both

XRD analyses (Tables 24 amp 25)

2KAlSi3 O8 + 2H+ + H2O lt=gt Al2 Si2 O5 (OH)4 + 2K+ + 4SiO2(aq) (21)

K-Feldspar Kaolinite

3KAlSi3 O8 + 2H+ lt=gt KAl2 Si3 AlO10 (OH)2 + 2K+ + 6SiO2(aq) (22)

K-Feldspar Mica

252 Porosity Analysis

Porosity of Catherine Sandstone rock samples were determined by the fluid saturation

method The method consisted of two major steps that involved calculation of the bulk (Vb) and

pore (Vp) volumes of the rock samples To saturate the rock sample with formation water the

sample was placed in an empty vacuum desiccator under a vacuum for approximately 15 minutes

to get the trapped fluid or air out of the pore spaces of the rock sample The vacuum desiccator

was then connected to a water supply line to fill it with the fluid until the samples were completely

immersed under water The samples were kept saturated in the vacuum desiccator for

approximately 24 hours Bulk volume of the irregular shaped rock chunk was calculated using the

buoyancy technique The water saturated sample was then immersed under water to calculate the

mass (Msub) in grams The sample was then removed from the water bath and surface dried The

57

mass of the surface dried sample is denoted by Msat that is the mass in grams of the rock sample

saturated by water Bulk volume (Vb) is calculated by Eq 23 and 24

Vb = ghij1ghkl

m (23)

Where is the density of water in grams per cubic centimetre

In the case where the core sample shape was that of a perfect cylinder the samplersquos bulk volume

was calculated by using buoyancy technique (Eq 23) as well as Eq 24

Vb = π r2 h (24)

Where r is the radius of the core and h is the length in centimetres

The core was then dried in an oven at 70oC to get rid of all the water from the pore spaces and

placed on the weighing machine to get mass of the dried sample in grams (Mdry) The pore volume

(Vp) of the rockcore sample is calculated using Eq 25

Vp = n]3o1n^pq

m (25)

The porosity of the rockcore sample in percentage is calculated by using Eq 26

Oslash = rsre

x 100 (26)

253 Permeability Analysis

Permeability of the Catherine Sandstone cores were estimated by using the core flooding

system (CFS) (For details see Figure 311 Chapter 3) The cores were pre-saturated with de-

ionized water within a vacuum for approximately 24 hours the same way as in porosity analysis

(Section 262) Each core was then flooded in the core flooding system with de-ionized water

under ambient conditions There were pressure transducers at the inlet P1 and outlet P2 point of the

core holder that measured the differential pressure across the core (For details see Figure 311

Chapter 3) The permeability was calculated using Darcyrsquos Law (Eq 27) with the help of

differential pressure (∆P) along the core The permeability of each core is reported in Table 26

58

and were acquired independently by using a three-point method for accuracy (Figures 215 and

216) The injection rate was doubled for each measurement illustrated in Figures 215 and 216

and a corresponding doubling of the ∆P was observed thus a similar permeability was measured

at each injection rate (Figures 215 and 216)

=minus tu∆dw A (27)

Where Q is the flow rate in m3s K is the permeability in m2 represents viscosity in Pas ∆P

is the difference between inlet and outlet pressure in Pa L is the core length in m and lsquoarsquo is the

cross-sectional area to flow in m2

Figure 215 Differential Pressure (∆P) building up because of varying injection rate (Q) for

core F1-1

y = 13692x + 03846

Rsup2 = 0994

0

2

4

6

8

10

12

14

16

0 002 004 006 008 01 012

∆P

(p

si)

Q (ccmin)

Differntial Pressure vs Injection Rate

(F1-1)

59

Table 26 Porosity and permeability of Catherine Sandstone cores estimated by using fluid

saturation method and core flooding system

Sample

no

Length

(cm)

Porosity

()

Small

Chunk

Porosity

()

Core

Sample

Error Permeability

(mD)

Description

F1-1 99 2384 2325 +-01 0476 Good for exp

F1-3 214 - 2029 +-08 lt1 low permeability


F1-5 63 - 23 +-08 13 Small

F2-1 15 2517 +-06 15 Sample broken

F2-3 15 amp 5 2846 - +-2 - Friable not suitable for CFS exp

F2-2 144 - 242 +-06 495 Good for CFS exp

F4-2 6 2296 267 +-129 1490 v high permeability

F4-1 206 - 217 - 150-500 Fines released

NY-3 - 269 - +-076 - Not suitable for CFS exp

I2-1 - 3114 - +-052 - Not suitable for CFS exp

I-1 - 2907 - +-055 - Not suitable for CFS exp



60


core F4-2

254 Thin Section Analysis

Thin sections were made from five different Catherine Sandstone core samples drilled from

three different locations at Freitag Station (Figures 26 to 210) The samples were impregnated

with blue coloured dye under vacuum to make the pore space visible in optical microscope images

Subsequent cross and plane polarized snap shots were taken of each sample at 4 and 10 times

magnification Following are the general legends for Figures 217 to 225

Q=quartz Ka=Kaolinite M=mica Qa=quartz overgrowth Po=porosity Li=lithic fragments

In general the Freitag core samples consisted of medium to fine grain sub-rounded to

angular shaped quartz crystals with clay minerals cemented in between the matrix The course

grained sandstones were well-sorted with lesser amount of clay minerals visible through-out the

samples (Figures 217 and 218) The fine grain sandstone was poorly sorted and comprised of

higher percentage of clay minerals with thin layers of mica uniformly distributed throughout the

samples (Figures 219 and 220) Large pore spaces dyed in blue colour were visible in all the

samples which indicate high porosity

y = 00825x - 00375

Rsup2 = 09973

0

005

01

015

02

025

03

035

04

0 1 2 3 4 5 6

∆P

(psi

)

Q (ccmin)


(F4-2)

61

Figure 217 Cross and plane polarized light microscope pictures of core 2-2 with 10 times

magnification Framework minerals are quartz mica and lithic fragments The sample

predominantly comprised of monocrystalline quartz grains Grains are sub-rounded to angular

with an average size of 02mm A couple of multi coloured mica grains are spotted within relatively

large quartz crystals under a cross polarized light All the clean greyish coloured uniform size

grains represent quartz Kaolinite grains can be seen in darker shades in the plane polarized

light

62

Figure 218 Thin section of core F2-2 under cross and plane polarized light microscope with 4

times magnification The core predominantly comprised of medium grained and well sorted sand

A small multicoloured vein of mica is visible in the middle of the picture In the plane polarized

light kaolinite is represented by dark coloured grains cement in between grey coloured quartz

crystals Porosity is shown by light blue coloured patches that are in significant numbers

distributed evenly throughout the section Pores also seem to be interconnected proving core F2-

2 to be highly porous and permeable (Table 26)

63

Figure 219 Core F1-3 under plane and cross polarize light microscope with 4 times

magnification only The core comprised of significantly finer grained quartz matrix than F2-2 The

grain sorting is moderate to poor with sizes ranging from 003mm to 03mm Several large grains

are visible within the small grain quartz crystals A number of thin mica veins can be seen within

small size quartz crystal and siliceous cement The multiple mica veins are representing low energy

environment depositing fine grain sediments The kaolinite is clearly visible under plane polarized

light and is evenly distributed around the whole section Light blue coloured porosity patches are

64

large in numbers but are mostly isolated This shows core F1-3 to have similar porosity as core

F2-2 but extremely low permeability (Table 26)

Figure 220 Thin section snap shots of core F1-3 with 10 times magnification Framework

minerals consists of quartz mica and lithic fragments Quartz consists of monocrystalline sub-

rounded to angular grains A closer view of mica vein can be seen in the cross and plane polarized

light Mica is platy in nature thus showing high birefringence All the pore spaces are isolated and

do not show any connections Various dark brown spots of kaolinite are visible around tiny quartz

grains and siliceous cement

65

Figure 221 The core sample is F4-2 with 4 times magnification The core consists of medium

grained quartz crystals similar to the core 2-2 The quartz grains are moderately sorted with grain

size in the range of 01 to 05mm represented by grey colour in cross polarized light Numerous

mica veins are visible within the matrix that are platy in nature A large number of interconnected

pore spaces (light blue in colour) can be seen covering large proportion of the image This suggest

the core to be highly permeable (Table 26) The core also contains a significant amount of

kaolinite distributed around the mica veins and can be spotted by its brown colour in plane

polarized light

66

Figure 222 High permeable core F4-2 with 4 times magnification under plane and cross

polarized light The snap taken at a different portion of the thin section containing mostly uniform

sized monocrystalline quartz crystals The quartz grains are rounded to angular in shape with an

average grain size of 02mm A few large rounded and angular grains of quartz are also

noticeable within the matrix Some dark spots of kaolinite are visible in the plane polarized light

There are large size pores with few of them being interconnected

67

Figure 223 Core F1-1 with 4 times magnification The core is well to moderately sorted with

medium to fined grained monocrystalline quartz crystals The grain size ranges from 002 to

025mm The framework minerals consist of mainly quartz and lithic fragments with minute mica

The texture is different from all the previously described cores (F2-2 F4-2 and F1-3) Only a

couple of small mica veins are visible associated with quartz matrix showing birefringence A

large number of pore spaces can be seen in plane polarized light The core seems to have high

porosity with permeability less than high permeable cores F2-2 and F4-2 (Table 26)

68

Figure 224 Core F2-3 with 4 times magnification under plane and cross polarized light The core

is poorly sorted with a couple of large monocrystalline quartz grains within a fine matrix The

larger quartz grains are up to 1mm in size altogether with numerous small grains crystals having

an average size of 02mm Most quartz dominant sandstone with a few patches of kaolinite are

visible in the plane polarized light A large number of interconnected pore spaces are present that

suggests core F2-3 to be highly porous and permeable

69

Figure 225 Core F2-3 with 10 times magnification under plane and cross polarized light A small

platy mica vein of grain size less than 02mm showing high birefringence can be spotted under

high magnifications It is surrounded by quartz crystals with a few patches of kaolinite The quartz

consists of monocrystalline grains having varying grain sizes in the range of 01 to 04mm

Kaolinite is represented by dark brown patches within the matrix The blue pore spaces are

occupying a large area in the image representing a highly porous rock

70

255 Electron Microprobe Analysis

The electron microprobe (EMP) is a useful tool to quantify major elements and perform

chemical analysis of mineral phase within thin sections The main purpose of performing EMP

analysis in the current study is to identify the mica phase (muscovitebiotite) present in the thin

sections EMP also aids in the quantification of the exact molar ratio of ions in the grains of quartz

and kaolinite Thin sections were polished and coated with carbon for EMP analysis The targeted

phase for analysis is excited by a 1-2 microm finely focused electron beam Wavelength dispersive

spectrometer is used to detect the produced X-rays The points for analysis were mica quartz and

kaolinite grains (Figures 219 and 222) that were preselected using an optical microscope

Multiple points on each mineral were taken for analysis from various locations around the thin

section to give an average result Mean and standard deviations were calculated from the results

obtained from multiple point analysis of each mineral The final value was taken within 2 standard

deviations

71

CHAPTER 3

3 Experimental Design and Methods

31 Single Phase Core-flood Design and Operation

The Core Flood System (CFS) 350 by Vinci is designed to conduct flow-through tests on

rock core plugs at temperatures and pressures equivalent to reservoir conditions It consists of a

number of components fully integrated and operated through its software A Hastelloy B - coated

stainless-steel hydrostatic core holder is kept at constant temperature using a heating jacket A core

plug with 1rdquo or 15rdquo diameter and a length of up to 12 inches is surrounded by a rubber sleeve and

placed into the core holder using a threaded plunger (Figure 311) The gap around the rubber

sleeve inside the core holder is filled with water using a hand pump A piston pump which is

illustrated as confining pump in Figure 331 is filled with water and used to build up the confining

pressure around the rubber sleeve to hold the core firmly The pore pressure is applied using an

injection pump and regulated using a dome-loaded back pressure regulator (Figure 311) and

nitrogen gas bottle pressure A handpump illustrated in Figure 311 is used to adjust the back

pressure while the confining pressure is controlled directly through the CFS operation software

The confining pressure can go up to 350 bars (5000psi) that correspond to the expected reservoir

pore pressure at approximately 35km depth A two-piston syringe injection pump with wetted

parts made up of Hastelloy is used to inject corrosive fluid at a constant flow rate or pressure using

the control software (Figure 311)

Two pressure transducers are installed at the inlet (P1 Figure 311) and outlet (P2 Figure

311) points of the core holder having a full-scale range of 5000psi A set of high and lower end

differential pressure transducers are installed with ranges of +- 500 psi (∆P1 Figure 311) and

+- 8 psi (∆P2 Figure 311) The higher and lower end differential pressure transducers have an

accuracy of +-01 and 001psi respectively There are 4 needle valves (AV 1-4 Figure 311) that

are programmed to operate automatically in response to pressure build up in the CFS The pressure

relief valve can also be operated independently through the CFS software The pressure transducer

lines that connect the inlet and outlet points of the core holder to the pressure transducers (Figure

311) are filled with silicon oil to sense the pressure build up inside the core holder Permeability

72

can be determined using the ∆P across the core plug according to Eq 27 described in detail in

section 253 Chapter 2

The experiment is typically operated at temperatures of up to 80oC Heating is applied and

maintain through the heating mantle wrapped around the core holder and injection fluid lines going

into the core (Figure 311) The temperature of the injection fluid can be regulated discretely with

the help of a heating jacket wrapped around the injection pump accumulators They are connected

to the heating bath that directly provides heat to the injection pump cylinders The fluid passes

through the core is sampled in the fraction collector consisting of 80 5-mL sampling tubes The

tubes are changed automatically after a given sample volume or time

Figure 311 Schematic diagram of Core Flooding System facility School of Earth Sciences


73

32 Core-flooding Experiments Objectives and Sequence

The core flood dissolution experiments were initially aimed to validate the preliminary

numerical modelling results that displayed significant change in porosity and permeability of

quartz dominated Catherine Sandstone rock when reacted to alkaline reagent using NaOH The

core will also be flooded with acidic reagent using HCl to compare the effluent chemistry with the

modelling results The goal is to chemically enhance the permeability of Catherine Sandstone core

by dissolution of reactive minerals that are clogging the pore spaces It is important to prevent

fines mobilization within the rock due to flooding that can artificially modify the porosity and

permeability of the core thus overestimating the effects of geochemical reservoir stimulation A

continuous fluid samples collection and analysis were done throughout the core flooding operation

A new methodology to calculate the effective surface area of the individual minerals in a

consolidated rock is developed using the dissolved cations measured in the fluid samples using

ICP-OES (Inductively Coupled Plasma - Optical Emission Spectrometry) during the CFS

experiments The surface area of minerals is a critical input variable for modelling mineral

reactions in porous rocks (Audigane et al 2007 Xu et al 2009 2010 Yang et al 2014 Black et

al 2015 Beckingham et al 2016) The derived effective surface area is then incorporated in

TOUGHREACT to update the reactive transport modelling of geochemical stimulation near the

wellbore The experimental setup and sequence are described in the following section The

experiment 1 consisted of CFS operation trials at different injection rates temperature and

pressure The actual core flood dissolution experiments began from experiment 2 as described in

the following section

321 Experiment 2

The purpose of experiment 2 was to flood Catherine Sandstone core with pH 12 fluid in

order to observe mineral dissolution and subsequent porosity and permeability changes in the core

sample The targeted mineral during alkali flooding is quartz due to its high reactivity under alkali

conditions as discussed earlier in Section 133 (Chapter 1 Figure 131) A core sample of coarse

grained sandstone with a high porosity and permeability core F2-2a (Figure 321 Table 321)

was used for Experiments 2 The core was saturated in 001M NaCl solution as a pseudo formation

fluid using a vacuum desiccator to fill the connected pores with brine The experimental conditions

(Table 322) were designed in accordance to the actual reservoir depth of Catherine Sandstone in

74

the Northern Denison Trough (Section 233 Chapter 2) Due to restricted range of sensitivity

(lt500 gt01 psi) of high and lower end pressure transducers (500 and 8 psi) the flow rate must be

adjusted in order to observe a pressure differential along the core Absolute pressure below 01 psi

is not detectable and should not exceed 500 psi Figure 322 helped to estimate the required flow

rate in relation to the permeability of the core to achieve ∆P value in the range of 01-500 psi

Experiments 2 started with the injection of NaOH solution with a pH 12 at reservoir conditions

(Table 322) The injection rate was kept constant at 005 mLmin resulting in a total fluid

residence time of 6 hours in the core Since the core had a high permeability (495 mD) a relatively

high injection rate was required to observe a pressure differential to calculate in-situ permeability

(Figure 322) Consequently the experiment was changed to a sequence of low flow or lsquosoakingrsquo

periods (005 mLmin flow for about 24 hours) followed by short high flow or lsquoflushingrsquo intervals

(gt05 mLmin flow) leading to a pressure differential across the core plug used to calculate

permeability (Eq 27 Chapter 2 Section 253)

Table 321 Properties of Catherine Sandstone cores used in the experiments

Core Length

(cm)

Diameter

(cm)

Porosity

()

Permeability

(mD)

Pore Volume

(mL)

F2-2a 64 381 242 495 1766

F1-3a 6 381 2029 lt1 139

F1-3b1 51 381 1802 lt1 1046

F1-3b2 5 381 18 lt1 1026

F2-2b 52 381 242 1870 1435

75

Figure 321 Core sample F2-2a before flooding used in experiment 2

Figure 322 Pressure build up against injection rate in a core of 6cm length at 60oC

76

Table 322 Experimental Conditions of core flooding The temperature confining and back

pressure was kept constant at 60oC 3000 and 2000 psi throughout the experiments

77

Figure 323 Core sample F1-3a after flooding used in experiment 3 amp 4

322 Experiment 3

A sample with a high permeability (495 mD) was used in Experiments 2 and required a

high flow rate of gt 05 mLmin in order to observe a pressure differential across the core As a

consequence the fluid residence time in the core plug was short In Experiment 3 a sample with

a low permeability (lt 1mD) core F1-3a (Figure 323) was selected for the next core flood

dissolution experiment Figure 322 displays the range of injection rates that can be used in the

core F1-3 to calculate in-situ permeability during the experiment ∆P can be raised above 01 psi

with flow rate below 005mLmin (Figure 322) A low injection rate allows a longer residence

time with continuous permeability data A flushing interval as in Experiments 2 is not required to

measure permeability Apart from the core sample all the experimental conditions were kept the

same as in Experiments 2 (Table 322) A constant injection rate of 003mLmin is applied

throughout the experiment for approximately 7 days leading to a total of 22 pore volumes

323 Experiment 4

Experiment 4 is initially designed to observe mineral dissolution at pH 11 (25oC) as a peak

in dissolved silica concentration was observed in experiment 3 at pH 11 (See Figure 421 Chapter

78

4) The same core sample was used as in Experiment 3 core F1-3a with exact experimental

conditions to replicate Experiment 3 This time however the core was pre-saturated in 05M brine

since higher ionic strength helps to reduced fines mobilization in the rock (Vaidya amp Fogler 1992)

A constant injection rate of 003mLmin is applied throughout the flooding period Experiment 4

is divided into 3 stages based on the pH of injection fluid (Table 323) In Stage 1 a NaOH reagent

with a pH of 11 was injected in the core for 7 days followed by further high concentration of NaOH

(pH 12) The core was soaked in pH 10 fluid for approx 5 days at the end of stage 1 Stage 2 lasted

for 10 days in which alternative high and low concentration of NaOH was injected to verify the

observations made in stage 1 Stage 3 consisted of acid injection for approximately 3 weeks at

constant flow rate using 001M HCl

Table 323 Conditions of stage 1 2 and 3 in experiment 4

324 Experiment 5

The objective of Experiment 5 is to conduct a separate core flood experiment using a fresh

core plug to replicate results of Experiment 4 stage 3 (acid injection) Catherine Sandstone core

sample F1-3b1 was used that is part of the same core used in Experiment 3 and 4 (Figure 324)

The core sample was saturated in DI water under vacuum instead of brine to reduce sodium (Na)

Core Conf

Pressure

(PSI)

Back

Pressure

(PSI)

oC

Form

Fluid

Injected

Fluid

pH Flow

Rate

mLmi

n

Stage 1 F1-3a 3000 2000 60 05M

NaCl

0001001

00001M

NaOH

1011

amp12

003

Stage 2 F1-3a 3000 2000 60 05 M

NaCl

0001001M

NaOH

10

12

003

Stage 3 F1-3a 3000 2000 60 05 M

NaCl

001M HCl 2 003

79

background concentration in the fluid samples That will help to observe dissolved sodium in the

fluid samples due to dissolution of trace feldspar minerals in the core (if any) All other

experimental conditions kept the same as Experiment 3 (Table 323) The core was flooded with

HCl solution of pH 2 at a constant injection rate of 003mLmin over the period of 30 days 13

mgL of Lithium (Li) and 20mgL of strontium (Sr) were added as tracers with the injection fluid

The tracer injection will help to observe the fluid transport within the core by monitoring the tracer

recovery at the outflow The tracer breakthrough is expected to appear at the outflow after injecting

approximately 104mL of the reagent that is equivalent to one pore volume of the core F1-3b1

(Tables 321 amp 322)

Figure 324 Core F1-3b1 and b2 before flooding used in experiment 5 amp 6

80

Figure 325 Core F2-2 before flooding used in experiment 7

325 Experiment 6a and 6b

The objective of Experiment 6a was a) to reproduce results of Experiment 5 (acid flooding)

and b) to execute a combined acid and alkaline treatment in one experiment Experimental

conditions were kept the same as in the previous experiment in order to reproduce results of

Experiment 5 An untreated Catherine Sandstone core plug (F1-3b2) was used that is part of the

core plug used in experiment 5 (Figure 324) It is expected to have the same petrophysical

properties as F1-3b1 (Table 321) The core was flooded at a constant injection rate of 003mLmin

with HCl solution of pH 2 for 11 consecutive days which constitutes 47 pore volumes At the end

of the experiment the core was flooded with DI water for 4 days until the acid was completely

flushed out of the core After flushing the core with neutral fluid NaOH solution of pH 12 was

injected in Experiment 6b at a constant flow rate of 003mLmin to reconstruct and validate the

alkaline flooding data in Experiment 4 The other objective of experiment 6b was to compare the

dissolved silica and aluminium concentrations in the outflow samples at varying injection rates

After 13 days of flooding at a constant injection rate of 003mLmin the injection rate was lowered

to 0015mLmin which increased the fluid residence time to 11 hours After injecting 30 pore

volumes the injection rate was increased to 006 and 012mLmin for periods of 4 days each Due

to the low permeability of the core F1-3b2 the higher injection rates resulted in large pressure build

up at the inlet of the core (Figure 322) The pressure build up hindered in acquiring further high

injection rates and shorter fluid residence time in experiment 6b

81

326 Experiment 7a amp 7b

A highly porous and permeable sample core F2-2b (Figure 325 Table 321) was flooded

with alkaline and acidic reagent in Experiments 7a and 7b The aim was to achieve higher injection

rates and reduce fluid residence time further than experiment 6b The core was flooded with NaOH

solution of pH 12 in experiment 7a The experiment lasted for 30 hours with 3 different injection

rates of 05 1 and 2mLmin Approximately 17 pore volumes of fluid was injected at each injection

rate The core was flushed with DI water at the end of experiment 7a for 3 consecutive days to

flush out any residual NaOH reagent HCl solution with a pH of 2 was injected in the same core

in Experiment 7b once all the residual NaOH solution was removed Subsequently injection rates

of 025 0125 05 and 1mLmin were applied with each injection interval consisting of 10 pore

volumes The experiment lasted for 3 days

33 Fluid Sampling and Analysis

Fluid samples were collected in the fraction collector of the CFS 350 in intervals of 15

minutes to 5 hours depending on the injection rate and fluid residence time in the core Each sample

was analysed for pH and dissolved silica concentration during the experiments and a subsample of

12mL was acidified using 3 drops of concentrated HNO3 for later cation analysis using ICP-OES

The pH of the samples was measured using a pH probe which was calibrated every morning by

conducting a three-point calibration using pH buffers of 4 7 and 10 giving average slope of 94-

97 The total dissolved silica concentration in each sample was measured daily during the core

flood operation by using the yellow silicomolybdic acid colorimetric method (Alexander et al

1954 Thornton amp Radke 1988 Coradin 2004) A subsample from each fluid sample collected at

the outflow during the CFS experiment was mixed with sodium molybdate solution together with

1N sulfuric acid for the UV-Vis analysis In colorimetric technique molybdic acid reacts

specifically with dissolved silica and forms a yellow coloured silicomolybdate complex A UV-

Visible spectrophotometer (Agilent Cary 60) was used to measure the absorbance of the coloured

solution at a wavelength of 405 in the samples After completion of each experiment the collected

fluid samples were then analysed for dissolved cations using ICP-OES (Inductively Coupled

Plasma Optical Emission Spectrometry) Multi-element standards were used for the calibration of

the ICP-OES according to Table 324 Each sample was diluted at a ratio of 15 with 2 nitric

acid for the ICP-OES analysis so that the diluted sample concentration is within the calibration

82

range The required dilution factor was estimated from the silica concentration measured initially

by uv-vis spectrophotometry

Table 324 Standards used in the ICP-OES for fluid sample analysis

34 Aqueous Speciation Modelling

The Geochemistrsquos Work Bench (GWB) software (Bethke and Yeakle 2012) is an aqueous

geochemistry software which contains a set of modules including SpecE8 The SpecE8 module

allows to calculate the aqueous speciation and the stability of minerals in a given fluid at the given

temperature and pressure Other modules can be used to predict reactions over time (reaction path

modelling) and model reactive-transport (Bethke and Yeakel 2012) The program package that is

being used in the current project is called SpecE8 of GWB version 110 The elemental

composition of a fluid sample was acquired by ICP-OES analysis and used as an input for the

aqueous speciation modelling in fluids collected at the outflow of the core flood experiments The

speciation was calculated at each point of the experiments where pH and cations concentration (Si

Al and K etc) in the outflow stabilized The charge balance function is applied on aqueous

concentrations of Cl- and H+ in the speciation modelling of HCL and NaOH scenarios respectively

in order to fix the pH of the system The results helped in understanding the factors controlling

cations distribution at each phase of the core flood experiments The thermodynamic databases

Elements Si Fe Mg Ca Al Na K Li Sr

Standard

Concentration

[mgL]

1000

1000

1000

1000

1000

1000

1000

100

10

Initial Dilution 075mL each element into

12mL of 2 HNO3

075mL each

element into

1275mL of 2

HNO3

Undiluted Undiluted

Calibration

Concentrations

[mgL]

50 20 10 350 075

50 20 10 350

075

100 50

30 10 2

10 5 3 1

02

83

used in the speciation are lsquothermotdatrsquo and lsquothermocomV8R6+tdatrsquo The lsquothermotdatrsquo database

was developed by LLNL and serves as the default thermodynamic database in GWB The

lsquothermocomV8R6+tdatrsquo is the expanded version of the LLNL database with more organic

species and radionuclides

84

CHAPTER 4

4 Results and Observations of Core Flooding Experiments

41 Experiment 2

The purpose of Experiment 2 was to flood Catherine Sandstone core with a solution with

a pH of 12 in order to observe mineral dissolution and subsequent porosity and permeability

changes in the core sample The core sample F2-2a with a permeability of 495 was flooded with a

NaOH solution with a pH of 12 at a constant injection rate of 005 mLmin Experiment 2 consisted

of soaking periods with an injection rate of 005 mLmin and flushing periods with an injection

rate of gt 05 mLmin as described in the previous section (see Section 331 Chapter 3) Flushing

periods were used to determine ∆P and respective permeability High flow rates resulted in fines

mobilization in Experiment 2 which was visible in the form of a fine suspension collected at the

outflow (Figure 411) Fines migration led to mechanically induced permeability increase during

each flushing period High injection rates during soaking periods in experiment 2 were also

necessary to build up a significant differential pressure that can be measured by the pressure

transducers on the core flooding instrument (Figure 323 Chapter 3) However due to a large

amount of unconsolidated fine sediments in the Catherine Sandstone core it was not feasible to

run experiments at a high flow rate The fines collected during experiments 2 were analysed using

XRD (Table 25 Chapter 2) and showed a high percentage of kaolinite (81) Even at injection

rates above 2 mLmin which reduced the residence time to a few minutes pressure build up was

less than 015 psi (Figure 412) As discussed in the methodology section (Chapter 3 Section 31)

the minimum sensitivity of the 8 psi ∆P transducer is 01 psi Therefore a differential pressure

below 01 psi resulted in unrealistic permeability readings Furthermore high injection rates during

soaking periods required large volume of reagent to run the experiment for several days in order

to achieve noticeable dissolution Hence this significantly increases the operational cost of a

geochemical stimulation Figure 413 shows the dissolved silica concentration in the fluid samples

collected over 5 hours intervals The total silica concentration plateaued at 40-43 mgL after 20

85

hours (Figure 413) indicating some dissolution of quartz and other clay minerals at a residence

time of 6 hours and a pH of 12 (NaOH)

Figure 411 Suspended fines in the fluid samples collected during Experiment 2

86

Figure 412 Permeability (left) and differential pressure (∆P) (right) plotted against injection

rate in Experiment 2

Figure 413 Dissolved silica concentrations in fluid samples during Experiment 2

42 Experiment 3

Given the extent of fines migration in Experiment 2 prohibiting to observe a change in

porosity and permeability caused by mineral dissolution a low permeability Catherine Sandstone

core was flooded with a pH 12 NaOH solution in Experiment 3 The Catherine Sandstone core

sample F1-3a (Figure 323 Section 32 Chapter 3) with a low permeability (lt 1mD) was selected

for Experiment 3 NaOH solution of pH 12 was injected into the core F1-3a at constant injection

rate of 003 mLmin pH in the fluid outflow samples of Experiments 3 to 7 were measured at a

temperature of 25oC Since the core flood experiments are conducted at 60oC the in-situ pH may

differ from the ex situ pH particularly during alkaline flooding (Table 41) Table 41 shows the

theoretical pH when the temperature of highly acidic pH-neutral and highly alkaline solutions is

increased from 25 to 60oC It is shown that the pH variation due to change in temperature is most

pronounced under highly alkaline conditions

20

25

30

35

40

45

0 20 40 60

silic

a (m

gl)

Hours

Experiment 2

87

No fines mobilization was observed in the fluid samples at the outflow due to a low

injection rate It took approx 48 hours and 6 pore volumes (PV) (Table 321) for the fluid samples

at the outflow to reach the injection pH of 12 (Figure 421) Core permeability as a result of a

change in ∆P took the same time to adjust and remained constant throughout 7 days of the injection

period (Figure 422) Silica concentration in the fluid samples increased at the beginning of the

experiment (0 ndash 80 hours) but later began to drop off until plateauing at approx 65 mgL after 120

hours or 15 PV of NaOH injection (Figure 421) As soon as the fluid samples started becoming

alkaline a gradual increase in aluminium concentration is observed that plateaus at approx 15

mgL (Figure 421) Only dissolved silica and aluminium were detectable (Figure 421)

suggesting that only quartz and kaolinite were dissolving The peak in silica concentration could

be pH dependent since the maximum silica concentration was observed at the outflow pH of 11

the dissolved silica started declining soon as the pH increases to 12 (Figure 421) Another

explanation for the peak in silica could be the presence of amorphous silica that dissolved only at

the beginning of Experiment 3

Table 41 Changes in pH due to change in temperature

pH Range Temperature

25degC 60degC

Acidic pH 200 pH 201

Basic pH 1200 pH 112

88

Figure 421 Dissolved cations concentrations and pH at the outflow during Experiment 3 The

breakthrough of injection pH is marked by vertical bar

Figure 422 Measured permeability (left) in response to change in ∆P (right) along the core

during experiment 3

0

2

4

6

8

10

12

14

0

15

30

45

60

75

90

105

120

0 20 40 60 80 100 120 140 160 180

pH

Con

c (

mg

l)

Hours

Experiment 3

SiAlCaFepH

pH Breakthrough

89

43 Experiment 4

Experiment 4 was designed to observe mineral dissolution at a pH of 11 given a maximum

dissolved silica concentration was observed in Experiment 3 before the pH in the outflow fluid

reached a pH of 12 (Figure 421) The same core sample was used as in Experiment 3 core F1-

3a and the same experimental conditions applied except for the difference in the pH of the

injection fluid The injection rate was kept constant to 003 mLmin throughout Experiment 4

Stage 1a started with the injection of pH 11 fluid using NaOH solution Silica concentration in the

fluid samples gradually increased and became constant at 10mgL after 4 days of injection (Figure

431) The silica concentration in the fluid sample at pH 11 was 20-30mgL lower than the

anticipated silica concentration based on Experiment 3 No aluminium was detected in the fluid

samples at this stage This observation suggests that the silica peak in Experiment 3 could be the

consequence of some trace silica mineral that flushed out few hours later The pH of the injection

fluid was increased to 12 in stage 1b and then lowered to lt10 (stage 1c) to observe silica

concentration at the outflow while changing from pH 12 to 10 A new injection fluid of pH 12

was added after 7 days of pH 11 injection (Stage 1b) The silica concentration in the outflow

jumped to 40mgL and was stable at 28-30mgL (Figure 431) The pH of the injection fluid was

then lowered to 10 (Stage 1c) resulting in an immediate drop in silica concentration without

showing a silica peak in the fluid samples at the outflow Aluminium concentration in the outflow

appeared as soon as the pH increased above 11 and later it followed the same trend as dissolved

silica Apart from silica and aluminium potassium is also detectible in the fluid samples within a

pH range of pH 10 to 12 The potassium concentration declined steadily at pH 11 and 12 in Figure

431 The potassium concentration spiked again and became steady as soon as the pH dropped to

10 (Figure 431)

In Stage 2 alternate high and low concentrations of NaOH solution were injected into core

F1-3a for 10 days The aim was to validate the reproducibility of the data generated in the previous

NaOH injection runs Stage 2 started with the injection of a high concentration of NaOH solution

(pH 12 at 25oC Stage 2a) which gave rise to elevated silica and aluminium concentrations in the

outflow samples (Figure 432) as seen in previous Stage 1b (Figure 431) The silica concentration

in the fluid samples plateaued at 45-49mgL after 2 days of injection together with aluminium The

injection pH was lowered to 10 (Stage 2b) for approximately 3 days until silica and aluminium

90

concentrations plateaued again A pH 12 fluid was then injected for the second time (Stage 2c) and

observed similar silica and aluminium concentration trends (Figure 432) The initial increase in

the silica concentration concurrent with an increase in pH before the pH plateau is reached could

be associated with fines mobilization and dissolution (Vaidya amp Fogler 1992) A rise in the pH of

the injection fluid may detach fines from the rock matrix which in turn may resulting an additional

dissolved silica peak The potassium concentration is below 1mgL when injecting a fluid with a

pH of 12 and only becomes detectable when injecting a fluid with at pH of less than 12 At the end

of Stage 2 the core was flushed with deionised water (Stage 2d) to get rid of any residual NaOH

solution in the core

Figure 431 Cation concentrations and pH in fluid samples in Experiment 4 Stage 1 Vertical

bars indicate the different stages of the experiment where the injection fluid was changed and the

new composition being injected is labelled

6

7

8

9

10

11

12

0

5

10

15

20

25

30

35

40

45

0 2 4 6 8 10 12 14

pH

Con

c (

mg

l)

Days

Experiment 4 (Stage 1)

SiAlCaMgFeKpH

Stage 1a pH= 11

05M NaCl

Stage 1b pH= 12

05M NaCl

Stage 1c

pH= 101

05M NaCl

91

Figure 432 Cation concentrations of fluid samples in Experiment 4 Stage 2 Vertical bars

indicate the different stages of the experiment

In stage 3 of Experiment 4 a 001 M HCl solution with a pH of 2 was injected in core F1-

3a The goal of acid injection was to enhance dissolution of other silicate minerals than quartz in

the core such as kaolinite and muscovite These minerals might control the interconnectivity of

pores since no change in the permeability of the core was observed throughout the period of NaOH

injection At the initial stage of acid injection pH at the outflow started to increase after 10 hours

from 76 to 104 (Figure 433) The pH increase could be caused by residual NaOH in the pore

space The fluid samples remained at a pH of about 10 for approximately 2 days and 6 PV result

in a build-up of silica and aluminium in solution (Figure 433) As soon as the pH of fluid samples

started decrease aluminium gradually disappeared while silica remained constant for 2 days at

near-neutral pH As the pH decreased further silica concentrations in the fluid samples increased

to more than 100mgL followed by an increase in magnesium and calcium concentration (Figure

433) that did not appear in the previous alkaline injection runs (stages 1 and 2 Figures 416 and

417 respectively) All three ions (Ca Mg Si) followed the same trend while the outflow was

buffered at a pH of about 6 Once magnesium and calcium started to gradually decrease pH in the

outflow samples dropped suddenly followed by the reappearance of aluminium This trend of pH

with magnesium and calcium suggests the presence of carbonates in trace amounts that buffer the

6

7

8

9

10

11

12

0

10

20

30

40

50

60

14 16 18 20 22 24

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

Stage 2a

pH= 12

001M

NaCl

Stage 2b

pH= 10

05M NaCl Stage 2c

pH= 12

DI water

Stage 2d

pH= 75

05 M NaCl

92

pH and release calcium and magnesium Once all carbonate minerals were dissolved the fluid

samples became acidic The data also suggests that aluminium is only stable in highly alkaline or

acidic solutions and precipitates under neutral pH conditions Speciation modelling was performed

based on the measured water composition of acidic pH-neutral and alkaline samples using

Geochemistrsquos Work Bench (GWB) The input data for speciation modelling at pH 12 is given in

Table 42 The resulting mineral saturation indices are plotted in Figure 435 Figure 435

illustrates that the saturation indices of all the aluminium bearing minerals such as kaolinite

boehmite gibbsite and muscovite are close to or above 0 suggesting that they are supersaturated

or at equilibrium with the system at pH 56 Thus all the aluminium bearing minerals are

potentially precipitating under pH-neutral conditions (here represented by a pH of 56 Fig 419)

which is in agreement with the lack of detectible dissolved aluminium when the pH drops below

7 (Figure 433) Another observation is the detection of dissolved iron in the fluid samples

following a similar trend as aluminium Similar to aluminium the saturation state of iron-bearing

minerals showed iron oxide is near saturation at a pH of 12 and became undersaturated under

acidic conditions Potassium became detectible as soon as the pH dropped below a pH of 6 because

muscovite is only soluble under alkaline and acidic conditions and is highly supersaturated under

pH-neutral conditions (Figure 435)

Figure 433 ICP-OES analysis of fluid samples in exp 4 stage 3 Vertical bar indicating

beginning of acid injection

0

2

4

6

8

10

12

000

2000

4000

6000

8000

10000

12000

14000

30 32 34 36 38 40 42

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

pH= 2

001M HCl

93

The permeability of the core remained constant during the injection of pH 11 fluid until it

varied to pH 12 (Figure 434) A sudden decrease in the permeability of the core after 10 days of

injection was observed in Figure 434 which appeared 2 days after increasing the pH of the

injection fluid to 12 The permeability of the core dropped from 024mD to 016mD (Figures

419) During stage 2 of experiment 4 with varying pH and NaOH concentrations the permeability

remained at the lowest value (016mD) until the alkali injection was halted after 28 days As soon

as the acid injection began in the final stage 3 of experiment 4 the permeability started increasing

and reached the initial value of 024mD before the experiment was stopped (Figures 419)

Figure 434 Permeability calculated in response to the ∆P fluctuation in experiment 4 The blue

green and red bars indicate change in fluid composition from alkaline basic to acidic respectively

01

014

018

022

026

03

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

Experiment 4

pH= 12

pH= 2pH= 75

pH= 11

Stage 2

Stage 1

Stage 3

94

Table 42 Input concentrations of dissolved cations used in the GWB speciation modelling at pH

12 (Figure 435) The pH of the system is given as a molar concentration of Na+ and H+ used in

experiment 4 which adjust the pH to the in-situ pH of the experiment at 60oC

Cations Concentration Unit

Al 3054 mgL

Si 4968 mgL

K 048 mgL

Na+ 001375 moll

H+ 10e-12 moll

Fe Mg Ca 178e-6 mgL

Figure 435 Saturation states of minerals at different pHrsquos (Time intervals 43 39 and 17 days of

Exp 4) from speciation modelling results using GWB lsquothermottdatrsquo database Negative and

positive values refer to below (undersaturated) and above (supersaturated) mineral equilibrium

respectively

-15

-10

-5

0

5

10

Quartz(SiO)

Chalcedony(SiO)

Kaolinite(AlSiO)

Boehmite(AlOH)

Gibbsite(AlOH)

Muscovite(KAlSiO)

FeO

Satu

ratio

n St

ates

(QK

)

Minerals

Experiment 4 (GWB Speciation)

pH 2

pH 56

pH 12

95

44 Experiment 5


core plug to replicate results of Experiment 4 Stage 3 (acid injection) Catherine Sandstone core

sample F1-3b1 was used that is part of the same core used in Experiment 3 and 4 (Figure 324

Section 32 Chapter 3) The injection rate was kept constant to 003mLmin throughout

Experiment 5 Injection was started with HCl solution of pH 4 The fluid samples collected at the

outflow remained neutral after 4 days of injection (Figure 441) which may indicate buffering

due to the dissolution of carbonates and silica minerals The pH in the injection fluid was then

reduced to 33 (Figure 441) causing the pH of the outflow fluid samples to drop from 7 to 59

after 6 days of injection The silica concentration remained constant at approximately 18mgL

while calcium and magnesium concentrations started to increase gradually (Figure 441) After 10

days a stock solution of HCl with a pH of 22 was injected into the core which led to a rapid

increase in calcium and magnesium concentrations in the fluid samples together with silica The

outflow fluid samples were buffered at the outflow was buffered at a pH of 6 for 4 days before the

calcium concentration and pH started to drop (Figure 441) Silica concentrations above 100mgL

were reached while the pH dropped close to 2 after 7 days of pH 2 injection The calcium and

magnesium concentrations decreased below detection limit after 7 days while at the same time

aluminium gradually increased to approximately 40mgL In order to verify complete dissolution

of carbonates from the core the injection fluid was changed to a pH of 3 and later a pH of 4 which

resulted in a silica concentration drop in the fluid samples Once the silica concentration in the

outflow reached constant values the pH in the HCl solution was set to 2 again which caused

aluminium and silica concentrations to rise again No dissolved calcium and magnesium were

detected in the fluid samples during this phase which validates the earlier hypothesis of complete

carbonate dissolution at that point (Figure 441)

A steep trend of permeability increase was observed in experiment 5 which began after a

week of acid injection (Figure 442) The permeability value of the core during the entire acid

injection increased from 03 to 08mD (Figure 442) Unlike previous observation during

experiment 4 (Figure 434) there was no reduction in the permeability of the core observed during

experiment 5

96

Figure 441 Cation concentrations and pH of fluid samples during acid injection in Experiment

5 Black bars indicate a change of the injection fluid

Figure 442 Permeability trend (left) during experiment 5 calculated using variation in ∆P

(right)

97

Lithium (Li) and strontium (Sr) were added to the injection fluid to test the suitability of

tracers characterise dispersion and mixing within the core in Experiment 5 A 131mgL lithium

tracer was added to the HCl injection solution with a pH of 3 and was injected after 18 days of

acid injection At that time the pH had dropped to 2 and carbonates were completely dissolved

(Figures 4110 and 4111) The lithium tracer was completely recovered at in the outflow samples

after approximately 10 to 15 hours which is equivalent to 1-15 pore volumes (PV) (Figure 443)

Later with the change of injection fluid 20mgL strontium tracer was added to the HCl stock

solution of pH 4 and injected into core (Figure 443) The outflow lithium concentration dropped

after 1PV (Figure 443) due to the injection of new fluid containing strontium only Strontium

was not recovered at the outflow even after approximately 5 days of pH 4 injection Subsequently

a solution with 6mgL lithium was injected once again with a HCl stock solution with a pH of 2 to

verify the previous tracer trend Lithium appeared in the fluid samples after 10 hours together with

strontium The appearance of strontium as soon as the pH dropped to 2 suggests Sr adsorption to

some minerals presumably clay minerals at a pH above 2 (Faucher et al 1952 Wahlberg et al

1965) Therefore strontium is not a suitable tracer under the applied experimental conditions of

pH 4

Figure 443 Lithium and strontium tracer concentrations recovered at the outflow in Experiment

5 Black bars indicate times when the injection fluid composition was changed

98

45 Experiment 6a

The objective of Experiment 6a was to reproduce results of acid injection in Experiment 5

An untreated Catherine Sandstone core plug (F1-3b2) was used that is part of the core plug used in

Experiment 5 (Figure 324 Section 32 Chapter 3) The injection rate was kept constant at 003

mLmin throughout the flooding period of 13 days Experiment 6a began with the injection of HCl

solution with a pH of 2 to verify the reproducibility of the data acquired in Experiment 5 (Figure

441) A fresh Catherine Sandstone core was used in Experiment 6 and the dissolved cations

followed similar trends to those observed in Experiment 5 (Figure 451) The calcium and

magnesium concentrations increased to 90 and 60mgL respectively indicating carbonate

dissolution while the pH remained buffered at pH 55 A drop in the pH occurred soon after

calcium and then magnesium dropped to less than 10mgL and remained constant (Figure 451)

The silica concentration showed a peak that corresponds to Day 15 of Experiment 5 (Figure 441)

and later reached stable concentrations of approx 45mgL Dissolved aluminium increased in

concentration later once fluid became acidic and the pH stabilized at 2 The late dissolved

aluminium increase is controlled by pH as seen in Figure 451 The potassium concentration

appeared at the beginning of pH 2 injection and decreases to a plateau once the pH dropped to 2

(Figure 451)

Figure 451 Dissolved cations in the fluid samples collected during experiment 6a The injection

rate is kept constant to 003 mLmin

0

1

2

3

4

5

6

7

0

15

30

45

60

75

90

105

120

135

0 5 10

pH

Con

c (

mg

l)

Time (Days)

Exp 6a (pH 2)

AlCaFeKMgSipH

99

46 Experiment 6b

Experiment 6b aimed to reproduce the results of the alkaline flooding experiment acquired

during pH 12 injection (Experiment 4 Stage 1 and 2) The Catherine Sandstone core F1-3b2 is

used in experiment 6b and the injection rate was kept 003 mLmin during initial 13 days of

flooding Experiment 6b showed similar trends in dissolved aluminium and silica as in Experiment

4 where trends of dissolved silica and aluminium concentrations varied as a function of pH In

Stage 2 of Experiment 6b variable injection rates were applied to observe the effect on mineral

dissolution and respective changes in the dissolved silica and aluminium concentrations (Figure

461) After 12 days of injection at 003mLmin the injection rate was reduced to 0015 mLmin

which resulted in an approximately 10mgL increase in the dissolved silica concentration while

the dissolved aluminium concentration stayed fairly constant during this period Once the

dissolved silica concentration reached a plateau after 10 days the injection rate was increased to

006mLmin causing a 20mgL decrease in the outflow silica concentration The injection rate was

then dropped back to the initial injection rate of 003mLmin which increased silica back to the

earlier concentration of approx 65mgL (Figure 461) Contrary to dissolved silica dissolved

aluminium did not show abrupt changes in concentration following a change in the injection rate

The dissolved aluminium concentration remained constant at an average concentration of

approximately 40mgL in response to above mentioned flow rates At the end of Experiment 6b

the injection rate was increased to 024mLmin which caused both silica and aluminium

concentrations to drop abruptly (Figure 461)

Speciation modelling was carried out using the water composition at times representing

different flow rates to better understand the observed aluminium concentrations in the outflow

When using the thermodynamic database thermodat common Al-bearing minerals remained

undersaturated at all stages of the experiment (Figure 462) which suggested aluminium

precipitation is not expected to occur A similar observation was for Experiment 4 at pH 12 and at

an injection rate of 003mLmin (Figure 435) Speciation modelling was then carried out for the

same time intervals of Experiment 6b using the thermodynamic database

thermocomV8R6+tdat Modelling results show boehmite gibbsite and muscovite to be in

equilibrium with the fluid (with QK = +- 05) at all flow rates except for muscovite being

undersaturated at the highest flow rate (Figure 463) One of the main differences between the

100

two databases is the solubility for aluminium bearing minerals The thermodynamic database

thermocomV8R6+tdat has slightly lower saturation constants for aluminium bearing mineral

than thermotdat as discussed for gibbsite by Pokrovskii amp Helgeson (1995)

Figure 461 Dissolved cation concentrations in response to variable injection rates and respective residence times in Experiment 6b Black lines indicate times at which the injection rate was changed A constant pH of 12 was measured after Day 7

101

Figure 462 Saturation states of minerals during different stages of Experiment 6b (Time intervals 26 12 31 34 and 45 days) from speciation modelling of the system using GWB and the lsquothermottdatrsquo database The legends represent injection rate and residence time

Figure 463 Saturation states of minerals during different stages of Experiment 6b (Same as Figure 462) from speciation modelling of the system using GWB and the lsquothermocomV8R6+tdatrsquo database The legends represent injection rate and residence time

-6

-5

-4

-3

-2

-1

0

Quartz Chalcedony Kaolinite Boehmite Gibbsite Muscovite

Satu

ratio

n St

ates

(QK

)

Minerals

Experiment 6b (Thermotdat)0015mlmin(684min)

003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

-35

-3

-25

-2

-15

-1

-05

0

05


Satu

ratio

n St

ates

(QK

)

Minerals

Experiment 6b (V8R6+tdat)

0015mlmin(684min)

003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

102

47 Experiment 7a

The aim of Experiment 7a was to achieve short fluid residence times by increasing the

injection rates relative to Experiment 6b The highly porous and permeable core sample F2-2b

(Table 321 Section 32 Chapter 3) was used in Experiment 7 Experiment 7a consisted of the

injection of NaOH solution with a pH of 12 at flow rates of 038 05 1 and 2 mLmin Contrary

to Experiment 4 and 6 dissolved silica and aluminium concentrations in the outflow fluid samples

responded similarly at higher flowrates (Figure 471) During the injection at a rate of 05 mLmin

dissolved silica and aluminium concentrations plateaued at 22 and 10mgL respectively

Increasing the injection rate to 1 mLmin led to a further drop in silica and aluminium concentration

to 11 and 6mgL respectively Increasing the injection rate further to 2 mLmin led to decreasing

silica and aluminium concentrations of 38 and 76 mgL respectively Speciation modelling

results using the water composition at selected times representative of different flow rates and

using the thermodynamic database thermocomV8R6+tdat are illustrated in Figure 472 It

shows that all the major rock forming minerals are undersaturated at the given high flow rates

suggesting mineral precipitation is not expected Consequently dissolved aluminium and silica

concentrations correlate with the fluid residence time which will be discussed further in Chapter

5 At such short residence times the dissolved potassium concentration in the outflow fluid samples

was below 1mgL

103

Figure 471 Dissolved cation concentration in response to varied injection rate Black bars

indicate change of injection rate

Figure 472 Saturation states of minerals during Experiment 7a (Time intervals 75 27 and 285

hours) from speciation modelling of the system using GWB and the lsquothermocomV8R6+tdatrsquo

database The legends represent injection rate and residence time

0

2

4

6

8

10

12

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30

pH

Con

c (

mg

l)

Hours

Experiment 7a_pH 12

Al

K

Si

pH

05 mlmin038 mlmin 1 mlmin

2 mlmin

-18

-16

-14

-12

-10

-8

-6

-4

-2

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7a_pH 12

05 mlmin(29min)

1 mlmin(14min)

2 mlmin(7min)

104

48 Experiment 7b

The objective of Experiment 7b was to achieve higher injection rates and reduced fluid

residence time than in previous acid injection experiments (Experiments 5 amp 6a) The same

Catherine Sandstone core sample F2-2b as in Experiment 7a was used Experiment 7b began with

the injection of HCl solution with a pH of 2 and a constant flow rate of 025mLmin A spike in

dissolved magnesium calcium and silica was observed in the first 10 hours while pH remained

neutral in the outflow samples (Figure 481) As soon as the dissolved magnesium and calcium

concentrations started to decline the outflow fluid pH dropped and the dissolved aluminium

increased (Figure 481) Once aluminium and silica concentrations plateaued on 38th Day the

injection rate was doubled to 05mLmin and subsequently to 1mLmin causing as similar response

in the trends of dissolved silica and aluminium concentrations (Figure 481) The GWB speciation

modelling of Experiment 7b shows all the minerals are undersaturated (QK lt -05) at the above

flow rates including quartz and chalcedony (Figure 482) Dissolved potassium concentration is

very low at the short residence time as reported for Experiment 7a (Figure 471)



0

2

4

6

8

10

12

0

10

20

30

40

50

60

0 20 40 60

pH

Con

c (

mg

l)

Hours

Experiment 7b_pH 2

Al

Ca

Fe

K

Mg

Si

pH

025 mlmin

0125 mlmin

05 mlmin1 mlmin

105

Figure 482 Saturation states of minerals during different stages of Experiment 7b (Time

intervals (20 495 and 6825 hours) from speciation modelling of the system using GWB and the

lsquothermocomV8R6+tdatrsquo database The legends represent injection rate and residence time

-25

-20

-15

-10

-5

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7b_pH 2

025mlmin(57min)

05 mlmin(29min)

1 mlmin(14min)

106

CHAPTER 5

5 DISCUSSION

51 Determining the Effective Surface Area (ESA) of Minerals

This research project was undertaken with the intend to investigate the feasibility of

enhancing porosity and permeability in siliciclastic reservoir rocks by means of geochemical

reservoir stimulation Core flood experiments have been conducted to assess the dissolution of

minerals as a function of pH The dissolution of reactive minerals is controlled by various factors

including the pH and the mineral surface area Rate constants for various silicate minerals as a

function of pH has been derived by various authors (Stober 1967 Rimstitdt and Barnesh 1980

Chou and Wollast 1985 Knauss and Wolery 1987 Carrol amp Walther 1990 Bennett 1991

House and Orr 1992 Ganor et al 1994 Palandri and Kharaka 2004 Mitra 2008 Oelkers et al

2008 Crundwell 2015) and is discussed in detail in Chapter 1 The reaction rate law used in

TOUGHREACT modelling is defined in Eq 12 (Chapter 1) and was adopted from Lasaga et al

(1994) Greater dissolution rates can cause greater alteration in the volume fraction of a mineral

contained in the rock within a given time The change in mineral volume fraction modifies the

porosity and permeability of the rock (Eq 16 amp 17 Chapter 1) One of the key parameters that

determines the reaction rate of a mineral in Eq 12 (Chapter 1) is reactive surface area (Helgeson

et al 1984 Velbel 1985 Haggerty and Gorelick 1995 Kieffer et al 1999 Colon et al 2004

Noiriel et al 2009 Luquot and Gouze 2009 Phan et al 2011 Gouze and Luquot 2011 Navarre-

Sitchler et al 2013 Hellevang et al 2013 Peters 2009 Landrot et al 2012 Golab et al 2013

Bolourinejad et al 2014 Lai et al 2015 Black et al 2015 Beckingham et al 2016 Beckingham

et al 2017) The reactive surface area (An) of a mineral is directly proportional to its reaction rate

according to Eq 12 There is a wide range of surface area values reported in the literature and is

used in reactive transport modelling studies (Black et al 2015 Lai et al 2015 Beckingham et

al 2016) In order to reduce the uncertainty of predicted mineral reaction rates it is important to

derive the site-specific surface area of minerals and to incorporate the realistic values in reactive

transport models Here a new methodology is developed to estimate the effective mineral surface

area in consolidated siliciclastic sandstone The derived mineral surface areas for the Catherine

107

Sandstone will later be used in the TOUGHREACT models to simulate geochemical stimulation

with alkaline or acid reagents

The rate equation (Eq 12 Chapter 2) given by Lasaga (1994) is rearranged (Eq 5) to

reflect the conditions of a core flood experiment

xylowast = (5)

Where r is the reaction rate in the units of mols k is the dissolution rate constant in molcm2s

and A is the reactive surface area in cm2

Taking the example of a core sample consisting of a single mineral that is flooded with

reactive fluid in a core flooding system (Figure 311 Chapter 3) the following steps are used to

determine the effective surface area of the mineral The first step is to determine the residence time

of the injected fluid in the core using Eq 51

Rt = 78z lowast V|= lowast 60 (51)

Where Rt is the residence time of fluid in the core calculated in seconds Q is the flow rate in units

of mLmin and Vp is the pore volume of the core in units of mL

Secondly the steady state concentration of dissolved cations in fluid samples collected

during the core flood experiment is converted to units of mass per pore volume using Eq 52

XR= CR lowast | (52)

Where lsquomirsquo is the mass per pore volume in milligrams where lsquoirsquo refers to any cation (SiKAlCa)

observed in collected fluid samples Ci is the concentration of species i in mgL Vp is the pore

volume of the core in litres (L)

Finally Rt (Eq 51) and mi (Eq 52) are put together as reaction rate lsquorrsquo in Eq 5 to

determine the effective surface area of a single mineral contained in the core using Eq 53

= (Sj)M (53)

108

Where Sa is the effective surface area in cm2g Dr is the temperature dependant dissolution rate

constant of the mineral in mgcm2sec converted from molecm2sec (k in Eq 5) as reported in

literature sources (Table 51) and M is the mass of the mineral in the core sample in grams as

determined using the weight percentage from XRD analysis (See Table 25 Chapter 2) and dry

weight of the core

The effective surface area of minerals in Catherine Sandstone cores is calculated by using

ion concentrations measured by ICP-OES in fluid samples that were collected during core flood

experiments (Chapter 4 Section 41) Flooding the core with fluids with a pH of 2 and 12 caused

mineral dissolution and respective increase in dissolved ions in the fluid samples at the outflow

The experiments were conducted at a constant flow rate and at a representative reservoir

temperature and pressure (Table 322 Chapter 3 Section 32) The residence time of the injected

reagent in the core is calculated from the pore volume and flow rate (Eq 51) The pore volume of

the sample was calculated from the porosity and the dimension of the core as described in Chapter

2 (Section 252) The Catherine Sandstone cores used in the experiments contain three major

minerals quartz (SiO2) kaolinite (Al2Si2O5(OH)4) and muscovite (KAl2 (AlSi3O10) (F OH)2)

according to XRD analysis (Table 25 Chapter 2) Silica is found in all three and aluminium is

found in two of the three minerals Once the residence time of fluid (Rt) in the core (Eq 51) is

calculated the following steps lead to the sequential calculation of the effective mineral surface

areas of muscovite kaolinite and quartz

1 The effective surface area of muscovite is calculated using the total dissolved potassium

concentration in the fluid outflow the muscovite concentration in the core sample and the

temperature-dependant dissolution rate constant of muscovite at pH 2 and 12 according to Knauss

amp Wolery (1989) and Oelkers et al (2008) respectively The dissolution rates (Dr) reported in

literature are in units of molecm2middotsec (Table 51) which must be adjusted to the temperature used

in the experiment (60 degC) according to Eq 14 and 15 and converted to units of mgcm2middotsec in

order to determine the effective surface area in cm2g using Eq 53

2 The total aluminium (Al) released by muscovite is estimated by the ratio of potassium

and aluminium in the chemical formula of muscovite (Table 27 Chapter 2) After accounting for

moles of aluminium from muscovite (Almuscovite) it is assumed that the remaining aluminium in

the fluid samples originated from kaolinite dissolution (Alkaolinite Eq 54) given no other Al-

109

bearing minerals were detected The dissolution rate of kaolinite at pH 12 reported in Carroll amp

Walther (1990) (Table 51) is then used to derive the effective surface area of kaolinite in the core

sample (Eq 52 amp 54)

Al kaolinite= Al total ndash Al muscovite (54)

3 The effective surface area of quartz in the core sample is calculated similarly using Eq

52 and 53 and the silica concentration in fluid samples However total dissolved silica in the

fluid would also have contributions from muscovite and kaolinite as all three of them contain silica

The silica concentration due to dissolution of kaolinite and muscovite can be estimated by their

stoichiometry using the ratio of aluminium to silica in their formula (Table 27 Chapter 2) Silica

in the fluid samples originating from quartz dissolution (Siquartz) can be estimated by subtracting

the moles of silica due to the dissolution of muscovite (Simuscovite) and kaolinite (Sikaolinite) from the

total moles of silica in the effluent (Eq 55)

Si quartz = Si total ndash Si muscovite ndash Si kaolinite (55)

The residence time of fluid in the core and the pore volume of the core is already known

from the previous calculations (Eq 51) The silica concentration as a result of quartz dissolution

(Eq 55) is used in Eq 52 as input to determine the effective surface area of quartz in cm2g using

Eq 53

110

Table 51 Dissolution rates of minerals at pH 2 and 12 at 60oC reported in the literature The

rate constants are adjusted to the experimental pH and temperature using Eq 14 amp 15 (See

Chapter 1 Section 141) The extreme right column represents rate constants adjusted to pH 112

(60oC) for comparison with alkali injection experiments (See Table 41 Section 42 Chapter 4)

511 Core Flood Experiments with Low Flow Rate

The effective surface area of major minerals contained in the Catherine Sandstone cores

are calculated by using ICP-OES data of the fluid samples that were collected during core flood

dissolution experiments (Chapter 4 Section 41) Flooding the core with fluids of pH 2 and 12

enabled mineral dissolution that released dissolved ions in the fluid samples at the outflow The

dissolved potassium aluminium and silica concentrations are used as indicator ions released due

to the dissolution of muscovite kaolinite and quartz respectively as described above Experiments

4 to 6 were conducted at a constant injection rate of 003mLmin (Table 322 Chapter 3 Section

32) The injection rate of 003mLmin was equivalent to a fluid residence time of 5 to 8 hours in

Dissolution Rate of Minerals (60oC)

pH rate

(molcm2s) Literature rate (molcm2s)

(Corrected for pH 112 Alkali

Injection Experiments)

Quartz via Si

2 32e-16 Knauss amp Wolery 1987 -

12 15e-12 61e-13

Kaolinite via Al

2 24e-16 Carrol amp Walther 1990

Ganor et al 1994

-

12 21e-15 98e-16

Muscovite via K

2 29e-16 Oelkers et al 2008

Palandri amp Kharaka 2004

-

12 312e-16 21e-16

111

the core depending upon the dimensions and pore volume of the core sample (Tables 321 amp 322

Chapter 3 Section 32) The fluid residence time of several hours was distinctively longer than in

Experiment 7 where the fluid residence time was lt1 hour Consequently ion concentrations in the

outflow of Experiment 4 to 6 were significantly higher than in Experiment 7

During the injection of a NaOH fluid with a pH of 12 and at the rate of 003mLmin the

major dissolved cations found in the fluid samples were potassium aluminium and silica in

Experiment 4 (Stage 1 amp 2) and Experiment 6b The potassium aluminium and silica trend in

Experiment 4 Stage 1 had not reached steady state (Figure 431 Chapter 4) Therefore Stage 1

results are not considered for effective surface area calculations The steady state concentrations

of potassium aluminium and silica during pH 12 injection in low flow rate (Experiments 4 and

6b) are reported in Table 52

The Catherine Sandstone cores contain three major minerals according to XRD analysis

quartz kaolinite and muscovite (Table 25 Chapter 2) Considering the chemical formula of the

respective minerals in the core the source of dissolved potassium in the outflow fluid samples

(Figure 432 Chapter 4 and Table 52) is derived from muscovite alone The steady state dissolved

potassium concentration is constant in Experiment 4 (Stage 2a and 2c) but dropped from 2 to

045mgL in Experiment 6b (Table 52) In contrast the dissolved aluminium concentration is

5mgL higher in Experiment 6b than in Experiment 4 (Table 52) while the dissolved silica

concentration is similar in the two experiments (~48mgL) Two different core samples with

different pore volumes and different fluid residence times were used in Experiment 4 and 6b (Table

321 Chapter 3) The lower concentration of potassium in Experiment 6b compared to Experiment

4 can be explained by the shorter fluid residence time The other reason for the differences in

dissolved potassium and aluminium concentration in the outflow samples could possibly relate to

differences in the mineral abundances in samples F1-3a and F1-3b (Baraka-Lokmane et al 2009)

The XRD results of sample F1-3 (Table 25 Chapter 2) only represents a small fraction of the core

and variations in mineral abundances may be possible

The steady state concentrations of dissolved potassium aluminium and silica given in

Table 52 can be used to estimate the effective surface area of muscovite kaolinite and quartz

according to the sequence of calculations presented at the beginning of this chapter The estimated

effective surface area of muscovite using the dissolved K concentrations of Experiment 4 (Stage

112

2) and 6b is in the range of 04 to 175 m2g (Table 53) The estimated effective surface area of

muscovite using pH 12 data is within the range of muscovite surface areas reported in the literature

(Table 53 Black et al 2015 Beckingham et al 2016 2017)

In order to estimate the effective surface area of kaolinite the total aluminium in the

outflow fluid samples associated with kaolinite has to be determined The molar ratio of aluminium

to potassium (Al K) in muscovite is 07 27 based on its stoichiometry derived from the micro

probe analysis (see Chapter 2 Section 255) The aluminium associated with kaolinite from the

total dissolved aluminium concentrations in Experiment 4 (Stage 2) and 6b (Table 52) are 27 and

32mgL respectively using Eq 54 Consequently the estimated effective surface area of kaolinite

at pH 12 using Eq52 is in the range of 2 to 24m2g which falls into the lower range of effective

surface area values reported for kaolinite in the literature (Table 53)

After accounting for the fraction of dissolved silica mobilised by the dissolution of

muscovite and kaolinite the remaining dissolved silica concentration is attributed to quartz

dissolution The respective concentrations are 14 and 6mgL according to Eq 55 The effective

surface area of quartz based on experiments using an injection fluid with a pH of 12 is in the range

of 00002 to 00006 m2g This is an order of magnitude lower than the smallest values of quartz

surface areas reported in the literature using BET and other methods (Black et al 2015 Lai et al

2015 Beckingham et al 2016 2017) (Table 53) One reason for the above observation could be

a high degree of amalgamation between quartz grain boundaries in consolidated rock which is

consistently under estimated in unconsolidated sediments The actual percentage of reactive quartz

mineral surface area could be very small relative to the high abundance of this mineral as pointed

out earlier (Beckingham 2017 Beckingham et al 2017)

The effective surface area of minerals in Catherine Sandstone core derived from pH 12

core flood experiments can be compared to the mineral effective surface areas derived by acid

injection experiments (Experiment 4 (Stage 3) 5 and 6a) A solution of HCl with a pH of 2 was

used in the acid injection experiments Total dissolved concentrations of potassium aluminium

and silica at steady state is reported in Table 53 Steady state dissolved cations trends in the fluid

samples at pH 2 are plotted in Figure 433 and 4110 The concentration of dissolved aluminium

is 4 to 8mgL higher than alkaline injection data (Table 52) The difference in aluminium

concentration can be explained by Figure 435 (Section 41 Chapter 4) Aluminium bearing

113

minerals are undersaturated and far-from-equilibrium at pH 2 as compared to neutral and alkaline

conditions Using the total dissolved potassium concentration of 5 3 and 15mgL in Eq 52 leads

to an estimated effective surface area of muscovite in range of 1 to 43 m2g (Table 53) The

effective surface area of muscovite under both acidic and alkaline conditions are within the same

order of magnitude and within a similar range reported in the literature (Table 53) After

accounting for the total aluminium released by muscovite based on its stoichiometry the remaining

aluminium (22 to 24mgL) is assumed to be mobilised by the dissolution of kaolinite as expressed

in Eq 54 The effective surface area of kaolinite is then estimated by using data from Experiment

4 (Stage 3) 5 and 6a in Eq 53 The calculated effective surface areas derived for kaolinite under

acidic conditions are 11 8 and 77 m2g respectively (Table 53) These values are in the upper

range of literature values reported in Table 53 and compare well to kaolinite effective surface area

calculated from core flood experiments carried out under alkaline conditions (Table 53)

The silica concentration trend in Experiment 4 (Stage 3) did not reach steady state until the

end therefore the quartz surface area will be overestimated using silica concentration in Stage 3

of Experiment 4 Furthermore quartz is oversaturated in the acidic regime as suggested by the

speciation modelling using (Figure 435) Thus the dissolution of quartz in the acidic regime is

not entirely kinetically controlled and therefore the effective surface area of quartz at pH 2 cannot

be estimated

114

Table 52 Average steady state concentrations of total dissolved cations in the low flow ratelong

residence time experiments used in Eq 52 amp 53 to calculate effective surface areas

Experiment Flow rate

(mLmin)

pH Si (mgL) Al (mgL) K (mgL)

4 (Stage 2a) 003 12 49 29 2

4 (Stage 2c) 003 12 49 29 2

4 (stage 3) 003 2 71 37 5

5 003 2 40 33 3

6a 003 2 44 28 15

6b 003 12 48 34 045

Table 53 Effective surface area calculated using Eq 53 and range of specific surface area

from the literature measured by BET and geometric analysis (Beckingham et al 2016 Black et

al 2015)

115

512 Core Flood Experiments with High Flow Rate

The effective surface areas (ESA) of muscovite kaolinite and quartz were estimated

separately in an experiment using higher flow rates and consequently shorter residence times (lt 1

hour) (Experiments 7 Figures 4118 to 4121 Chapter 4) Variable flow rates were used in earlier

experiments in order to observe the effect on steady state cation concentrations in the outflow

Higher flow rates (025 to 2mLmin) were used to ensure that minerals in the core remained

undersaturated and far-from-equilibrium under both alkaline and acidic conditions (Figures 4119

to 4121 Chapter 4 Section 41) The steady state concentrations of dissolved potassium

aluminium and silica at the outflow during Experiment 7 is reported in Table 53

The effective surface area (ESA) of minerals in core F2-2b under alkaline conditions can

be determined from the steady state cation concentrations in Experiment 7a (Figure 432 Chapter

4) Three different flow rates were used corresponding to short fluid residence times of 28 14 and

7 minutes in the core The steady state cation concentrations responded linearly with changes in

the fluid residence time (Figure 432 Chapter 4) Using the steady state concentration of

potassium at these flow rates (Figure 432 Chapter 4 and Table 54) the average effective surface

area of muscovite at pH 12 is calculated as 121 m2g using Eq 52 and 53 The measured effective

surface area of muscovite at short residence times is within the same order of magnitude as

Experiment 4 under alkaline conditions (ESA = 175 m2g Table 53) However comparing the

measured effective surface area to the BET-N2 measured surface areas from literature (Black et

al 2015 Beckingham et al 2016) it exceeds the highest reported values of muscovite surface

areas (Table 55) After accounting for the total dissolved aluminium from muscovite using the Al

K ratio the remaining aluminium (8 mgL) is assigned to kaolinite dissolution and can be used

with Eq 52 and 53 to determine an effective surface area value of 164 m2g for kaolinite This

value is of the same order of magnitude as Experiment 4 and 6b under alkaline conditions and

similar to the range reported in the literature (Tables 53 and 55) The effective surface area of

quartz estimated using remaining dissolved silica in the outflow (81mgL) using Eq 55 is 00064

m2g The measured effective surface area of quartz falls into the lower range of surface area values

for quartz cited in the literature (Table 55) but an order of magnitude higher than surface area

values of quartz reported in Table 53 A detailed discussion on the above observations is stated in

later Section 513

116

The same core F2-2b was flooded with a pH 2 HCl solution in Experiment 7b with a range

of fluid residence times (lt 1 hour) in the core The resulting steady state concentrations of

dissolved potassium aluminium and silica are reported in Table 54 The dissolved cations

concentration decreased significantly compared to the previous experiment under alkaline

conditions indicating lower reactivity of siliciclastic minerals at pH 2 condition The muscovite

effective surface area estimated from Eq 53 is 71 m2g which is within same order of magnitude

as effective surface area of muscovite in Experiment 7a (Table 55) The total dissolved aluminium

associated with kaolinite dissolution is 15mgL estimated in the same way using Eq 54 The

effective surface area of kaolinite at a pH of 2 with short residence time is 143 m2g which is

comparable to the results of Experiment 7a (Table 55) Finally the quartz surface area using

Experiment 7b data is estimated using the remaining silica concentration of 03mgL An effective

surface area for quartz of 03 m2g is calculated which is two orders of magnitude higher than the

quartz effective surface area estimate from Experiment 7a at pH 12 (Table 55) However it is still

within the higher range of effective surface area values reported in the literature (Black et al 2015

Beckingham et al 2016) (Table 55)

Table 54 Average steady state concentrations of total dissolved cations in high flow rateshort

residence time experiments used in Eq 52 and 53 to calculate effective surface areas


(mLmin)


7a

05

12

2165 95 05

1 11 59 025

2 76 385 0125

7b

025

2

79 64 07

05 395 32 035

1 2 165 025

117

Table 55 The average effective surface area calculated using Eq 53 and data from experiments

7a and 7b The range of reported surface areas from the literature are also tabulated (Beckingham

et al 2016 Black et al 2015)

513 Mineral Dissolution Near- and Far-from-Equilibrium

The effective surface area of minerals calculated by Eq 53 accounts for the following

three mineral and rock properties lsquoDrrsquo is the dissolution rate constant for quartzkaolinitemica in

molecm2middotsec at given temperature and pH conditions (Tab 51) lsquomirsquo is the dissolved

silicaaluminiumpotassium mass per sample pore volume and lsquoRtrsquo is the residence time of injected

fluid in the rock which is represented by lsquoRtrsquo (Eq 53) In order to make the effective surface area

estimates using Eq 53 the dissolution of respective mineral must be kinetically controlled and

no secondary precipitation may occur (Table 322 Chapter 3) Moreover the reacting minerals

should be highly undersaturated (QK lt -05) which is a condition of the transition-state-theory

The mineral saturation indices modelled using GWB are plotted and discussed in the results section

(see Chapter 4 Section 41) If the residence time of injected fluid in the core is reduced by half

the dissolved concentrations of respective cations in the outflow fluid samples should get lowered

by an equal proportion in case of kinetically controlled dissolution The fluid residence time versus

silica concentration for Experiment 6b is plotted in Figure 511 and shows a nonlinear trend which

conflicts with the theory described above for a kinetically controlled dissolution regime (Figure

511)

118

Figure 511 Residence time vs outflow silica concentration because at variable injection rates

Figure 512 Residence time vs outflow aluminium concentration because of variable injection

rates

0

10

20

30

40

50

60

70

0 200 400 600 800

Silic

a (m

gl)

Residence Time (min)

(Experiment 6b_Si)

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800

Alu

min

ium

(m

gl)


(Experiment 6b_Aluminum)

119

The aluminium trend as a function of residence time (Figure 512) behaves similarly to

silica (Figure 511) With each variation in the residence time the dissolved aluminium

concentration dropped disproportionately (Figure 512) Furthermore the aluminium bearing

mineral boehmite is oversaturated in Experiment 6b according to the speciation modelling (Figure

472 Section 47 Chapter 4) Aluminium precipitation may be the reason for the observed

aluminium trend in Figure 512 Thus the effective surface area of kaolinite and quartz calculated

by using data under low injection rates or longer residence time is not reliable

Experiment 7a and 7b were operated at high injection rates in order to observe the

dissolution of quartz kaolinite and muscovite under highly undersaturated conditions where

mineral dissolution is kinetically controlled and no secondary precipitation is expected The

speciation modelling results for Experiment 7 are plotted in Section 41 Chapter 4 (Figures 4119

and 21) At the applied injection rates the silica aluminium and potassium bearing common rock

forming minerals are undersaturated and far-from-equilibrium under both acidic and alkali

conditions (Figures 4119 and 21) Figures 513 to 518 show steady state cation concentrations

versus fluid residence time acquired in experiments using alkaline and acid injection fluids during

Experiment 7a and 7b

Figure 513 Residence time vs outflow aluminium concentration at pH 12 (Exp 7a)

0

2

4

6

8

10

12

0 10 20 30 40

Alu

min

ium

(m

gl)


(Experiment 7a_Aluminium)

120

The dissolved aluminium silica and potassium outflow concentrations resulting from pH

12 injection at three different residence times are plotted in Figures 513 514 and 515 Unlike

in Figures 511 and 512 both aluminium and silica concentrations increased linearly with an

increase in the fluid residence time The effective surface area of quartz kaolinite and muscovite

can therefore be reliably estimated using the dissolved silica aluminium and potassium outflow

concentrations under pH 12 conditions (Figures 513 514 and 515)

The data acquired from acid flooding (pH 2) at high injection rates and short residence

times in Experiment 7b is plotted in Figures 516 517 and 518 Silica and aluminium

concentrations correlate linearly with fluid residence time (Figures 516 and 517) as expected

given silica and aluminium bearing minerals are highly undersaturated (Section 41 Chapter 4)

For comparison estimating the quartz effective surface area under the acidic conditions and longer

fluid residence time was unreliable because of quartz and chalcedony saturation in the fluid

(Section 41 Figure 435)

Figure 515 shows a linear correlation between dissolved potassium and the fluid residence

time for Experiment 7a suggesting the dissolution of muscovite is kinetically controlled

Consequently the results can be used to estimate the effective surface area of muscovite

Figure 514 Residence time vs outflow silica concentration at a pH of 12

0

5

10

15

20

25

0 10 20 30 40

Silic

a (m

gl)


(Experiment 7a_Silica)

121

Figure 515 Residence time vs outflow potassium concentration at a pH of 12

Figure 516 Residence time vs outflow aluminium concentration at a pH of 2

0

01

02

03

04

05

06

0 10 20 30 40

Pota

ssiu

m (

mg

l)


(Experiment 7a_Potassium)

005

115

225

335

445

5

0 20 40 60 80

Alu

min

ium

(m

gl)



122



0

2

4

6

8

10

12

0 20 40 60 80

Sili

ca (m

gl)


(Experiment 7b_Silica)

0

01

02

03

04

05

06

07

08

0 20 40 60 80

Pota

ssiu

m (

mg

l)


(Experiment 7b_Potassium)

123

514 Error Analysis

The effective surface areas of muscovite kaolinite and quartz were estimated based on

steady state outflow concentration observed in Experiment 6 (Table 53) and Experiment 7 (Table

55) It is demonstrated that the estimated effective mineral surface areas are higher in experiments

with a shorter fluid residence time The following sub-sections will discuss potential errors of these

results

5141 Quartz Surface Area

The steady state dissolved silica concentrations do not correlate linearly with residence

times greater than 1 hour (Figure 511) At short residence times of less than 30 minutes (Figure

514) a linear response is observed corresponding to the kinetically controlled regime at pH 12

Thus the effective surface area of quartz may have been underestimated using Experiment 4 and

6 data (Table 53) Silica bearing minerals under acidic conditions in experiments 4 5 and 6a were

oversaturated as illustrated in the GWB speciation model (Figure 435 Section 43) Therefore

the surface area of quartz in the acidic regime is not reported in Table 53 However in contrast

with previous experiments in the acidic regime the GWB speciation of experiment 7b (Figure

4121 Chapter 4 Section 41) illustrates that silica bearing minerals are undersaturated

Consequently silica is unlikely to precipitate at short fluid residence times keeping quartz

dissolution purely controlled by kinetics at pH 2 Hence the effective surface area of quartz at pH

2 was estimated using experiment 7b data (Figure 481 Table 55) A several orders of magnitude

discrepancy between the estimated Sa of quartz under acidic and alkaline conditions can be seen

in Table 54 The quartz dissolution rate at pH 2 reported in literatures (Knauss amp Wolery 1987

Palandri amp Kharaka 2004) is 3 orders of magnitude lower than at pH 12 (Table 51) The total

silica concentration assigned to quartz in Experiment 7b using Eq 55 is overestimated considering

the slow dissolution kinetics of quartz at pH 2 One of the possible sources of additional silica

could be from the 15wt of amorphous material reported in the XRD analysis of core F2-1 (Table

25 Chapter 2 Section 25) The total silica concentration in Experiment 7b is considerably low

(2-10mgL) at given injection rates After accounting for silica release from muscovite and

kaolinite the remaining silica is as low as 03mgL Thus a small influx of silica from an unknown

source can cause broad discrepancies in the final effective surface area value of quartz This leads

to a large uncertainty in the effective surface area of quartz at low pH Furthermore it is also

124

possible that some uncertainty in the final silica concentration assigned to quartz has propagated

through the steps described previously in section 51 (Eq 54 amp 55)

The stoichiometry of kaolinite and muscovite in the core is estimated through the micro

probe analysis on the thin sections described in Chapter 2 section 255 The analysis was done on

multiple points of each mineral giving cation weight percentages within a certain amount of error

(Table 27 Chapter 2) The final moles of silica aluminium and potassium that are assigned to

kaolinite and muscovite in the core are within error of +- 002 012 and 015 respectively The

effect of stoichiometric error (microprobe analysis) on the final dissolved silica concentration

assigned to quartz by Eq 55 at pH 2 and 12 is illustrated in Figure 519 The red and blue marker

represent the average steady state dissolved silica concentration at pH 2 and 12 respectively used

for quartz surface area calculations in Table 54 The error bar represents the maximum upper and

lower extremities of silica concentration that is possible within two standard deviations (Table 27

Chapter 2) The relative error in dissolved silica at pH 2 is very large (+- 04 at an absolute

concentration of 04mgL Figure 519) that is caused by minute variation in muscovite and

kaolinite stoichiometry On the other hand the relative error in silica concentration at pH 12 is

very small (+- 04 at an absolute concentration of 28mgL Figure 519) The resultant effective

surface area of quartz from silica concentration in Figure 519 and the possible errors are plotted

in Figure 5110 The estimated error in quartz effective surface area at pH 2 is more than two

orders of magnitude In contrary the error in the effective surface area pH 12 only varies by a

factor of 3 ie it is within the same order of magnitude (Figure 5110) Hence the effective surface

area of quartz at pH 12 proved to have a much lower error that at pH 2

125

Figure 519 Total silica assigned to quartz at pH 2 and 12 after accounting for error in the

stoichiometry of muscovite and kaolinite

Figure 5110 Error in the final value of quartz effective surface area at pH 2 and 12 after

accounting for the error in the stoichiometry of muscovite and kaolinite

0

05

1

15

2

25

3

35

-01

0

01

02

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14

Si a

t pH

12

(mg

l)

Si a

t pH

2 (

mg

l)

pH

Si Assigned to Quartz

0

0002

0004

0006

0008

001

0001

001

01

1

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Quartz

126

5142 Kaolinite Surface Area

Kaolinite surface area under acidic and alkali conditions reported in Table 53 overlook the

possibility of aluminium precipitation at longer residence time as illustrated in Figure 472

(Chapter 4 Section 41) This resulted in lower effective surface area values as shown in Table 53

as compared to kaolinite surface areas reported in Table 54 However the calculated kaolinite

surface area remains within the same order of magnitude regardless of whether secondary

precipitation was taken into account

There is approximately 15 of uncharacterized material in the core F2-1 according to XRD

results (Table 25 Chapter 2 Section 25) A sensitivity analysis was carried out to determine the

effect of error in the XRD analysis on surface area results (Figure 5111) The total weight percent

of kaolinite reported in the XRD analysis was varied between 5 and 10 in order to see the effect

on the effective surface area of kaolinite Figure 5112 compares the aluminium concentration

assigned to kaolinite at pH 2 and 12 after accounting for the stoichiometric error (like quartz)

Figure 5113 illustrates the resultant effective surface area of kaolinite and the possible deviation

from the average value The propagated error in the calculated effective surface area of kaolinite

at pH 2 is approximately within +- 2 m2g (2σ) while at pH 12 is approx +- 6 m2g (2σ) The

errors in kaolinite effective surface areas under both acidic and alkaline conditions are within the

same order of magnitude (Figure 5113) illustrating an insignificant error introduced by the

uncharacterised phase by XRD

5143 Muscovite Surface Area

Unlike quartz and kaolinite the effective surface area of muscovite based on long and short

fluid residence time is very similar (Table 55) However effective surface area of muscovite is

slightly underestimated at pH 12 (Table 53) due to the effect of precipitation at longer fluid

residence times Due to uncharacterized amorphous material in the XRD data there may be a

possible error in the final weight percentage of muscovite reported in Table 25 (Chapter 2 Section

25) The muscovite weight percent is increased from 5 to 10 which reduces the final surface

area to 18 m2g (Figure 5111) The only possible source of potassium is muscovite considering

the XRD results in Table 25 (Chapter 2 Section 25) Therefore the muscovite effective surface

area is calculated independently using the total potassium concentration in the effluent That

127

eliminates any possibility of error propagation through the surface area calculation as in the case

for quartz and kaolinite

Figure 5111 Sensitivity analysis of final Sa values calculated from experiment 7 data lsquoXRDrsquo

represents actual weight percent reported in Table 41

Figure 5112 Total aluminium assigned to kaolinite at pH 2 and 12 after accounting for the

error in the stoichiometry of muscovite and kaolinite

0

2

4

6

8

10

12

Kaolinite Muscovite

Surf

ace

Are

a (m

2 g)

Sensitivity Analysis

XRD XRD+5 XRD+10

0

1

2

3

4

5

6

7

8

9

10

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14

Al a

t pH

12

(mg

l)

Al a

t pH

2 (

mg

l)

pH

Al Assign to Kaolinite

128

Figure 5113 Error propagation in the final value of kaolinite effective surface area at pH 2

and 12 after accounting for the error in the stoichiometry of muscovite and kaolinite

52 Determining the Intrinsic Porosity-Permeability Relationship

Mineral dissolution and precipitation in porous rocks can lead to modification in its

intergranular structure causing abrupt changes in porosity and permeability To predict the degree

of permeability enhancement by mineral dissolution it is crucial to understand the complexity of

the porosity-permeability relationship for a given rock type As described in the previous chapter

on reactive transport modelling (Section 15 Chapter 1) there are many key equations found in

the literature that strive to quantify the permeability change due to modification in porosity (Taylor

1948 Michaels amp Lin 1954 Freeze amp Cherry 1977 Nelson 1994 Bear 1972 Bourbie amp Zinszner

1985 Vaughan 1987 Verma amp Pruess 1988 Tokunaga et al 1998 Dewhurst et al 1999b Pape

et al 1999 Revil amp Cathles 1999 Luijendijki amp Gleeson 2015) There are three different

relationships used in the TOUGHREACT code that can extrapolate porosity and permeability

change in the rock at laboratory and field scale (Section 142 Chapter 1) The relationship between

porosity and permeability as described by Verma amp Pruess (1988) is found to better capture the

permeability at the field scale (Xu et al 2012) In order to apply Verma and Pruess porosity-

8

10

12

14

16

18

20

22

24

8

10

12

14

16

18

20

22

24

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Kaolinite

129

permeability relationship in the reactive transport models there are two unknown site-specific

variables emptyc (critical porosity) and W(power law exponent) that must be defined for the

TOUGHREACT simulation (Section 16 Chapter 1)

Catherine Sandstone cores were chosen for the core flood experiments to dissolve the

dominant rock forming framework minerals and derive data to determine the two unknown

variables in the Verma and Pruess equation (Section 16 Chapter 1) Core plugs were planned to

be flooded by the reactive reagents such as NaOH and HCl solutions of pH 12 amp 2 respectively

which would reside in the rock for several hours The residence time of the reactive fluid in the

core was controlled by the injection rate and total pore volume of the core The injected reagent

would react with mineral grains that were clogging the interconnectivity of the pores this would

ultimately enhance the permeability of the core plug The change in differential pressure due to

increasing permeability can be used to calculate the injectivity index of the core that can be

incorporated in TOUGHREACT modelling to derive the unknown parameters of the Verma and

Pruess equation (Section 16 Chapter 1)

521 Fines Migration in High Permeability Sandstone

The core flood Experiment 2 (Section 41 Chapter 4) showed significant enhancement in

permeability enforced by higher injection rates (Figure 412 Chapter 4) The permeability in that

case was modified mechanically due to fines migration that released undissolved mineral particles

out of the core (Gabriel et al 1983 Bennion et al 1996) In a geochemical stimulation scenario

the permeability is supposed to be entirely influenced by mineral dissolution Since the mechanical

process was dominant in Figure 412 the data no longer represented permeability enhancement

by geochemical stimulation Therefore it cannot be incorporated in the reactive transport models

The TOUGHREACT models only account for permeability change as a function of mineral

dissolution or precipitation as described in Chapter 1 Also fines mobilization can cause damage

to the bore hole and reservoir eventually clogging the pore spaces in a natural system (Gabriel et

al 1983 Bennion et al 1996) Hence the mechanically induced permeability change is by no

means helpful but an important observation in conducting geochemical stimulation tests at

laboratory scale

130

Since the permeability of Catherine Sandstone cores vary substantially (Table 321

Chapter 3 Section 32) a low permeability core was used as an alternative for later experiments

522 Initial Permeability Changes when Flooding at High and Low pH

The core flood Experiment 3 (Section 42 Chapter 4) was conducted on a tight core plug

of Catherine Sandstone with permeability less than 1mD Using an injection rate as low as

003mLmin (Table 322 Section 32 Chapter 3) successfully reduces the issue of fines

mobilization allowing the experiment to be run at a constant injection rate The permeability

reached a plateau at lt01 mD after more than 2 days of injection (Figure 422 Section 42 Chapter

4) The experiment continued for 5 more days at a constant injection rate dissolving framework

minerals as indicated by the silica concentration in outflow fluid samples (Figure 421 Section

42 Chapter 4) The permeability remained constant at the lowest value until the NaOH injection

was halted The current amount of mineral dissolution was not enough to achieve the goal of

modifying core permeability in a period of 7 days A silica peak was observed (Figure 421

Section 42 Chapter 4) while the pH was gradually increasing from 10 to 11 The dissolution may

be enhanced at a specific pH below 12 Additional NaOH flooding experiments were conducted

to verify the above observation (Figure 421 Section 42 Chapter 4)

Experiment 4 aimed to maximize dissolution and flood the core for several weeks until an

increase in permeability was observed The experiment ran for approximately 6 weeks with a

constant injection rate of 003mLmin Two different types of reactive fluid (NaOH amp HCl) were

injected with varying concentrations and pH levels The sandstone core continually released

dissolved cations that were detected in the fluid samples throughout the flooding (Figures 416

417 and 418 Section 43 Chapter 4) However dissolution was insufficient to pose substantial

changes to the permeability of the core in the time frame of more than a month A sudden decrease

in the permeability after 10 days of injection was observed in (Figure 434 Section 43 Chapter

4) that appeared a few days after increasing the pH of the injection fluid This small variation in

permeability may not be associated with framework mineral dissolution or precipitation It may be

the consequence of fines that may release due to the interaction of the highly alkali fluid with the

unconsolidated clay minerals of the Catherine Sandstone core (Valdya amp Fogler 1992) There was

no permeability reduction observed during the injection of pH 11 fluid until it varied to pH 12

(Figure 434 Section 43 Chapter 4) The permeability at the final stage 3 of the experiment (HCl

131

injection) started increasing and reached the initial permeability of the core Also the permeability

trend had not reached a plateau till the end of experiment 4 (Figure 434 Section 43 Chapter 4)

Therefore it might be possible that the permeability enhancement would continue further Unlike

alkali injection there was no permeability reduction due to fines mobilization evident in the last

stage of experiment 4 (Figure 434 Section 43 Chapter 4) Most of the fines material in the core

belongs to kaolinite as reported in the XRD analysis (Table 25 Chapter 2) During the acid

injection phase kaolinite fines that were released throughout the alkali phase might have been

dissolved causing permeability to increase gradually until it matched the initial permeability value

The current observations indicate that NaOH is unsuitable for enhancing sandstone permeability

while maintaining the rockrsquos stability After more than a month of core flooding it can be

concluded that NaOH at pH 12 does not lead to significant quartz dissolution in a sandstone core

Therefore it cannot lead to noteworthy enhancement in permeability in a limited time

Experiments 3 amp 4 failed to introduce a notable permeability reduction in the sandstone

cores under extreme alkali conditions Alkali fluid injection also caused pore clogging due to fines

mobilization The amount of dissolution at pH 12 was not effective at dissolving fines to counter

the permeability reduction due to their mobilization A pressure drop corresponding to a

permeability increase was observed in the later stage of experiment 4 that was associated with acid

injection (Figure 434 Section 43 Chapter 4) In order to observe the extent of enhanced

permeability by acid injection a fresh core of Catherine Sandstone was flooded with pH 2 fluid in

experiment 5

The core flooding in Experiment 5 started with the injection of pH 4 amp 3 fluids that were

later replaced by pH 2 fluid after 10 days of injection (Figure 441 Section 44 Chapter 4) The

permeability of the core increased from 03 to 08mD throughout the duration of experiment 5

(Figure 442 Section 44 Chapter 4) There is no absolute interpretation for the sudden increase

in the permeability of the core since there were no significant changes in the fluid composition

within the period of 6 days (Figure 441 Chapter 4) Fluid sample analysis from ICP-OES showed

a spike in cation concentration after 9 days of acid injection beginning with calcium and

magnesium and followed by silica (Figure 441 Chapter 4) Comparing to fluid chemistry the

permeability increase began three days earlier than the cation spike in the fluid samples Hence

there is not a direct correlation between outflow fluid chemistry and the permeability increase

132

The peak of Ca and Mg (Figure 441 Section 41) is most likely associated with trace carbonate

mineral that dissolved completely within the period of one week The dissolution of trace minerals

might have caused the sudden increase in the permeability (Figure 442 Chapter 4) that later

reached a plateau as the trace minerals were removed entirely from the core through dissolution

There was no observed permeability reduction during the entire period of acid injection Therefore

fines mobilization was only induced by highly alkaline fluid

A large oscillation can be observed in the permeability values after 15-20 days of

experiment 5 (Figure 442 Chapter 4) The core flooding system had two ∆P transducers with a

maximum detection limit of 8 and 500 psi respectively (See Chapter 3 Section 31) The CFS was

recording the ∆P using the 500 psi ∆P transducer until the differential pressure dropped below 8

psi (Figure 442 Chapter 4) then the system automatically switched to the higher sensitivity (8

psi) ∆P transducer Consequently a tiny variation in the pressure reading corresponded to a

significant change in the calculated permeability (Figure 442 Chapter 4) Thus the variability in

permeability at the end of experiment 5 may not be real However error in the overall permeability

increase in Figure 442 (Chapter 4) due to the sensitivity limit of the pressure transducers was

within +-002mD which is negligible Hence the permeability changes in experiment 5 was not

an artefact Therefore experiment 5 data is used later in the modelling section (Chapter 6 Section

621) to derive the unknown variables emptyc and Win Verma amp Pruess equation (Eq 114 Chapter

1)

133

CHAPTER 6


61 Core Scale Modelling

A core scale reactive transport model was built to reproduce the results generated by the

core flood dissolution Experiment 7 at pH 2 and 12 (Section 417 Chapter 4) The experimentally

derived effective surface area of minerals contained in the Catherine Sandstone core (Table 55

Chapter 5) were incorporated in the core scale reactive transport models to compare the modelled

silica and aluminium concentration trend with Experiment 7 data The core scale model results

help to validate the estimated effective surface area of major rock forming minerals in Catherine

Sandstone core (Section 51 Chapter 5) The experimentally estimated effective surface area

results will be used later in the near well formation scale models (Section 62) to demonstrate the

effectiveness and feasibility of geochemical reservoir stimulation in the Catherine Sandstone at

field scale The dimensions of the geological model and the petrophysical properties of the core

were kept the same as in core F2-2b which was used in Experiment 7 (Table 321 Section 32

Chapter 3) The reactive transport modelling code TOUGHREACT (TR) version 12 (described

in Section 142 Chapter 1) was used to perform the simulations The equation of state used in the

core scale reactive transport model was EOS1 of TOUGHREACT EOS1 is capable of modelling

single phase two water problems at high temperatures and pressures representing deep reservoir

conditions (Xu et al 2004)

611 Comparison of Experiment 7b to Model Results at pH 2

The model versus Experiment 7b trends in dissolved silica and aluminium at pH 2 is

illustrated in Figure 611 and 612 The simulation ran for 22 hours at a constant injection rate of

025mLmin The steady state silica concentration at the outflow reached 89mgL after 14 hours

of pH 2 injection which is a close match to the steady state silica concentration of 92mgL during

pH 2 injection in Experiment 7b (Figure 611) There was an initial spike in the dissolved silica

in the experiment observed after 3 hours of pH 2 injection which was not visible in the modelled

silica trend The silica spike might be the result of highly reactive amorphous phases of silica

attached to the grains of quartz crystals This might resulted in an initial incongruent dissolution

134

before reaching a steady state as reported in the literature (Marini 2007 Aradottir et al 2013

Beckingham et al 2016) The final silica concentrations used to estimate the effective surface area

of silicate minerals were taken once the silica reached a steady state (Section 51 Chapter 5)

Therefore matching the experimental silica peak with the modelling results is not required for our

purposes However the trend of modelled aluminium concentration at pH 2 differed significantly

from the Experiment7b aluminium concentration trend (Figure 612) The dissolved aluminium at

the outflow in Experiment 7b is suppressed during the initial 14 hours of pH 2 injections after

which it spiked and reached a steady state within 20 hours (Figure 612) The delay in the

experimental aluminium trend can be explained by the buffering of the pH just below 5 due to the

dissolution of carbonate minerals in the core as explained in Section 444 (Chapter 4) The

buffering of the pH due to carbonate dissolution is well represented by the modelled pH trend in

Figure 612 However the dissolved aluminium concentration in the model continued to increase

gradually even at pH levels close to 5 The increasing aluminium concentration can be explained

by the Geochemistrsquos Work Bench (GWB) modelling results (Figure 435 Chapter 4) which show

that aluminium bearing minerals are supersaturated at pH 5 The aluminium bearing minerals

started dissolving as soon as the pH became more acidic (Figure 612) There was approximately

a 2mgL difference between the total dissolved aluminium in the model versus that observed in

Experiment 7b once it reached a steady state (Figure 612) The difference could be the outcome

of the thermodynamic database used in TOUGHREACT (Wolery 1992) which consisted of

higher saturation constants for aluminium bearing minerals than thermocomV8R6+tdat as

explained by Pokrovskii amp Helgeson (1995) for gibbsite (See Section 46 Chapter 4) As shown

by previous speciation modelling for Experiment 6b (Figures 462 and 463 Chapter 4) the

thermodynamic database thermocomV8R6+tdat better explains the current experimental results

than thermotdat (Wolery 1992) The lower solubility constants for aluminium bearing minerals

in thermocomV8R6+tdat will give a better fit to the modelled steady state concentration of

aluminium in Experiment 7b shown in Figure 612

135

Figure 611 Dissolved silica trend of TR modelling versus Experiment 7b pH 2 injection

Figure 612 Dissolved aluminium trend of TR modelling versus Experiment 7b pH 2 injection

0

5

10

15

20

25

0 2 4 6 8 10 12 14 16 18 20 22 24

silic

a (m

gl)

Time (Hours)

Exp 7b vs TR model_pH 2

Model_Si Exp_Si

012345678910

0

1

2

3

4

5

6

7

0 5 10 15 20 25

pH

alum

inum

(m

gl)

Time (Hours)


Model_Al Exp_Al pH_Model

136

612 Comparison of Experiment 7a to Model Results at pH 12

A second core scale reactive transport simulation was run using the same geological model

and experimental conditions as in Figure 611 and 612 by simulating the injection of a NaOH

solution of pH 12 The simulation was run for 75 hours at a constant injection rate of 05mLmin

The steady state silica concentration at the outflow reached 258mgL after approximately 30

minutes (Figure 613) which was comparable to the steady state silica concentration of 24mgL

in Experiment 7a There was an initial spike in experimental silica at 2 hours after the pH 12

injection (Figure 613) which was similar to the silica spike in Figure 611 The silica spike can

be explained by the initial incongruent dissolution of amorphous material in the core as explained

in Section 612 The modelled aluminium trend due to pH 12 injection slightly differed from the

Experiment 7a (Figure 614) However the experimental pH trend matched with the modelled

aluminium trend The aluminium was supressed at pH 115 in Experiment 7a whereas the model

showed a spike in aluminium concentrations while the pH was in transition from 11 to 12 (Figure

614) The steady state aluminium concentration in the model was 4mgL higher than the

Experimental 7a result (Figure 614) The early aluminium spike in the model and higher steady

state concentration can be explained by the different thermodynamic databases used in

TOUGHREACT compared to GWB modelling (Section 611)

Figure 613 Dissolved silica concentration in TOUGHREACT modelling versus Experiment 7a

(pH 12 injection)

0

10

20

30

40

50

0 2 4 6 8

silic

a (m

gl)

Time (Hours)

Exp 7a vs TR model_pH 12

Exp_Si Model_Si

137

Figure 614 Dissolved aluminium trend of TR modelling versus Experiment 7a pH 12

injection


The effective surface area of major minerals contained in the Catherine Sandstone core

(from XRD analysis Table 25 Chapter 2) were successfully estimated using the empirical

relationships derived in Chapter 5 (Section 51) The fluid chemistry analysed by ICP-OES (Table

43 Chapter 4) during core dissolution experiments was used to determine the effective surface

area of muscovite (using K) kaolinite (using Al) and quartz (using Si) using Equations 51 to 55

(Section 51 Chapter 5) Furthermore there was a consistent trend of calcium and magnesium

reported in all the acid injection experiments (Experiments 5 6a and 7b Chapter 4) which

appeared for a limited time during the injection of the pH 2 fluid The calcium and magnesium

trends corresponded to none of the three major minerals reported in the XRD analysis or the thin

section studies (Sections 251 and 254 Chapter 2) Since the calcium and magnesium peak only

showed up when the injection fluid was acidic they likely represent carbonate minerals (calcite

7

8

9

10

11

12

13

0

2

4

6

8

10

12

14

16

0 2 4 6 8

pH

alum

inum

(m

gl)

Time (Hours)


Exp_Al Model_Al pH_Exp

138

and dolomite) that dissolved during pH 2 flooding However the trace amount of carbonate was

flushed out completely within 6 days of pH 2 injections (Figures 4112 and 4119 Section 41

Chapter 4) Since these trace minerals could not be detected by XRD or thin section microscopy

it was impossible to account for their volume fraction and effective surface area by common

mineral analysis

A simple mass balance approach was applied to estimate the mass of calcite and dolomite

in the Catherine Sandstone (Sample F1-3) using the concentration of magnesium and calcium in

the fluid sample (Figure 441 Chapter 4) The estimated total weight percent of calcite and

dolomite together with other framework minerals in the core F1-3 reported in XRD analysis

(Chapter 2 Table 25) were added in the core scale reactive transport model The aim was to

characterize the effective surface area of trace carbonates by matching the experimental calcium

and magnesium trends during injection of pH 22 fluid in Experiment 5 (Figure 441 Chapter 4)

with the model results The reactive transport modelling code TOUGHREACT version 12

(Section 142 Chapter 1) was used for the simulations

6131 Core Scale Model versus Experiment 5

A core scale two-dimensional (1D) geological model was constructed using the graphical

user interface PetraSim (Section 141 Chapter 1) The dimensions of the geological model were

kept the same as for the core F1-3b1 used in Experiment 5 (Table 321 Chapter 3) The weight

percent of minerals were taken from Table 25 (Section 251 Chapter 2) The core was flooded

with HCl of pH 22 at an injection rate of 003mLmin for a modelled period of 6 days The total

modelling period corresponded to the time duration of pH 22 injection in Experiment 5 (Figure

441 Chapter 4) until the concentration of dissolved calcium and magnesium dropped to less than

1mgL The effective surface area of calcite and dolomite entered in the model was varied in

iterations until a good match of the dissolved calcium and magnesium changes between the model

and the Experiment 5 results (Figures 615-618) were observed Figure 615 illustrates the

dissolved calcium concentration and the trend from the TOUGHREACT (TR) model versus the

Experiment 5 core flood Although dolomite contains both calcium and magnesium ions (Ca

Mg(CO3)2) it alone was insufficient input to match the initial peak of 125mgL calcium reported

in the CFS experiment results (Figure 615) Since the calcite dissolution rate is significantly

higher than dolomite (Palandri amp Kharaka 2004) adding a small amount of calcite in the model

139

(Table 611) was necessary to match the calcium peak in experimental results (Figure 615) The

effective surface area of calcite and dolomite that lead to a good match between the model and

the experimental results was 500 and 4000 cm2g respectively (Table 611) The predicted

effective surface area of calcite was in the lower range of values reported in the literature while

dolomite fell within the higher range (Palandri and Kharaka 2004 Pokrovsky et al 2009 Black

et al 2015 Beckingham et al 2016) When examining the magnesium trends dolomite as a lone

source for magnesium in the model was not enough to correspond closely with the experimental

magnesium trend with a peak concentration of 90mgL Therefore an additional magnesium

bearing carbonate mineral (magnesite) was included in the reactive transport model to improve the

match between the model output and magnesium trend generated in Experiment 5 (Figure 616)

Keeping the estimated effective surface area and amounts of calcite and dolomite constant (Table

611) more than 10 simulations were performed with variable amounts and effective surface area

of magnesite to fit the experimental magnesium trend The two best possible fits between model

and experimental magnesium trend are illustrated in Figures 616 and 618 The effective surface

area and volume fraction of calcite and dolomite were kept constant in both simulations (Figure

615) In the first simulation the magnesite effective surface area of 500 cm2g and weight percent

of 015 was used (Table 611 Figures 615 and 616) Figures 617 and 618 shows the modelled

calcium and magnesium trends respectively while the effective surface area and weight percent

of magnesite was increased to 600 cm2g and 018 respectively Dissolved calcium remained

unaffected by the addition of magnesite (MgCO3) which contained no calcium In both cases the

modelled calcium trend matched the experimental data (Figures 615 and 617) Figures 616 and

618 shows the two best possible fits for magnesium using TOUGHREACT modelling with the

parameters reported in Table 611 There remained a possibility of an unknown magnesium

bearing carbonate mineral such as brucite contributing in the resultant magnesium concentration

in Experiment 5 But due to the limitation of mineral database in TOUGHREACT it could not be

included in the models

140

Table 611 The predicted effective surface areas used in the core scale reactive transport model

The weight percentage of carbonates used in the model are estimated from Experiment 5 data

using a mass balance approach

Figure 615 Experimental versus modelled calcium trend using Experiment 5 data The predicted

input parameters used for the calcite dolomite and magnesite effective surface area are 500 4000

and 500 cm2g The weight percentages are 0025 005 and 0150 for calcite dolomite and

magnesite respectively

0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)

Experiment 5 vs TR Model

TR_Ca (ppm) Exp_Ca (ppm)

TOUGHREACT Modelling Parameters

Effective surface area (cm2g)

Weight Percent ()

Calcite 500 0025

Dolomite 4000 0050

Magnesite

500 0150

600 0180

141

Figure 616 Experimental versus modelled magnesium trend using Experiment 5 data The

predicted input parameters used for calcite dolomite and magnesite effective surface area are

500 4000 and 500 cm2g The weight percentages are 0025 005 and 0150 for calcite dolomite

and magnesite respectively


input parameters used for calcite dolomite and magnesite effective surface area are 500 4000



0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)


TR Mg(ppm) Exp_Mg (ppm)

0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)



142





62 Near Well Formation Scale Modelling

621 Background and Motivation

The experimentally derived effective surface area of minerals contained in the Catherine

Sandstone core (Section 51 Chapter 5) were incorporated in the near well formation scale reactive

transport models presented in the following sections The motive was to assess the effectiveness

of geochemical stimulation for enhancing the permeability of a quartz dominated sandstone at field

scale using experimentally derived parameters for that sandstone The reactive transport modelling

code TOUGHREACT version 12 (described in Section 142 Chapter 1) was used to perform the

simulations The equation of state used in the geochemical reservoir stimulation model was EOS1

of TOUGHREACT EOS1 is capable of modelling single phase two water problems at high

temperatures and pressures representing deep reservoir conditions (Xu et al 2004) The model

calculated the change in porosity of the rock using a mass balance approach by accounting for the

change in mineral volume fraction due to dissolution (Section 142 Chapter 1) The Kozeny-

Carman relationship between porosity and permeability (Eq 113 Chapter 1) was used in the

0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



143

current models to derive the final permeability of the medium given by the change in porosity in

the model (Section 142 Chapter 1) The reactive transport models would also help to evaluate

the feasibility of geochemical reservoir stimulation at field scale by simulating CO2 injection

scenarios before and after geochemical stimulation The CO2 injection models were simulated by

using the equation of state ldquoECO2Nrdquo that is used for solving problems related to multiphase

mixtures of CO2 and water (Xu et al 2004)

622 Model Setup

The geological model was built using PetraSim mimicking the reservoir conditions of the

Catherine Sandstone as described in Chapter 2 with input from Garnett et al 2013 The reservoir

is represented as radial model with radius of 1000 metres a thickness of 7 metres (Figure 621)

The porosity of the Catherine Sandstone unit was set to 17 with a uniform vertical and horizontal

permeability of ~32 millidarcy (Table 621) as stated in a report on the ZeroGen project by Garnett

et al (2013) The sandstone consists of more than 70 quartz with additional silicate minerals

(Table 622) A one-dimensional radial flow gridding was used consisting of 100 radial blocks

(Section 141 Chapter 1) The grids extended laterally with logarithmic increasing radii out to the

complete length of the reservoir from the wall of the injection well This provided a dense gridding

near the injection point allowing to closely monitor the geochemical affects within the immediate

vicinity of the wellbore The initial and boundary conditions were applied using the mineralogical

characterisation of Catherine Sandstone core logs from a depth of approx 900 metres (Garnett et

al 2013)

623 Reaction Kinetics


stated below in Equation 61 (Lasaga et al 1994)


where n denotes a mineral index positive values of rn indicate dissolution and negative values of

precipitation amp$lowast is the rate constant (moles per unit mineral surface area and unit time) which is

temperature-dependent An is the reactive surface area of the mineral per kg of H2O and Ω$is the

kinetic mineral saturation ratio (Xu et al 2004) An is calculated by the combination of user input

144

volume fraction of the minerals and their respective reactive surface areas by Eq 65 For many

minerals the rate constant k can be calculated using three mechanisms relating to different pH

regimes (Lasaga et al 1994 Palandri and Kharaka 2004)


88+=] (62)

amplowast = amp+exp[1236 789 minus

88+=]A

$ (63)

amplowast = amp+Bexp[123C6 789 minus

88+=]AB

$C (64)


respectively Ea is the activation energy in kJmol for each mineral in the geological model reported

in Table 623 amp+ is the rate constant in molm2middots at 25oC reported for each acid base and neutral

mechanisms in Table 623 R is the gas constant in kJmol K T is absolute temperature in Kelvin

a is the activity of the subscripted species and ni is an exponent constant (Table 623)

624 Reactive Surface Area

In TOUGHREACT the surface area of the minerals to be used in the above equation (Eq

61) is calculated by the general relationship


Where the values An represents the reactive surface area of the minerals in unit of m2mineralkgwater

Am is the effective surface area of the individual minerals estimated from Eq 53 (Section 51

Chapter 5) with input from the user in m2g reported in Table 623 The core samples of Catherine

Sandstone only contained quartz and clay minerals due to the weathering of feldspars Therefore

the effective surface area of the feldspars reported in Garnett et al (2013) (Table 622) is assumed

to be the same as the quartz (Table 623) Vfrac is the volume fraction of the minerals already

present in the model in units of m3 mineralm3

solids reported in Table 622 Cw is the wetted surface

conversion factor in units of kgwaterm3medium The values of Am Vfrac and Cw vary throughout the

dynamic simulation as a result of mineral dissolution and precipitation

145


Table 621 Hydrogeological parameters for a one-dimensional radial flow model (Garnett et al

2013)

146


Table 623 Kinetic parameters for calculating mineral dissolutionprecipitation rates (Palandri

and Kharaka 2004 Xu et al 2009)

Neutral Mechanism Acid Mechanism Basic Mechanism

Minerals A

(m2 g-1)

k25

(mol m2 s-1)

Ea

(KJ mol-1)

k25 Ea n(H+) k25 Ea n(H+)

Albite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

Ankerite 0006 126e-9 6276 6457e-4 361 05 - - -

Anorthite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

K-feldspar 0006 389e-13 38 871e-11 517 05 631e-22 941 -0823

Quartz 0006 398e-14 218 - - - 513e-17 259 -05

Kaolinite 16 6918e-14 222 4898e-12 659 077 8913e-18 179 -0472

Muscovite 71 282e-14 22 14e-12 22 037 282e-15 22 -022

147

625 Grid Size Optimization

The number of grid cells and their spacing in the geological model is important to collect

a sufficient number of data points within the zone of interest (Sonnenthal et al 2005 Audigane et

al 2007 Xu et al 2007 Sengor et al 2007 Xu et al 2012) The radial geological model of

Catherine Sandstone reservoir was tested with varying grid numbers and spacing within the near

well radius by observing a tracer concentration against distance from the wellbore Bromide (Br)

was used in the following reactive transport models to track the plume penetration into the

Catherine Sandstone reservoir Bromide is often used as a conservative tracer in groundwater

recharge studies (Flint et al 2002 Aquilanti et al 2016 Wu et al 2016) Bromide was injected

as an independent tracer together with reactive fluid of low pH (acidic) and high pH (alkali) in the

reservoir model with 50 radial cells that resulted in a distorted tracer concentration curve (Figure

622) Since most of the reaction would take place near the wellbore a large number of data points

were required within the immediate vicinity of the injection point The grid spacing was optimized

by increasing the number of cells to 100 where the width of each cell increased logarithmically

moving away from the injection well This gave a much denser gridding near the wellbore The

50-radial cells model consisted of 10 grids within a 12m radius with a minimum spacing of 08m

The 100 cells model consisted of 20 grids within 12m radius with a minimum spacing of 04m

The 100 cells model with the dense gridding near the wellbore produced a more realistic s-shaped

tracer concentration curve shown in Figure 623 that is usually observed in field experiments

148

Figure 622 Bromide tracer concentration curve with 50 radial grid cells

Figure 623 Bromid tracere concentration curve with 100 radial grid cells

149

626 Reservoir Stimulation using Alkaline Reagents

6261 Constant Injection Rate and Duration

A NaOH solution of pH 12 was injected in the modelled reservoir for 20 days at a constant

injection rate of 12 kgs The maximum permeability change in the 20 days of injection was 28

mD (from 33 mD to 61 mD Figure 624) Figure 624 also illustrates the pH trend and radius of

influence within near wellbore after 20 days of pH 12 reagent injection The radius of influence

is the effective zone within 2 metres around the wellbore where most of the permeability change

took place (Figure 624) In the first meter the permeability increased to 61 mD which then

decreased and plateau at 55 mD at 2 metres from the well This was followed by a sharp decrease

in the permeability beyond 2 meters from the well that corresponded to the pH drop from 12 to

118 (Figure 624) At a point 3 metres into the reservoir from the injection well the permeability

remained at its initial value of 33 mD Figure 625 shows changes in the pH within the first 40

meters of reservoir and after 20 days of injection until the pH dropped to the actual formation water

pH of 8 Following the permeability change the pH of the injected plume gradually dropped as it

infiltrates into the reservoir (Figure 625) with a maximum penetration radius of 25 metres around

the wellbore A small bent in the pH trend that corresponded to the permeability drop in Figure

624 within 2 meters from the wellbore is marked by a vertical bar in Figure 625 The pH was

buffered by the changing fluid chemistry within the near wellbore and decreased gradually until it

took a sharp drop after 20 metres (Figure 625) In contrast to the first 2 meters there was no

gradual decrease in the permeability corresponding to the pH change seen from 2 meters on in the

reservoir The permeability remained constant below a pH of 118 (Figure 625) Hence the

injected plume penetration was much deeper into the reservoir although it was only effective

within a few metres

150

Figure 624 The pH and permeability trends within closed radius of wellbore after 20 days of

injection

Figure 625 The injected fluid pH trends after 20 days of NaOH solution of pH 12 injection and

the plume penetration distance from the wellbore Vertical bar indicates initial drop in pH that

resulted in permeability change in Figure 624

3000

3500

4000

4500

5000

5500

6000

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

pH

Distance

Q=12 kgs_pH 12_20 Days

pH (12kgs) Permeability (12 kgs)

7

8

9

10

11

12

13

0 10 20 30 40

pH

Distance(m)


pH Drop

151

The varying stauration states of the rock forming minerals contained in the Catherine

Sandstone reservoir are illustrated in Figure 626 Due to injection of alkali fluid all of the

minerals were undersaturated within the first 2 metres from the wellbore which coincided with

the zone of maximum permeability change in Figures 624 Within the radius of less than a meter

into the reservoir a sharp change in the ankerite sauration index was observed (Figure 626)

which corresponded with the sudden drop in permeability from 61 to 55mD in Figure 624

Following ankertie the saturation indices of the remaining minerals approached equilibrium with

the formation fluid as the pH dropped below 12 due to the release of silica and carbonate as a result

of dissolution (Figures 625 and 626) The absolute volume fraction of ankerite anorthite and

albite within the near wellbore decreased to zero within 20 days (Figure 627) which indicated

that they were completely dissolved by the pH 12 fluid The change in volume fraction of the other

silicate minerals within the near wellbore was very small (Figure 628) This showed that most of

the permebaility change in Figure 624 was due to the dissolution of feldspar minerals The

dissolution of the remaining silicate minerals including quartz at pH 12 was not imposing

noticeable change to the reservoir permeability at a selected flushing period of 20 days

Figure 626 Saturation Indices of minerals contained in the reservoir after 20 days of NaOH (pH

12) injection Positive and negative values indicates precipitation and dissolution

-20

-15

-10

-5

0

5

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)


albite ankertite anorthite k-FeldsparQuartz Kaolinite Muscovite

152


Figure 628 Change in minerals volume fraction in the reservoir after 20 days of NaOH (pH 12)

injection Negative sign indicates dissolution

000E+00

500E-03

100E-02

150E-02

200E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)


ankerite anorthite albite

-160E-04

-140E-04

-120E-04

-100E-04

-800E-05

-600E-05

-400E-05

-200E-05

000E+00

0 5 10 15 20 25 30 35

∆V

olum

e Fr

actio

n

Distance (m)


k-feldspar quartz kaolinite muscovite

153

6262 Varying Injection Duration

The 20 days stimulation period at an injection rate of 12 kgs led to an enhancement in

the permeability of 30 mD near the wellbore (as described in Section 6261) Due to the change

in pH that influenced the saturation state of the minerals (Figure 626) the maximum radius of

influence remained at approximately 2 metres from the wellbore In order to overcome any

immediate drop in the pH and to increase the radius of influence using the same concentration of

reagent the stimulation period was increased from 20 to the maximum of 120 days at a constant

injection rate (Figure 629) Multiple simulations were performed at varying total number of days

of geochemical stimulation using NaOH solution of pH 12 The maximum permeability

enhancement at 4 different injection periods remained approximately 62 mD (Figure 629)

However there was a noticeable increase in the radius of influence around the wellbore going from

30 to 120 days of constant injection of a fluid with a pH of 12 The radius of influence already

extended to 4m after 30 days of pH 12 injection (Figure 629) The pH trends in Figure 6210

demonstrated that the plume penetrated further into the reservoir over time The pH eventually

dropped down to the initial pH of 8 after 120 days and more than 70 metres inside the reservoir

With every 30 days increase in the total injection time the plume penetrated a further 10-15 metres

into the reservoir (Figure 6210) In contrast there was only a 1 metre enhancement in the radius

of influence with every doubling of the total injection period as illustrated in Figure 629

Comparing the permeability trend with the pH there were two significant plateaus in the

permeability trend (Figure 629) that coincided with the pH trends in Figure 6210 and 6211

The decline in the permeability from 62mD to 56mD at 2-4 meters was explained by the initial

bend in the pH (Figure 6211) The second drop in permeability from 56m to 33mD at 4-6 metres

was explained by the small drop in pH from 12 to 119 (Figure 6211)

154

Figure 629 Permeability changes within certain distance of the wellbore in response to the

varying injection duration

Figure 6210 The injected fluid pH trends after varying total injection period and the plume

penetration distance from the wellbore

32

37

42

47

52

57

62

67

0 2 4 6 8

Perm

eabi

lity

(m

D)

Distance (m)

30-120 Days Injection (Q=12 kgs)

permeability_30 days permeability_60 days


8

85

9

95

10

105

11

115

12

125

0 20 40 60 80

pH

Distance (m)

30-120 Days Injection (12kgs)

pH_30 days pH_60 dayspH_90 days pH_120 days

155

Figure 6211 The injected fluid pH trends within closed radius of the wellbore after varying the

injection period

6263 Varying Injection Rate

While keeping the injection period constant (20 days) the injection rate was varied to

observe the effect on the injeciton plume and reservoir stimulation A NaOH solution of pH 12

was injected in the Catherine Sandstone reservoir model Two higher injection rates of 5 and 10

kgs were tested to compare to the initial rate of 12kgs used in the previous sections The

permeability and pH responses to the higher injection rates are illustrated in Figures 6212 and

6213 respectively The permeability and pH trends were similar to the trends seen for longer

injection durations of 90 and 120 days (Figures 629 and 6210) At the maximum injection rate

model of 10kgs the radius of influence (which was the zone of maximum permeability

enhancement) extended up to 8 metres after 20 days of injection (Figure 6212) The permeability

change in Figure 6212 was similar to the permeability enhancement after 120 days of injection

at 12kgs (Figure 629) The injection plume penetrated 80 metres into the reservoir in 20 days at

maximum injection rate of 10kgs (Figure 6214) compared to 60 metres at 12kgs in 120 days

(Figure 6210) There was an initial drop in the permeability trend from approx 61mD to 56mD

in both scenarios (Figures 629 and 6212) which was not observed in the respective pH trends

(Figures 6210 and 6213) The first drop in permeability was controlled by the initial change in

119

1192

1194

1196

1198

12

1202

1204

1206

0 2 4 6 8

pH

Distance (m)


pH_30 days

pH_60 days

pH_90 days

pH_120 days

156

the saturation indices of anorthite and ankerite (Figure 6215) A sharp increase in the saturation

index of anorthite from -22 to -12 together with ankertie which approached equilibrium (Figure

6215) coincided with the sharp decrease in the permeability from 62 to 56mD (Figure 6212)

The abrupt change in saturation indices of anorthite and ankertie also overlapped the appearence

of dissolved calcium close to 2 metres into the reservoir in Figure 6215 The saturation index of

anorthite followed the same trend later as other minerals in the system and eventually approached

equilibrium after 6 metres (10 kgs) into the reservoir This was represented by the sharp decrease

in both initial injection pH and permeability The maximum enhancement in the permeability

around the immeadiate vicinity of the wellbore at a maximum injection rate of 10kgs was

approximately 30 mD (Figure 6212) which was similar to the previous similuations (Figure

629) Using the mineral composition of Catherine Sandstone the permeability could not be

enhanced further since permeability increase near the wellbore at pH 12 was domianantly

controlled by the dissolution of carbonate and feldspar minerals As soon as these two reactive

minerals were dissolved completely (Figure 6216) under pH 12 within the first 6 meters into the

reservoir there was no further enhancement in the reservoir permeability The dissolved silica

concentration in Figure 6217 reduced to zero within the near wellbore as the formation fluid was

entirely replaced by the injected fluid of pH 12 within 2 to 6 metres As soon as the dissolved silica

apppeared due to dissolution of silicate minerals the pH started to drop and the dissolution rate

was reduced accordingly The dissolved silica concentration gradually increased until the

maximum plume penetration radius was reached (30 to 80 metres Figures 6214 and 6217) The

gradual spike in silica represented the dissolution of remaining silicate minerals such as quartz

kaolinite and muscovite at pH 118 as they remained undersaturated as shown in Figure 6512

Since there was no further increase in the permeability beyond 2-6 metres (Figure 6212) the

dissolution of quartz was not significant enough to impose a noteworthy increase in the reservoir

permeability

157

Figure 6212 Permeability trends as a function of varying injection rates after 20 days of pH 12

injection

Figure 6213 The pH trends within close radius of the wellbore as a function of varying

injection rates after 20 days of NaOH (pH 12) injection

32

37

42

47

52

57

62

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)

20 Days Varying Injection Rate

12 kgs

5 kgs

10 kgs

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

pH

Distance (m)

pH vs Injection rate

20days(12kgs)

20days(5kgs)

20days(10kgs)

158

Figure 6214 The pH trends as a function of varying injection rates after 20 days of NaOH (pH

12) injection showing complete plume penetration into the reservoir

Figure 6215 Saturation states of minerals in Catherine Sandstone reservoir after 20 days of

injection at maximum injection rate of 10 kgs Positive and negative values indicates precipitation

and dissolution

8

85

9

95

10

105

11

115

12

0 10 20 30 40 50 60 70 80 90

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

-27

-22

-17

-12

-7

-2

3

0 2 4 6 8 10

Ca

(mol

kg)

Satu

ratio

n In

dex

Distance (m)

20 Days Injection (10 kgs)

albite ankertite anorthite k-FeldsparQuartz Kaolinite Muscovite Dissolved Ca

159

Figure 6216 Absolute volume fraction of ankertie and anorthite after 20 days of pH 12 injection

at the rate of 10kgs

Figure 6217 Dissolved silica concentration in the near wellbore as a function of varying

injection rates At 20 days

000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

140E-02

160E-02

180E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)

Volume Fraction of Minerals_10kgs_20 days

Ankerite Anorthite albite

624E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Con

c (

mol

kg)

Distance (m)

SiO2 vs Inj Rates

SiO2_12kgs SiO2_5kgs SiO2_10kgs

160

627 Reservoir Stimulation using Acidic Reagents

In order to compare the performance of alkaline flooding with acid HCl solution with a

pH of 2 was injected uner the same reservoir conditions as described in Section 626 The

simulation was performed at a constant injection rate of 12 kgs for a period of 20 days The

maximum permeability enhancement near the wellbore was 6 mD after 20 days of HCl (pH 2)

injection (Figure 6218) The pH trend during acid injection was comparable to the permeability

trend in Figure 6218 The permeability started dropping gradually as soon as the injection pH

buffered to neutral (Figure 6218) drastically reducing the mineral dissolution rate The only

mineral that was close to saturation and did not dissolve throughout the acid injection was quartz

(Figure 6219) Hence the change in volume fraction of quartz during pH 2 injection is zero as

shown in Figure 6220 Similar to the pH 12 injection the permeability enhancement due to the

injection of fluid with a pH of 2 was mainly controlled by the dissolution of carbonate (ankerite)

as its volume fraction reduced to zero within 4 metres into the reservoir (Figure 6220) Figure

6221 compares the dissolved silica concentration in the reservoir within 30 metres around the

wellbore after injection of fluids with a pH of 2 and 12 at a constant injection rate of 12 kgs for

20 days A significant increase in dissolved silica was observed during the injection of a pH 12

solution as compared to the silica trend during acid injection (pH 2) Higher dissolved silica

indicated enhanced dissolution of silicate minerals during alkali (pH 12) injection As a

consequence substantial differences in the final permeability increase could be seen during the

alkali flooding in comparision to the acid flooding at similar reservoir conditions (Figure 6222)

This further explains the lower effectiveness of acid controlled dissolution compared to alkali

stimulation in silicisclastic reservoirs Quartz and other silicate mineral were well under saturated

at a pH of 12 (Figure 6220) which was enough to impose approximately 30 mD increase in the

permeability in comparision with acid injection (Figure 6222) The radius of influence of

permeability enhancement during acid injection was similar to the pH 12 injection after 20 days

(Figure 6222)

161

Figure 6218 Permeability and pH trends after 20 days of HCl (pH 2) injection and the radius of

influence from the wellbore

Figure 6219 Saturation states of minerals contained in the reservoir after 20 days of HCl (pH


0

1

2

3

4

5

6

7

8

9

30

31

32

33

34

35

36

37

38

0 5 10 15 20 25 30

pH

Perm

eabi

lity

(mD

)

Distance (m)


Permeability pH

-50

-40

-30

-20

-10

0

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)


albite ankertite anorthite k-Feldspar

Quartz Kaolinite Muscovite

162

Figure 6220 Change in minerals volume fraction in the reservoir after 20 days of HCL (pH 2)


Figure 6221 Dissolved SiO2 in the reservoir after 20 days of NaOH (pH12) and HCl (pH 2)

injection at a constant rate of 12 kgs

000E+00

100E-03

200E-03

300E-03

400E-03

500E-03

600E-03

700E-03

-700E-04

-600E-04

-500E-04

-400E-04

-300E-04

-200E-04

-100E-04

000E+00

0 5 10 15 20 25 30

Vol

Fra

ctio

n (a

nker

ite)

∆V

olum

e Fr

actio

n

Distance (m)

20 Days_pH 2

k-feldspar quartz kaolinitemuscovite anorthite albiteankerite (vol frac)

600E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 10 20 30 40

Con

c (

mol

l)

Distance (m)

SiO2 Concentration

SiO2_NaOH SiO2_HCl

163

Figure 6222 Comparision of permeability enhancement within near wellbore after 20 days of

NaOH and HCl injection at constant injection rate of 12 kgs

63 Comparison of Porosity-Permeability Relationship

The Kozeny-Carman relationship was used to predict the porosity and permeability

relationship in the reactive transport models (Eq 113 Chapter 1) The relationship was derived

for a homogenous sandstone with uniform grain sizes as discussed in Chapter 5 (Section 52)

Keeping in mind that in a realistic scenario we have a heterogenous sandstone reservoir such as

the Catherine Sandstone (Garnett et al 2013) the change in permeability due to porosity

modification can vary significantly There may be multiple possible relationships between porosity

and permeability in a geological reservoir at field scales that can not be predicted with a single

simplified emperical relationship (Taylor 1948 Michaels amp Lin 1954 Freeze amp Cherry 1977

Nelson 1994 Bear 1972 Bourbie amp Zinszner 1985 Vaughan 1987 Verma amp Pruess 1988

Tokunaga et al 1998 Dewhurst et al 1999b Pape et al 1999 Revil amp Cathles 1999 Luijendijki

amp Gleeson 2015) Consequently a sensitivity analysis was carried out to predict various

possibilities for the extent of permeability increase due to change in porosity by mineral

dissolution The Verma amp Pruess porosity-permeability relationship (Chapter 1 Eq 114) that is

3200

3700

4200

4700

5200

5700

6200

6700

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)

20 Days Injection_12kgs

NaOH_pH 12 HCl_pH 2

164

incorporated in TOUGHREACT (TR) can be used for relatively complex reservoir rocks (Verma

amp Pruess 1988 Xu et al 2004b) It consists of independent variables that can be derived

experimentally for a realistic estimation of permeability change in a specific rock type (See

Chapter 5 Section 52)

A noticable increase in the permeability of the Catherine Sandstone core throughout the

core flooding experiments was only observed during the acid injection in Experiment 5 (Figure

526 Chapter 5 Section 522) Therefore Experiment 5 data was used to derive emptyc (critical

porosity) and W(power law exponent) in the Verma amp Pruess equation (Eq 114 Chapter 1) A

core scale reactive transport model was built with a mineral composition as reported in Table 25

(Chapter 2 Section 25) The dimensions of the geological model were kept the same as the core

F1-3b2 (Table 321 Chapter 3) More than 20 TOUGHREACT simulations were performed using

different combinations of emptyc and W values to find the best fit to the permeability versus time trend

in Experiment 5 (Figure 631) Three emptyc values 008 01 and 015 were used in the final models

that are discussed in the current section as they gave the closest fit to the experimental data (Figure

631) TR models used Wvalues of 30 and 16 to find the best fit with the experimental data (Figure

631)

Figure 631 Core flood data of Experiment 5 versus laboratorycore scale TOUGHREACT

modelling using Verma amp Pruess pore-perm relation with W=30 16 and emptyc=008 01 015

02

04

06

08

1

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

pH 2 Injection

CFS_Exp

TR_008_30

TR_01_30

TR_015_16

165

Furthermore the Verma amp Pruess porosity and permeability relationship (Eq57 Chapter 5) was

applied to the acid injection simulation at near well formation scale (Section 627) injecting a HCl

solution with a pH of 2 The same geological model and reservoir conditions as in Figure 611

were applied in the current simulations Two different emptyc of 008 and 01 were used in the field

scale simulation while keeping the same W constant (30) A solution of HCl of pH 2 was injected

at a constant rate of 12 kgs for 20 days leading to a permeability enhancement from 33 to 250

mD (Figure 632) Both values of emptyc (008 and 01) gave similar trends of permeability

enhancement after 20 days of pH 2 injection (Figure 632) This permeability increase is

significantly higher than predicted by the Kozeny-Carman relationship (7mD Figure 6218)

However the radius of influence in Figure 632 remained the same as in Figure 6218

Figure 632 Near well formation scale simulation of pH 2 injection using derived Wand emptyc values

of Verma amp Pruess equation by Experiment 5 data Two different emptyc values are used keeping W constant which resulted in same permeability trend

000

5000

10000

15000

20000

25000

30000

0 2 4 6 8 10

Per

mea

bil

ity

(m

D)

Distance (m)

pH 2 n=30 (critical porosity=008 01)

166

64 Feasibility Study

The application of geochemical reservoir simulation in geological CO2 sequestration

projects could help facilitate greater injection rates of CO2 in the subsurface It is essential to have

a storage reservoir with adequate flow properties in order to inject CO2 at high injection rates

(Lucier and Zoback 2008 Hosa et al 2011 Garnett et al 2013 Oye et al 2013 Verdon et al

2013 Hansen et al 2013 Lamy-Chappuis et al 2014 Peysson et al 2014 Andre et al 2014)

Fluid flow within a geological reservoir depends on inter connectivity of pore spaces that is

referred to as permeability The major technical limitation that caused the ZeroGen project


2 to 3 million tonnesyear (Garnett et al 2013) The reason was the highly heterogeneous nature

of Catherine Sandstone with variable permeability due to sedimentary facies variation The

Catherine Sandstone was the potential CO2 storage unit within the Denison Trough of the Bowen

Basin It is a heterogenous siliciclastic reservoir ranging in permeability from 08-520 mD (Table

23 Chapter 2) Near well formation scale simulations of the Catherine Sandstone in the previous

section were performed by assuming an average low permeability of 32 mD in the targeted storage

interval The extent of permeability enhancement ranged from 61 to 250 mD depending on the

empirical porosity-permeability relationship applied in the models (Figures 6218 and 632) In

order to assess the effectiveness of enhanced permeability in overcoming the reservoir pressure

build-up it was essential to simulate CO2 injection scenarios This would evaluate the effect of

permeability changes in Catherine Sandstone on the net CO2 pressure build up while injecting CO2

at a commercially required injection rate (Garnett et al 2013 Lamy-Chappuis et al 2014) To

simulate CO2 injection scenarios the geological model of the Catherine Sandstone and grid

distribution was kept the same as in previous field scale TOUGHREACT runs (Sections 626 and

627) The equation of state ECO2N was used to simulate the injection of pure CO2 into the

Catherine Sandstone (Xu et al 2004) Only the transport solver TOUGH2 was applied in the

following simulations ignoring the effect of chemical reactions (Pruess 1991) The aim was to

observe the pressure build-up near the well during CO2 injection

CO2 was injected at a constant rate of 12 kgs in the reservoir model with an average initial

permeability of 32 mD The pressure in the reservoir model was initially 200 bars that increased

to approximately 245 bars over 20 days of injection (Figure 641) The maximum permeability

167

enhancement due to the injection of pH 12 reagent using a Kozeny-Carman relationship was from

32 to 62 mD over 120 days of injection (Figure 629) The radius of influence achieved after 120

days of injection was approximately 4-5 metres (Figure 629) The CO2 injection was simulated

again in the Catherine Sandstone with an improved permeability of 62 mD modified within the

fixed radius of 5 metres The permeability beyond 5 meters radius into the entire reservoir was

kept 32 mD There were approximately 4 bars of pressure relief at the wellbore after 120 days of

pH 12 injection as shown in Figure 641 There was a larger shift in permeability caused by pH 2

injection using the Verma amp Pruess empirical relationship (Figure 632) with pressure decreased

from 245 bars to 238 bars within 60 metres around the wellbore (Figure 641) Even though there

was a significant increase in the permeability of 250 mD relative to the initial permeability of 32

mD (Figure 632) there was no significant difference in the reservoir pressure build up due to the

limited radius of influence of 5 meters around the wellbore (Figure 632)

Figure 641 Pressure build-up near the wellbore during CO2 injection as a function of different

near wellbore permeabilities Initial refers to the pressure build up from initial reservoir pressure

of 200 bars to 245 bars at actual reservoir permeability of 32 mD before geochemical stimulation

62 and 250 mD represents reservoir pressure build up after permeability enhancement in the near

wellbore after geochemical reservoir stimulation using Carman-Kozeny and Verma amp Pruess

porosity-permeability relation respectively

215

220

225

230

235

240

245

250

0 50 100 150 200 250 300

Pres

sure

(Bar

s)

Distance (m)

Wellbore Pressure_CO2 Injection_12 kgs

Initial (32mD) Carman Kozeny (62mD) Verma amp Pruess (250mD)

168

CHAPTER 7

7 Conclusion and Recommendations

71 Conclusion

This PhD project explored the potential of geochemical reservoir stimulation technique to

enhance the permeability of the Catherine Sandstone A permeability enhancement will lead to

higher CO2 injectivity in the sandstone reservoir and thereby improve the technical and

commercial viability of the ZeroGen CCS project (Garnett et al 2013) A feasibility study of

geochemical reservoir stimulation was performed by using field scale reactive transport modelling

Furthermore in this study the importance of determining site specific surface area of minerals is

highlighted and a new method has been developed to experimentally determine the effective

surface area of minerals in a consolidated core sample Surface area is one of the key parameters

that determines the reactivity of minerals in modelling studies simulating fluid-rock interaction

The following sections summarise the outcomes of experimental and modelling studies

711 Core Flood Dissolution Experiments

The effective surface area of quartz kaolinite and muscovite contained in a consolidated

core sample of Catherine Sandstone was successfully determined using core flood dissolution

experiments Highly reactive fluids of pH 2 and 12 were injected in cores to dissolve the

framework minerals High flow rates and short fluid residence times in the core flood experiments

helped keep the minerals undersaturated and far-from-equilibrium under both alkaline and acidic

conditions The measured effective surface area of kaolinite and muscovite were similar for both

high and low pH experiments but the effective surface area of quartz differs by two orders of

magnitude Moreover a significant variation in the effective surface area of quartz measured under

acidic conditions was observed in the stoichiometric error analysis due to its low solubility Hence

the effective surface area of quartz can be best determined accurately using a highly alkaline

injection fluid The measured effective surface area of quartz at pH 12 is within the lower range

while muscovite and kaolinite at pH 2 and 12 are within the higher range of BET and geometric

surface areas reported in the literature

169

The core flood dissolution experiments also aimed to observe the permeability

enhancement in Catherine Sandstone cores due to the dissolution of quartz and other siliciclastic

minerals A strong alkaline reagent of pH 12 was selected due to its high reactivity with quartz

relative to highly acidic solutions Quartz dissolution at pH 12 was not significant enough to

enhance the permeability of the core within the injection period of 30 days Instead the

permeability of the core was reduced during each alkaline (pH 12) injection The additional

pressure build-up was caused by the fines mobilization triggered by the interaction of the

negatively charged hydroxyl ions with the surfaces of the clay minerals Consequently

permeability enhancement in core flood experiments was only observed during acid injection

Hence alkaline fluids are not a suitable reagent for geochemical stimulation in clay rich

sandstones

712 Reactive Transport Modelling

7121 Modelling Experimental Results

Core scale reactive transport modelling using experimentally derived effective surface

areas was performed to compare the modelled effluent chemistry with data from the core flood

experiments The modelled silica trend after reaching a steady state at pH 2 and 12 showed a

good match with the steady state dissolved silica concentrations during core flood experiments

The modelled aluminium trend after reaching a steady state appeared to be 3mgl higher than the

steady state aluminium concentration during the core flood experiments at both acidic and alkaline

injections The higher aluminium concentration in the modelling may reflect high solubility

constant values for aluminium bearing minerals in the thermodynamic database used in the current

simulations Therefore it is necessary to test the consistency of reactive transport model outputs

by using different thermodynamic databases

Furthermore the core scale model helped determine the effective surface area of carbonates

in the Catherine Sandstone core samples which were present in trace amounts The carbonates

remained undetected during the mineralogical analysis of the samples using thin sections and XRD

analysis They were apparent in the form of dissolved calcium and magnesium ions in the fluid

samples during core flood experiments The effective surface area of carbonates was successfully

measured by matching the non-steady state concentration trends of calcium and magnesium during

170

the core flood experiment with the TOUGHREACT model Dissolved calcium in the fluid samples

during experiments was derived from calcite and dolomite dissolution while magnesium was

released by dolomite and magnesite dissolution The measured effective surface area of calcite and

magnesite falls within the lower range while the effective surface area of dolomite is within the

higher range of literature reported surface areas

7122 Modelling Geochemical Reservoir Stimulation at the Near Well Formation Scale

Near Well Formation Scale reactive transport modelling was done to assess the

effectiveness of geochemical stimulation at field scale The experimentally measured effective

surface areas of framework minerals in the Catherine Sandstone were used in the field scale

models The Kozeny-Carman porosity-permeability relationship was then used to extrapolate the

permeability change in the reservoir as a function of changing porosity due to mineral dissolution

The maximum permeability enhancement was higher during the alkaline injections in comparison

to the permeability increase during acid injections However the radius of influence remained

similar ie a few metres into the reservoir in both pH scenarios Since the phenomena of fines

migration is not considered in the modelling studies Therefore the above observation goes in

contrast to the experimental observation where fines migration limited permeability enhancement

during alkaline injection The permeability enhancement in the models reported at pH 12 and 2

was due to the dissolution of feldspars and carbonates The quartz dissolution was not significant

enough to impose a noticeable change in the permeability of Catherine Sandstone at either pH

level The porosity-permeability relationship of Verma amp Pruess incorporated in the

TOUGHREACT code was also optimised to experimental data The two site specific variables emptyc

(critical porosity) and W(power law exponent) in the Verma amp Pruess equation were successfully

derived by matching the permeability trend during the core flood experiment versus the modelled

data The modelled permeability enhancement in the Catherine Sandstone reservoir using Verma

amp Pruess relationship was an order of magnitude higher as compare to the simulations ran with

Kozeny-Carman equation But the radius of influence remained the same in both simulations

In the end the reservoir pressure build-up in Catherine Sandstone due to CO2 injection was

modelled to determine whether CO2 injectivity can be improved through ldquogeochemical reservoir

stimulationrdquo The acquired permeability data by using the Kozeny-Carman and Verma amp Pruess

porosity-permeability relations were used in the CO2 injection modelling Even though there could

171

be up to an order of magnitude increase in the reservoir permeability after geochemical stimulation

using Verma amp Pruess relationship there was no significant reduction in the pressure build up

observed during the CO2 injection A greater radius of permeability enhancement into the reservoir

was required to impose a significant drop in the pressure around the wellbore The maximum radius

of influence during geochemical reservoir stimulation remained at 6 metres around the wellbore

even after an injection period of 120 days Therefore the current methodology is not sufficient to

enhance the injectivity of CO2 at field scale

72 Recommendations

The following improvements in the research approach and research objectives have been

derived

bull The geological model used so far consisted of a sandstone reservoir with a homogenous

distribution in porosity permeability and minerology The core samples of Catherine

Sandstone contain multiple high and low permeable facies as described in Chapter 2

Section 24 Such facies variation if considered in the geological model may result in a

different output of porosity and permeability modification due to mineral dissolution

Hence a more complex and heterogenous geological model in future studies would help

present a more realistic representation of a CO2 storage reservoir

bull The TOUGHREACT modelling code comes with the default thermodynamic database

EQ36 compiled by Wolery (1992) There are other available databases used in the

speciation modelling in Chapter 4 Section 46 the results of which were better explained

with the experimental observations Even though EQ36 is one of the most commonly used

databases for geochemical modelling there is still a need to run the reactive transport

models using different thermodynamic databases to compare results This will lead to an

improved understanding of the underlying geochemical processes and a close comparison

of the modelled versus experimental data

bull The field scale modelling scenarios in the current study consisted of pH 2 and 12 injections

to dissolve reactive minerals around the wellbore At both pH levels the injected fluid was

172

buffered within the immediate vicinity of the wellbore This caused a significant drop in

the fluid-rock reactivity thus drastically reducing mineral dissolution and further

permeability enhancement in the reservoir A reactive reagent with a higher pH buffering

capacity such as organic solutions may help in reaching a greater radius of influence

around the wellbore Therefore a more in-depth investigation is required to study the buffer

capacities of different reactive fluids and model their ability to achieve a greater radius of

permeability enhancement around the wellbore

173

BIBLIOGRAPHY Alcott A D Swenson B Hardeman (2006) ldquoUsing PetraSim to create execute and post-

process TOUGH2 modelsrdquo Proceedings of TOUGH Symposium 2006 Lawrence Berkeley National Laboratory Berkeley California May 15-17 2006

Alexander G B (1954) ldquoThe polymerization of monosilicic acidrdquo Journal of American Chemical Society 76 2094-2096

Al-Tabbaa A Wood DM (1987) ldquoSome measurements of the permeability of kaolinrdquo Geotechnique 37 499ndash514

Andre L Peysson Y amp Azaroual M (2014) Well injectivity during CO2 storage operations in deep saline aquifers - Part 2 Numerical simulations of drying salt deposit mechanisms and role of capillary forces International Journal of Greenhouse Gas Control 22 301-312

Anthony DP (2001) ldquoMerivale field study PL 44 Denison Trough Queenslandrdquo Report for Oil Company of Australia Ltd (unpublished)

Anthony DP (2004) ldquoA review of recent conventional petroleum exploration and in field gas reserves growth in the Denison Trough Queenslandrdquo In Boult PJ Johns DR and Lang SS (eds) PESArsquos Eastern Australasian Basins Symposium II Handbook 277-296

Aquilanti L Clementi F Nanni T Palpacelli S Tazioli A Vivalda PM (2016) ldquoDNA and fluorescein tracer tests to study the recharge groundwater flow path and hydraulic contact of aquifers in the Umbria-Marche limestone ridge (central Apennines Italy)rdquo Environmental Earth Science 75 5436ndash5441

Aradottir E S P Sigfusson B Sonnenthal E L Bjornsson G and Jonsson H (2013)

ldquoDynamics of basaltic glass dissolution ndash capturing microscopic effects in continuum scale modelsrdquo Geochimica et Cosmochimica Acta 121 311ndash327

Audigane P I Gaus I Czernichowski-Lauriol K Pruess and T Xu (2007) ldquoTwo-dimensional reactive transport modelling of CO2 injection in a saline aquifer at the 256 Sleipner site North Seardquo American Journal of Science 307 974ndash1008

Baker J C and Patrice de Caritat (1992) ldquoPost depositional history of the Permian sequence in the Denison Trough Eastern Australiardquo The American Association of Petroleum Geologists Bulletin 76 No 8 1224-1249

Baker J C (1989) ldquoPetrology diagenesis and reservoir quality of the Aldebaran Sandstone Denison Trough east-central Queenslandrdquo PhD thesis University of Queensland (unpublished)

Baker J C (1991) ldquoDiagenesis and reservoir quality of the Aldebaran Sandstone Denison Trough east-central Queensland Australiardquo Sedimentology 38 819-838

Baker J C (2008) ldquoSandstone Petrology-Springton-4 and ZeroGen-6 Denison Troughrdquo Reservoir Solutions PTY LTD (unpublished)

174

Baker J C (2009) ldquoWell completion report EPQ-1 Queenslandrdquo Reservoir Solutions Pty Ltd report for ZeroGen

Baker J C Fielding C R de Ceritat P and Wilkinson M M (1993) ldquoPermian evolution of sandstone composition in a complex back-arc extensional to foreland basin The Bowen Basin eastern Australiardquo Journal of Sedimentary Petrology 63 881-893

Baraka-Lokmane S Main I G Ngwenya B T Elphick S C (2009) Application of complementary methods for more robust characterization of sandstone cores Marine and Petroleum Geology 26 39-56

Bastian L V (1964) ldquoPetrographic notes on the Peawaddy Formation Bowen Basin Queenslandrdquo Bur Miner Resour Aust Rec 1964193 (unpublished)

Bear J (1972) ldquoDynamics of Fluids in Porous Mediardquo Elsevier New York

Beckingham L E Mitnick E H Steefel C I Zhang S Voltolini M Swift A M Yang L Cole D R Sheets J M Ajo-Franklin J B DePaolo D J Mito S and Z Xue (2016) ldquoEvaluation of mineral reactive surface area estimates for prediction of reactivity of a multi-mineral sedimentrdquo Geochimica et Cosmochimica Acta 188 310ndash329

Beckingham L E Mitnick E H Steefel C I Zhang S Voltolini M Swift A M Yang L Cole D R Kneafsey J T Landrot G Sheets J M Ajo-Franklin J B DePaolo D J Mito S and Z Xue (2017) ldquoEvaluation of accessible mineral surface areas for improved prediction of mineral reaction rates in porous mediardquo Geochimica et Cosmochimica Acta 205 31ndash49

Beckingham L E (2017) ldquoEvaluation of macroscopic porosity-permeability relationships in heterogenous mineral dissolution and precipitation scenariosrdquo Water Resources Research 53 httpsdoiorg1010022017WR021306

Bennett P C (1991) Quartz dissolution in organic-rich aqueous systems Geochimica et Cosmochimica Acta 55 1781- 1797

Bennett P C (1988) The dissolution of quartz in dilue aqueous solutions of organic acids at 25degCrdquo Geochimica et Cosmochimica Acta 52 1521-1530

Bethke CM and Yeakel S (2012) ldquoThe Geochemistrsquos Workbench Release 90 Reaction Modelling Guiderdquo Aqueous Solutions LLC Champaign Illinois

Bennion D B Thomas F B Bennion D W Bietz R F (1996) ldquoFluid design to minimize invasive damage in horizontal wellsrdquo Journal of Canadian Petroleum Technology 35 (9) 45ndash52 November

Black J R Carroll S Haese R (2015) ldquoRates of mineral dissolution under CO2 storage conditionsrdquo Chemical Geology 399 134-144

Bloom P R and Weaver R M (1982) ldquoEffect of the removal of reactive surface material on the solubility of synthetic gibbsitesrdquo Clays and Clay Minerals 30 281-286

175

Bolourinejad P Shoeibi Omrani P and Herber R (2014) ldquoEffect of reactive surface area of minerals on mineralization and carbon dioxide trapping in a depleted gas reservoirrdquo International Journal of Greenhouse Gas Control 21 11ndash22

Bourbie T and Zinszner B (1985) ldquoHydraulic and acoustic properties as a function of porosity in fontainebleau sandstonerdquo Journal of Geophysical Research 90 11524ndash11532

Brady P V and Walther J V (1990) ldquoKinetics of quartz dissolution at low temperaturesrdquo Chemical Geology 82 253-264

Byrnes A P (1997) ldquoReservoir characteristics of low-permeability sandstones in the Rocky Mountainsrdquo Mountain Geologist(1) 37

Chou L and Wollast R (1985) ldquoSteady state kinetics and dissolution of mechanisms of albiterdquo American Journal of Science 285 963ndash993

Cohen C E Ding D Quintard M amp Bazin B (2008) From pore scale to wellbore scale Impact of geometry on wormhole growth in carbonate acidization Chemical Engineering Science 63(12) 3088-3099

Colon C F J Oelkers E H Schott J (2004) ldquoExperimental investigation of the effect of dissolution on sandstone permeability porosity and reactive surface areardquo Geochimica et Cosmochimica Acta 68 805ndash817

Coradin T Eglin D and Livage J (2004) ldquoThe silicomolybdic acid spectrophotometric method and its application to silicatebiopolymer interaction studiesrdquo Journal of Spectroscopy 18 567576

Crundwell F K (2015) The mechanism of dissolution of the feldspars Part I Dissolution at conditions far from equilibrium Hydrometallurgy 151 151-162

Dandekar Y A (2013) ldquoPetroleum Reservoir Rock and Fluid Properties 2nd editionrdquo CRC Press Taylor and Francis Group NewYork

Dewhurst S N et al (1999) ldquoInfluence of clay fraction on pore-scale properties and hydraulic conductivity of experimentally compacted mudstonesrdquo Journal of Geophysical Research 104 29261

Dickins J M and Malone E J (1973) ldquoGeology of the Bowen Basin Queenslandrdquo Department of Minerals amp Energy Bureau of Mineral Resources Geology amp Geophysicsrdquo Bulletin 130

Exon N F and Kirkegaarad G (1965) ldquoNotes on the stratigraphy of the north-east part of the Tambo 1250000 Sheet areardquo Bur Miner Resour Aust Rec 196590 (unpublished)

Faucher J A Southworth R W and Thomas H C (1952) ldquoAdsorption studies on clay minerals I Chromatography on claysrdquo Journal of Chemical Physics 20 157-160

Fielding C R Falkner A J Kassan J and Draper J J (1990a) ldquoPermian and Triassic depositional systems in the Bowen Basin In Beestonrdquo J W (Ed) Bowen Basin

176

Symposium 1990 Proceedings Geological Society of Australia (Queensland Division) 21- 25

Fielding C R Sliwa R Holcombe R I and Kassan J (2000) ldquoA new palaeogeographic synthesis of the Bowen Basin of central Queenslandrdquo In Beeston JW (Ed) Bowen Basin Symposium 2000 Proceedings Geological Society of Australia 287- 302

Flint A L Flint L E Kwicklis E M Fabryka-martin J T Bodvarsson G S (2002) ldquoEstimating recharge at Yucca Mountain Nevada USA Comparison of methodsrdquo Hydrogeology Journal 10 180ndash204

Freeze R A and Cherry J A (1977) ldquoGroundwaterrdquo Prentice-Hall Englewood Cliffs NJ

Gabriel G A Inamdar G R (1983) ldquoAn experimental investigation of fines migration in porous mediardquo SPE 58th Annual Technical Conference and Exhibition San Francisco CA October 5ndash8 1983 SPE Paper No 12168

Garnett A J Greig C R Oettinger M (2013) ZeroGen IGCC with CCS A case Historyrdquo The State of Queensland (Department of Employment Economic Development and Innovation)

Garside I E (1990) ldquoFacies and potential reservoir study Freitag Catherine and Mantuan formations northern ATP 337P Denison Troughrdquo Report for AGL Petroleum (unpublished) Australia Ltd (unpublished)

Global CCS Institute (2014) ldquoThe Global Status of CCS 2014rdquo Melbourne Australia

Golab A Romeyn R Averdunk H Knackstedt M Senden T J (2013) ldquo3D characterisation of potential CO2 reservoir and seal rocksrdquo Australian Journal of Earth Sciences 60 111ndash123

Gouze P Luquot L (2011) ldquoX-ray microtomography characterization of porosity permeability and reactive surface changes during dissolutionrdquo Journal of Contaminant Hydrology 120ndash121 45ndash55

Haggerty R Gorelick S M (1995) ldquoMultiple-rate mass transfer for modelling diffusion and surface reactions in media with pore-scale heterogeneityrdquo Water Resource Research 31 2383ndash2400

Hansen O Gilding D Nazarian B Osdal B Ringrose P Kristoffersen J-B Hansen H (2013) ldquoSnoslashhvit The history of injecting and storing 1 Mt CO2 in the Fluvial Tubaringen Fmrdquo Energy Procedia 37 3565-3573 doi 101016jegypro201306249

Heederik J P (1988) ldquoGeothermische Reserves Centrale Slenk Nederlandrdquo Exploratie en evaluatie Technical report TNO Utrecht

Helgeson H C Murphy W M Aagaard P (1984) ldquoThermodynamic and kinetic constraints on reaction rates among minerals and aqueous solutions II Rate constants effective surface area and the hydrolysis of feldsparrdquo Geochimica et Cosmochimica Acta 48 2405ndash2432

177

Hellevang H Pham V T H Aagaard P (2013) ldquoKinetic modelling of CO2ndashwaterndashrock interactionsrdquo International Journal of Greenhouse Gas Control 15 3ndash15

Hill D and Denmead A K (1960) ldquoThe geology of Queenslandrdquo Journal of geological Society of Australia 7

Hosa A Esentia M Stewart J amp Haszeldine S (2011) ldquoInjection of CO2 into saline formations Benchmarking worldwide projectsrdquo Chemical Engineering Research and Design 89 1855-1864 doi 101016jcherd201104003

House W A and Orr D R (1992) Investigation of the pH-dependence of the Kinetics of Quartz Dissolution at 25degC Journal of the Chemical Society-Faraday Transactions 88(2) 233-241

IPCC (Intergovernmental Panel on Climate Change) (2005) ldquoIPCC special report on carbon dioxide capture and storagerdquo prepared by Working Group III of the Intergovernmental Panel on Climate Change [Metz B O Davidson H C de Coninck M Loos and L A Meyer (eds)] Cambridge University Press Cambridge United Kingdom and New York NY USA 442

Jackson K S Hawkins P J amp Bennett A J R (1980) ldquoRegional facies and geochemical evaluation of the southern Denison trough Queenslandrdquo APEA Journal 20 143-158

John B H and Fielding C R (1993) ldquoReservoir potential of the Catherine Sandstone Denison Trough East Central Queenslandrdquo APEA Journal 33(1X) 176-187

Kalfayan L (2008) Production enhancement with acid stimulationrdquo 2nd Edition PennWell Corporation Tulsa Oklahoma USA

Kieffer B Colon CFJ Oelkers EH Schott J (1999) ldquoAn experimental study of the reactive surface area of the fontainbleau sandstone as a function of porosity permeability and fluid flow raterdquo Geochimica et Cosmochimica Acta 63 3525ndash3534

Knauss K G and Wolery T J (1986) ldquoDependence of albite dissolution kinetics on pH and time at 25degC and 70degCrdquo Geochimica et Cosmochimica Acta 50248 l-2497

Knauss K G and Wolery T J (1987) ldquoThe dissolution kinetics of quartz as a function of pH and time at 70degCrdquo Geochimica et Cosmochimica Acta 52 43-53

Knauss K G and Wolery T J (1989) ldquoMuscovite dissolution kinetics as a function of pH and time at 70degCrdquo Geochimica et Cosmochimica Acta 53 1493-501

Knoll M D (1996) ldquoA petrophysical basis for ground penetrating radar and very early time electromagnetics Electrical properties of sandndashclay mixturesrdquo PhD thesis The University of British Colombia

Konikow L F and Mercer J W (1988) ldquoGroundwater flow and transport modellingrdquo Journal of Hydrogeology 100 379409

178

Lai P Moulton K and Krevor S (2015) ldquoPore-scale heterogeneity in the mineral distribution and reactive surface area of porous rocksrdquo Chemical Geology 411 260ndash273

Lamy-Chappuis B Angus D Fisher Q Grattoni C amp Yardley B W D (2014) Rapid porosity and permeability changes of calcareous sandstone due to CO2 enriched brine injection Geophysical Research Letters 41(2) 399-406

Landrot G Ajo-Franklin J B Yang L Cabrini S Steefel C (2012) Measurement of accessible reactive surface area in a sandstone with application to CO2 mineralization Chemical Geology 318ndash319 113ndash125

Lasaga A C Soler J M Ganor J Burch T E and Nagy K L (1994) ldquoChemical weathering rate laws and global geochemical cyclesrdquo Geochimica et Cosmochimica Acta 58 2361-2386

Liu M Zhang S Mou J amp Zhou F (2013) Wormhole Propagation Behavior Under Reservoir Condition in Carbonate Acidizing Transport in Porous Media 96(1) 203-220

Lucier A and Zoback M (2008) Assessing the economic feasibility of regional deep saline aquifer CO2 injection and storage A geomechanics-based workflow applied to the Rose Run sandstone in Eastern Ohio USA International Journal of Greenhouse Gas Control 2(2) 230-247

Luijendijk E and Gleeson T (2015) How well can we predict permeability in sedimentary basins Deriving and evaluating porosity-permeability equations for noncemented sand and clay mixtures Geofluids (1-2) 67

Luquot L Gouze P (2009) ldquoExperimental determination of porosity and permeability changes induced by injection of CO2 into carbonate rocksrdquo Chemical Geology 265 148ndash159

Marini L (2007) ldquoGeological Sequestration of Carbon Dioxide Thermodynamics Kinetics and Reaction Path Modellingrdquo Elsevier Amsterdam

Marsh C and Scott A (2005) Review of the Carbon Dioxide Injection and Storage Potiential of the Denison Trough CO2CRC Report no RPT05-0015

Martin K R (1982) ldquoA petrological study of the Aldebaran Formation and Reids Dome Beds in AAR Merivale-1 and -2 Westgrove-5 and Glentulloch-4 Denison Trough Queenslandrdquo Report for AAR Ltd (unpublished)

Martin K R (1986) ldquoPetrology and diagenesis of the Reids Dome Beds in Westgrove-3 Denison Trough Queenslandrdquo Report for AAR Ltd (unpublished)

McClung G (1981) ldquoReview of the Stratigraphy of the Permian Back Creek Group in the Bowen Basin Queenslandrdquo Geological Survey of Queensland Publication 371 Paleontological Paper 44

Mesri G and Olson R E (1971) ldquoMechanism controlling the permeability of claysrdquo Clays and Clay Minerals 19 151ndash158

179

Michaels A S and Lin C (1954) ldquoPermeability of kaoliniterdquo Industrial and Engineering Chemistry 46 1239ndash1246

Mitra A (2008) Silica Dissolution at Low pH in the Presence and Absence of Fluoriderdquo PhD Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University

Mitra A and J D Rimstidt (2009) Solubility and dissolution rate of silica in acid fluoride solutions Geochimica Et Cosmochimica Acta 73(23) 7045-7059

Mollan R G Dickins J M Exon N F (1969) ldquoGeology of the Springsure 1250000 sheet areardquo Queensland Bureau of Mineral Resources Report 123 p 119

Mollan R G (1972) ldquoExplanatory notes on the Springsure geological sheetrdquo Sheet SG55-3 international index Australian Government Publishing Service Canberra 1972

Murray CG (1990) ldquoTectonic evolution and metallogenesis of the Bowen Basinrdquo In Editor not supplied Bowen Basin Symposium 1990 Proceedings Geological Society of Australia (Queensland Division) 201-212

Navarre-Sitchler A Cole D Rother G Jin L Buss H Brantley S (2013) ldquoPorosity and surface area evolution during weathering of two igneous rocksrdquo Geochimica et Cosmochimica Acta 109 400ndash413

Nelson P H (1994) ldquoPermeability-porosity relationships in sedimentary rocks Log Analystrdquo 35(3) 38e62

Noiriel C Luquot L Madeacute B Raimbault L Gouze P Van Der Lee J (2009) ldquoChanges in reactive surface area during limestone dissolution An experimental and modelling studyrdquo Chemical Geology 265 160ndash170

Oelkers E H Schott J Gauthier J M Roncal T H (2008) ldquoAn experimental study of the dissolution mechanism and rates of muscoviterdquo Geochimica et Cosmochimica Acta 72 4948-4961

Ott H de Kloe K van Bakel M Vos F van Pelt A Legerstee P Makurat A (2012) ldquoCore-flood experiment for transport of reactive fluids in rocksrdquo Review of Scientific Instruments 83 84

Oye V Aker E Daley T M Kuumlhn D Bohloli B amp Korneev V (2013) ldquoMicroseismic monitoring and interpretation of injection data from the in Salah CO2 storage site (Krechba) Algeriardquo Energy Procedia 37 4191-4198 doi 101016jegypro201306321

Palandri J L and Kharaka Y K (2004) ldquoA compilation of rate parameters of water-mineral interaction kinetics for application to geochemical modellingrdquo US Geological Survey Open File Report 2004-1068

Pape H Clauser C and Iffland J (1999) ldquoPermeability prediction based on fractural pore-space geometryrdquo Geophysics 64 1447ndash1460

180

Paten R J and G P McDonagh (1976) ldquoBowen basinrdquo in R B Leslie H J Evans and C 1 Knight eds ldquoEconomic geology of Australia and Papua New Guineardquo Petroleum Australian Institute of Mining and Metallurgy Monograph Series 7 403-420

Peryea F J and Kittrick J A (1988) ldquoRelative solubility of corundum gibbsite boehmite and diaspore at standard state conditionsrdquo Clays and Clay Minerals 36 391-396

Peters CA (2009) ldquoAccessibilities of reactive minerals in consolidated sedimentary rock an imaging study of three sandstonesrdquo Chemical Geology 265 198ndash208

Peysson Y Andre L amp Azaroual M (2014) Well injectivity during CO2 storage operations in deep saline aquifers-Part 1 Experimental investigation of drying effects salt precipitation and capillary forces International Journal of Greenhouse Gas Control 22 291-300

Pham V T H et al (2011) ldquoOn the potential of CO2ndashwaterndashrock interactions for CO2 storage using a modified kinetic modelrdquo International Journal of Greenhouse Gas Control 5 1002ndash1015

Pokrovsky O S Golubev SV Schott J Castillo A (2009) ldquoCalcite dolomite and magnesite dissolution kinetics in aqueous solutions at acid to circumneutral pH 25 to 150 degC and 1 to 55 atm pCO2 new constraints on CO2 sequestration in sedimentary basinsrdquo Chemical Geology 265 (1ndash2) 20ndash32

Pokrovskii VA and Helgeson HC (1995) ldquoThermodynamic properties of aqueous species and the solubilities of minerals at high pressures and temperatures the system Al2O3-H2O-NaClrdquo American Journal of Science 295 1255-1342

Portier S and Vuataz F D (2010) Developing the ability to model acid-rock interactions and mineral dissolution during the RMA stimulation test performed at the Soultz-sous-Forets EGS site France Comptes Rendus Geoscience 342(7-8) 668-675

Portier S Andre L and Vuataz F D (2007) Review on chemical stimulation techniques in oil industry and applications to geothermal systems CREGE ndash Centre for Geothermal Research Neuchacirctel Switzerland

Pruess K (1991) ldquoTOUGH2-A general purpose numerical simulator for multiphase fluid and heat flowrdquo Report LBL-29400 Berkeley California Lawrence Berkeley Laboratory ACC NNA199402020088

Qinghua W Guiling W Wei Z Haodong C and Wei Z (2016) ldquoEstimation of Groundwater

Recharge Using Tracers and Numerical Modelling in the North China Plainrdquo Water 8 353

Revil A and Cathles L M (1999) Permeability of shaly sands Water Resources Research 35(3) 651-662

Rimstidt J D and Barnes H L (1980) ldquoThe kinetics of silica-water reactionsrdquo Geochimica et Cosmochimica Acta 44 1683-1699

181

Sengor S S N Spycher T R Ginn R K Sani B Peyton (2007) ldquoBiogeochemical reactive-diffusive transport of heavy metals in Lake Coeur drsquoAlene sedimentsrdquo Applied Geochemistry 22(12) 2569-2594

Sengor S S Spycher N Ginn T R Sani R K Peyton B (2007) ldquoBiogeochemical reactive-diffusive transport of heavy metals in Lake Coeur drsquoAlene sedimentsrdquo Applied Geochemistry 22(12) 2569-2594

Shafiq M U Shuker M T amp Kyaw A (2013) Performance Comparison of New Combinations of Acids with Mud Acid in Sandstone Acidizing Research Journal of Applied Sciences Engineering and Technology 7(2)(2014) 323-328

Sonnenthal E Ito A Spycher N Yui M Apps J Sugita Y Conrad M Kawakami S (2005) ldquoApproaches to modelling coupled thermal hydrological and chemical processes in the Drift Scale Heater Test at Yucca Mountain International Journal of Rock Mechanics and Mining Sciences 42 6987ndash719

Steefel C Depaolo D Lichtner P (2005) Reactive transport modeling An essential tool and a new research approach for the Earth sciences Earth and Planetary Science Letters 240 539-558 101016jepsl200509017

Stober W (1967) ldquoFormation of silicic acid in aqueous suspensions of different silica modificationsrdquo Advan Chem Ser 67 161-182

Stoessell R K and Pittman E D (1990) Secondary porosity revisited The chemistry of feldspar dissolution by carboxylic acids and anions AAPG Bulletin (American Association of Petroleum Geologists) (United States) 1795

Tavenas F Jean P Leblond P and Leroueil S (1983) ldquoThe permeability of natural soft clays Part II Permeability characteristicsrdquo Canadian Geotechnical Journal 20 645ndash660

Taylor D W (1948) ldquoFundamentals of soil mechanicsrdquo Soil Science 66 161

Thompson J E and Duff P G (1965) ldquoBentonite in the Upper Permian Black Alley Shale Bowen Basin Queenslandrdquo Bur Miner Resour Aust Rec 1965171 (unpublished)

Thornton D S amp Radke J C (1988) ldquoDissolution and Condensation Kinetics of Silica in Alkaline Solutionrdquo SPE Reservoir Engineering 3 743-752 10211813601-PA

Tokunaga T Hosoya S Tosaka H Kojima K (1998) ldquoAn estimation of the intrinsic permeability of argillaceous rocks and the effects on long-term fluid migrationrdquo Geological Society London Special Publications 141 83ndash94

Vaidya R N and Fogler H S (1990) ldquoFormation Damage due to Colloidally Induced Fine Migration Colloids and Surfacesrdquo 50 215ndash229

Vaidya R N and Fogler H S (1992) ldquoFines Migration and Formation Damage Influence of pH and Ion Exchangerdquo SPE Production Engineering 7(4) 325ndash330

182

Vasseur G Dje ran-Maigre I Grunberger D Rousset G Tessier D Velde B (1995) ldquoEvolution of structural and physical parameters of clays during experimental compactionrdquo Marine and Petroleum Geology 12 941ndash954

Vaughan P J (1987) ldquoAnalysis of permeability reduction during flow of heated aqueous fluid through westerly graniterdquo Academic Press New York 529ndash539

Velbel M A (1985) ldquoGeochemical mass balances and weathering rates in forested watersheds of the southern blue ridgerdquo American Journal of Science 285 904ndash930

Verma A and Pruess K (1988) ldquoThermohydrological conditions and silica redistribution near high-level nuclear wastes emplaced in saturated geological formationsrdquo Journal of Geophysical Research 93 1159ndash1173

Wahlberg J S Baker J H Vernon R W Dewar R S (1965) ldquoExchange Adsorption of Strontium on Clay Mineralsrdquo Geological Survey Bulletin (US) No 1140-C

Wesolowski DJ Palmer D A and Begun G M (1990) ldquoComplexation of aluminate anion by Bis-Tris in aqueous media at 25-50degCrdquo Journal of Solution Chemistry 19 159-173

Wilkinson M M (1983) ldquoPermian geology and environment of deposition of the Aldebaran Sandstone of the Serocold Anticline east-central Queenslandrdquo BSc Honours thesis University of Queensland (unpublished)

Wolery T J (1992) ldquoEQ3NR A Computer Program for Geochemical Aqueous Speciation-Solubility Calculations Theoretical Manual Users Guide and Related Documentationrdquo (Version 70) UCRL-MA-110662-PT-in Lawrence Livermore National Laboratory Livermore California

Xu T Sonnenthal E Spycher N Karsten Pruess (2004) TOUGHREACT users guide ldquoA

simulation program for non-isothermal multiphase reactive geochemical transport in variable saturated geologic mediardquo Lawrence Berkeley National Laboratory LBNL-55460

Xu T Spycher N Sonnenthal E Zheng L Pruess K (2012) TOUGHREACT users guide

ldquoA simulation program for non-isothermal multiphase reactive geochemical transport in variable saturated geologic media Version 20rdquo Lawrence Berkeley National Laboratory University of California Berkeley

Xu T and Pruess K (1998) ldquoCoupled modelling of non-isothermal multiphase flow solute

transport and reactive chemistry in porous and fractured mediardquo Model development and validation Lawrence Berkeley National Laboratory Report LBNL-42050 Berkeley California 38 pp TIC 243735

Xu T Apps J Pruess K Yamamoto H (2007) ldquoNumerical modelling of injection and mineral

trapping of CO2 with H2S and SO2 in a sandstone formationrdquo Chemical Geology 242 (3ndash4) 319ndash346

183

Xu T Ontoy Y Molling P Spycher N Parini M Pruess K (2004b) ldquoReactive transport modelling of injection well scaling and acidizing at Tiwi Field Philippinesrdquo Geothermics 33(4) 477-491 2

Xu T Rose P Fayer S Pruess K (2009) ldquoOn modelling of chemical stimulation of an

enhanced geothermal system using a high pH solution with chelating agentrdquo Geofluids 9 167ndash177

Xu T Zhang W Pruess K (2010) ldquoNumerical Simulation to Study Feasibility of Using CO2

as a Stimulation Agent for Enhanced Geothermal Systemrdquo Thirty-Fifth Workshop on Geothermal Reservoir Engineering Stanford University Stanford California SGP-TR-188

Yang L Xu T Yang B Tian H Lei H (2014) ldquoEffects of mineral composition and

heterogeneity on the reservoir quality evolution with CO2 intrusionrdquo Geochemistry Geophysics Geosystems 15 605ndash618 doi101002 2013GC005157

Ziolkowski V and Taylor R (1985) ldquoRegional structure of the north Denison Troughrdquo Bowen

Basin Coal Symposium Geological Society of Australia Abstracts Series 17 129-135

Minerva Access is the Institutional Repository of The University of Melbourne

AuthorsAli Syed Anas

TitleDetermining the effective surface area of minerals and the implications for near wellboregeochemical reservoir stimulation

Date2018

Persistent Linkhttphdlhandlenet11343216037

Terms and ConditionsTerms and Conditions Copyright in works deposited in Minerva Access is retained by thecopyright owner The work may not be altered without permission from the copyright ownerReaders may only download print and save electronic copies of whole works for their ownpersonal non-commercial use Any use that exceeds these limits requires permission fromthe copyright owner Attribution is essential when quoting or paraphrasing from these works

i

ABSTRACT Sufficient CO2 injection capacity is a key criteria for a prospective CO2 storage site and has proven

to be a technical impediment for the development of a CO2 storage operation for example in case

of the ZeroGen project This study develops and applies geochemical reservoir stimulation

procedures involving pH-controlled solutions to promote mineral dissolution and increase

permeability of a siliciclastic reservoir to enhance CO2 injectivity Effective deployment of a

geochemical stimulation technique at field scale requires site-specific data and an understanding

of the underlying geochemical reactions coupled to fluid flow within a reservoir Thus laboratory

scale experiments are developed and experimental results are used in reactive transport

simulations using the TOUGHREACT code to assess the degree of mineral dissolution and

possible associated increase in porosity and permeability under variable conditions The surface

area of minerals is often one of the least well-constrained variables in porous rocks and therefore

introduces a large uncertainty in reactive-transport modelling results Weathering reaction rates in

natural systems have been shown to be orders of magnitude lower than predicted using models

involving assumptions regarding mineral surface area-to-mass ratios The discrepancy has been

explained by several reasons including mineral overgrowth poor pore-to-pore connectivity and

heterogeneous flow fields Therefore a new methodology has been developed to determine the

effective surface area of minerals using core flood experiments and applied to Catherine Sandstone

samples The derived mineral effective surface areas are incorporated into near-wellbore reactive

transport models evaluating the feasibility of enhancing permeability through geochemical

stimulation

ii

DECLARATION


preface





Syed Anas Ali

iii













iv





















been successful

v














151 Permeability 24

















vi

















321 Experiment 2 73

322 Experiment 3 77

323 Experiment 4 77

324 Experiment 5 78






41 Experiment 2 84

42 Experiment 3 86

43 Experiment 4 89

44 Experiment 5 95

45 Experiment 6a 98

46 Experiment 6b 99



vii

5 DISCUSSION 106














137



622 Model Setup 143









71 Conclusion 168




viii

GLOSSARY



mineralm3mineral)



medium)




ix




x



5


11



13



16



18


25


27


39


30


32


36


36



40



48


49



50


51

xi



52



53


58


60



72








85



88


88


90


91


92


93


94


96


96


97

xii


98


100


101


101


103


103


104


105


118


118


119


120


121


121


122


122


125


125


127

xiii


127


128


135


135


136


137


140


141


141


142


145




150


150


151

xiv


152


152


154


154


155


157


157


158


158


159


159


161


161


162


162


163


164


165


167

xv



21


27


44


45


46


55


55


59







94



114


114


116


117

xvi


140


145


146


146

1

CHAPTER 1




























2































3






of TOUGHREACT code

4


5




















6






























7


131 CO2 Injectivity



















for CCS









8


















methods














9































10
















reagent

11




12































13

















14




























15
















1412 Regular Mesh






16



right

1413 Polygonal Mesh




17




1414 Radial Mesh











18



















19












88+=] (13)


6 789 minus8

8+=]A$ (14)


6 789 minus8

8+=]AB$C (15)















20













solids and








in the model

$GH=05r (19)




saturation

21















A=λ n M Ao (110)






$ = D 7 JJK=1M

(111)






22








following equation










Q = QR (81emptyS)T










23









=$V

(114)











field scale









24

151 Permeability



























25















26









follows


(115)










Q = QRYDV (116)





27


Gleeson 2015)

Equation Equation

Number



Power

Law

Porosity

16 ampR m2 765e-17 153e-19 844e-23


Power

Law void

ratio

17 ampR m2 616e-17 154e-19 118e-21



28

















29


Cathles 1999)















30





amp Gleeson 2015)










31



















de1dS (121)











32










Chapter 2)



33








modelling resources














34

CHAPTER 2


Characterization


























35



















36





37









Reids Dome Beds

221 Reids Dome Beds














38













marine conditions

39




























40









41





















al 1969)









42






























43



















Sandstones










44



















Depth 105060 106230 106680 127500

Porosity () 32 65 86 61


Quart + Chert () 863 913 906 793

K-feldspar () 64 51 63 78


Mica () 03 - - -



45












in Baker 2008

Depth (m) 58888 94645

Porosity () 125 94





Mica () 03 03









46













Garnett et al 2013)



Depth 85454 91535 92022 94321 94376 94510

Porosity () 177 123 134 131 126 117

Permeability(mD) 330 520 322 321 121 080

Quart + Chert

()

881 757 751 849 817 806

K-feldspar () 50 149 130 78 107 88

Plagioclase () 07 39 45 21 27 33

Mica () - 03 - - - 03

Authigenic

Kaolin ()

27 11 07 50 51 28


47

















48

241 Sampling Sites















Mollan et al 1969)

49



50


Mount Catherine













51











52



al 1969)

53



1969)

54






























55



Sample Quartz

Wt

plusmn1

Kaolinite

Wt

plusmn1

Orthoclase

Wt plusmn1

Albite

Low

Wt

plusmn1

Muscovite

Wt plusmn1

Ammonio-

-Jarosite

Wt plusmn1

F1-1 81 7 1 2 9

F1-4 81 7 1 2 9

F4-2 81 7 1 2 9

F2-1 81 7 1 2 9

F2-3 81 7 1 2 9

I 1 63 9 5 4 18 2

I 2-1 62 6 3 4 24

NY-3 78 5 4 2 11

NY-4 72 10 5 1 12



Sample F1-3c

F2-1

F2-2b

(Fines)

wt sd wt sd wt sd


Quartz 652 1 672 04 - -




Kaolinite 227 03 139 02 81 55

Mica 45 05 37 0 18 12

56

















K-Feldspar Mica












57



Vb = ghij1ghkl

m (23)




Vb = π r2 h (24)





Vp = n]3o1n^pq

m (25)


Oslash = rsre

x 100 (26)










58










core F1-1

y = 13692x + 03846

Rsup2 = 0994

0

2

4

6

8

10

12

14

16

0 002 004 006 008 01 012

∆P

(p

si)

Q (ccmin)


(F1-1)

59



Sample

no

Length

(cm)

Porosity

()

Small

Chunk

Porosity

()

Core

Sample

Error Permeability

(mD)

Description

F1-1 99 2384 2325 +-01 0476 Good for exp



F1-5 63 - 23 +-08 13 Small

F2-1 15 2517 +-06 15 Sample broken


F2-2 144 - 242 +-06 495 Good for CFS exp


F4-1 206 - 217 - 150-500 Fines released






60


core F4-2















y = 00825x - 00375

Rsup2 = 09973

0

005

01

015

02

025

03

035

04

0 1 2 3 4 5 6

∆P

(psi

)

Q (ccmin)


(F4-2)

61







light

62








63








64









65








polarized light

66







67








68







69







70













deviations

71

CHAPTER 3




























72












73






















321 Experiment 2









74















Core Length

(cm)

Diameter

(cm)

Porosity

()

Permeability

(mD)

Pore Volume

(mL)

F2-2a 64 381 242 495 1766

F1-3a 6 381 2029 lt1 139

F1-3b1 51 381 1802 lt1 1046

F1-3b2 5 381 18 lt1 1026

F2-2b 52 381 242 1870 1435

75



76



77


322 Experiment 3












323 Experiment 4



78












324 Experiment 5





Core Conf

Pressure

(PSI)

Back

Pressure

(PSI)

oC

Form

Fluid

Injected

Fluid

pH Flow

Rate

mLmi

n

Stage 1 F1-3a 3000 2000 60 05M

NaCl

0001001

00001M

NaOH

1011

amp12

003

Stage 2 F1-3a 3000 2000 60 05 M

NaCl

0001001M

NaOH

10

12

003

Stage 3 F1-3a 3000 2000 60 05 M

NaCl

001M HCl 2 003

79











80





















81































82



















Standard

Concentration

[mgL]

1000

1000

1000

1000

1000

1000

1000

100

10


12mL of 2 HNO3

075mL each

element into

1275mL of 2

HNO3

Undiluted Undiluted

Calibration

Concentrations

[mgL]

50 20 10 350 075

50 20 10 350

075

100 50

30 10 2

10 5 3 1

02

83





84

CHAPTER 4


41 Experiment 2
























85




86




42 Experiment 3












20

25

30

35

40

45

0 20 40 60

silic

a (m

gl)

Hours

Experiment 2

87

















25degC 60degC



88




during experiment 3

0

2

4

6

8

10

12

14

0

15

30

45

60

75

90

105

120

0 20 40 60 80 100 120 140 160 180

pH

Con

c (

mg

l)

Hours

Experiment 3

SiAlCaFepH

pH Breakthrough

89

43 Experiment 4






















10 (Figure 431)








90













6

7

8

9

10

11

12

0

5

10

15

20

25

30

35

40

45

0 2 4 6 8 10 12 14

pH

Con

c (

mg

l)

Days


SiAlCaMgFeKpH

Stage 1a pH= 11

05M NaCl

Stage 1b pH= 12

05M NaCl

Stage 1c

pH= 101

05M NaCl

91



















6

7

8

9

10

11

12

0

10

20

30

40

50

60

14 16 18 20 22 24

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

Stage 2a

pH= 12

001M

NaCl

Stage 2b

pH= 10

05M NaCl Stage 2c

pH= 12

DI water

Stage 2d

pH= 75

05 M NaCl

92




















0

2

4

6

8

10

12

000

2000

4000

6000

8000

10000

12000

14000

30 32 34 36 38 40 42

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

pH= 2

001M HCl

93











01

014

018

022

026

03

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

Experiment 4

pH= 12

pH= 2pH= 75

pH= 11

Stage 2

Stage 1

Stage 3

94





Al 3054 mgL

Si 4968 mgL

K 048 mgL

Na+ 001375 moll

H+ 10e-12 moll

Fe Mg Ca 178e-6 mgL




respectively

-15

-10

-5

0

5

10

Quartz(SiO)

Chalcedony(SiO)

Kaolinite(AlSiO)

Boehmite(AlOH)

Gibbsite(AlOH)

Muscovite(KAlSiO)

FeO

Satu

ratio

n St

ates

(QK

)

Minerals


pH 2

pH 56

pH 12

95

44 Experiment 5




























experiment 5

96




(right)

97
















pH 4



98

45 Experiment 6a
















(Figure 451)



0

1

2

3

4

5

6

7

0

15

30

45

60

75

90

105

120

135

0 5 10

pH

Con

c (

mg

l)

Time (Days)

Exp 6a (pH 2)

AlCaFeKMgSipH

99

46 Experiment 6b






























100





101



-6

-5

-4

-3

-2

-1

0


Satu

ratio

n St

ates

(QK

)

Minerals


003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

-35

-3

-25

-2

-15

-1

-05

0

05


Satu

ratio

n St

ates

(QK

)

Minerals


0015mlmin(684min)

003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

102

47 Experiment 7a

















was below 1mgL

103






0

2

4

6

8

10

12

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30

pH

Con

c (

mg

l)

Hours

Experiment 7a_pH 12

Al

K

Si

pH


2 mlmin

-18

-16

-14

-12

-10

-8

-6

-4

-2

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7a_pH 12

05 mlmin(29min)

1 mlmin(14min)

2 mlmin(7min)

104

48 Experiment 7b
















0

2

4

6

8

10

12

0

10

20

30

40

50

60

0 20 40 60

pH

Con

c (

mg

l)

Hours

Experiment 7b_pH 2

Al

Ca

Fe

K

Mg

Si

pH

025 mlmin

0125 mlmin

05 mlmin1 mlmin

105




-25

-20

-15

-10

-5

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7b_pH 2

025mlmin(57min)

05 mlmin(29min)

1 mlmin(14min)

106

CHAPTER 5

5 DISCUSSION



























107





xylowast = (5)


















= (Sj)M (53)

108





weight of the core


























109

















Eq 53

110















pH rate




Quartz via Si


12 15e-12 61e-13

Kaolinite via Al


Ganor et al 1994

-

12 21e-15 98e-16

Muscovite via K



-

12 312e-16 21e-16

111































112































113


















be estimated

114




(mLmin)


4 (Stage 2a) 003 12 49 29 2

4 (Stage 2c) 003 12 49 29 2

4 (stage 3) 003 2 71 37 5

5 003 2 40 33 3

6a 003 2 44 28 15

6b 003 12 48 34 045



al 2015)

115






























later Section 513

116



















(mLmin)


7a

05

12

2165 95 05

1 11 59 025

2 76 385 0125

7b

025

2

79 64 07

05 395 32 035

1 2 165 025

117



















511)

118



rates

0

10

20

30

40

50

60

70

0 200 400 600 800

Silic

a (m

gl)


(Experiment 6b_Si)

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800

Alu

min

ium

(m

gl)



119


















0

2

4

6

8

10

12

0 10 20 30 40

Alu

min

ium

(m

gl)



120


















0

5

10

15

20

25

0 10 20 30 40

Silic

a (m

gl)



121



0

01

02

03

04

05

06

0 10 20 30 40

Pota

ssiu

m (

mg

l)



005

115

225

335

445

5

0 20 40 60 80

Alu

min

ium

(m

gl)



122



0

2

4

6

8

10

12

0 20 40 60 80

Sili

ca (m

gl)



0

01

02

03

04

05

06

07

08

0 20 40 60 80

Pota

ssiu

m (

mg

l)



123

514 Error Analysis





results

























124






















125





0

05

1

15

2

25

3

35

-01

0

01

02

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14

Si a

t pH

12

(mg

l)

Si a

t pH

2 (

mg

l)

pH


0

0002

0004

0006

0008

001

0001

001

01

1

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Quartz

126






























127







0

2

4

6

8

10

12

Kaolinite Muscovite

Surf

ace

Are

a (m

2 g)


XRD XRD+5 XRD+10

0

1

2

3

4

5

6

7

8

9

10

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14

Al a

t pH

12

(mg

l)

Al a

t pH

2 (

mg

l)

pH


128

















8

10

12

14

16

18

20

22

24

8

10

12

14

16

18

20

22

24

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Kaolinite

129




























laboratory scale

130































131




















experiment 5











132



















1)

133

CHAPTER 6




























134



























135



0

5

10

15

20

25

0 2 4 6 8 10 12 14 16 18 20 22 24

silic

a (m

gl)

Time (Hours)


Model_Si Exp_Si

012345678910

0

1

2

3

4

5

6

7

0 5 10 15 20 25

pH

alum

inum

(m

gl)

Time (Hours)



136



















(pH 12 injection)

0

10

20

30

40

50

0 2 4 6 8

silic

a (m

gl)

Time (Hours)


Exp_Si Model_Si

137


injection













7

8

9

10

11

12

13

0

2

4

6

8

10

12

14

16

0 2 4 6 8

pH

alum

inum

(m

gl)

Time (Hours)



138





mineral analysis


























139



























140








0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)





Weight Percent ()

Calcite 500 0025

Dolomite 4000 0050

Magnesite

500 0150

600 0180

141









0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)



142



















0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



143







622 Model Setup













al 2013)









144





88+=] (62)


88+=]A

$ (63)


88+=]AB

$C (64)




















145



2013)

146





Minerals A

(m2 g-1)

k25

(mol m2 s-1)

Ea

(KJ mol-1)


Albite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

Ankerite 0006 126e-9 6276 6457e-4 361 05 - - -

Anorthite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

K-feldspar 0006 389e-13 38 871e-11 517 05 631e-22 941 -0823

Quartz 0006 398e-14 218 - - - 513e-17 259 -05

Kaolinite 16 6918e-14 222 4898e-12 659 077 8913e-18 179 -0472

Muscovite 71 282e-14 22 14e-12 22 037 282e-15 22 -022

147




















148



149























within a few metres

150


injection




3000

3500

4000

4500

5000

5500

6000

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

pH

Distance



7

8

9

10

11

12

13

0 10 20 30 40

pH

Distance(m)


pH Drop

151


















-20

-15

-10

-5

0

5

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)



152




000E+00

500E-03

100E-02

150E-02

200E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



-160E-04

-140E-04

-120E-04

-100E-04

-800E-05

-600E-05

-400E-05

-200E-05

000E+00

0 5 10 15 20 25 30 35

∆V

olum

e Fr

actio

n

Distance (m)



153
























154





32

37

42

47

52

57

62

67

0 2 4 6 8

Perm

eabi

lity

(m

D)

Distance (m)




8

85

9

95

10

105

11

115

12

125

0 20 40 60 80

pH

Distance (m)



155


injection period

















119

1192

1194

1196

1198

12

1202

1204

1206

0 2 4 6 8

pH

Distance (m)


pH_30 days

pH_60 days

pH_90 days

pH_120 days

156

























permeability

157


injection



32

37

42

47

52

57

62

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


12 kgs

5 kgs

10 kgs

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

158





and dissolution

8

85

9

95

10

105

11

115

12

0 10 20 30 40 50 60 70 80 90

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

-27

-22

-17

-12

-7

-2

3

0 2 4 6 8 10

Ca

(mol

kg)

Satu

ratio

n In

dex

Distance (m)



159





000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

140E-02

160E-02

180E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



624E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Con

c (

mol

kg)

Distance (m)

SiO2 vs Inj Rates


160


























(Figure 6222)

161





0

1

2

3

4

5

6

7

8

9

30

31

32

33

34

35

36

37

38

0 5 10 15 20 25 30

pH

Perm

eabi

lity

(mD

)

Distance (m)


Permeability pH

-50

-40

-30

-20

-10

0

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)




162





000E+00

100E-03

200E-03

300E-03

400E-03

500E-03

600E-03

700E-03

-700E-04

-600E-04

-500E-04

-400E-04

-300E-04

-200E-04

-100E-04

000E+00

0 5 10 15 20 25 30

Vol

Fra

ctio

n (a

nker

ite)

∆V

olum

e Fr

actio

n

Distance (m)

20 Days_pH 2


600E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 10 20 30 40

Con

c (

mol

l)

Distance (m)

SiO2 Concentration

SiO2_NaOH SiO2_HCl

163

















3200

3700

4200

4700

5200

5700

6200

6700

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


NaOH_pH 12 HCl_pH 2

164
















631)



02

04

06

08

1

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

pH 2 Injection

CFS_Exp

TR_008_30

TR_01_30

TR_015_16

165













000

5000

10000

15000

20000

25000

30000

0 2 4 6 8 10

Per

mea

bil

ity

(m

D)

Distance (m)


166































167



















215

220

225

230

235

240

245

250

0 50 100 150 200 250 300

Pres

sure

(Bar

s)

Distance (m)



168

CHAPTER 7


71 Conclusion

























169











sandstones



















170































171








72 Recommendations


derived


















172








173
















174















175
















176















177















178















179















180















181















182

















183













Date2018



ii

DECLARATION


preface





Syed Anas Ali

iii













iv





















been successful

v














151 Permeability 24

















vi

















321 Experiment 2 73

322 Experiment 3 77

323 Experiment 4 77

324 Experiment 5 78






41 Experiment 2 84

42 Experiment 3 86

43 Experiment 4 89

44 Experiment 5 95

45 Experiment 6a 98

46 Experiment 6b 99



vii

5 DISCUSSION 106














137



622 Model Setup 143









71 Conclusion 168




viii

GLOSSARY



mineralm3mineral)



medium)




ix




x



5


11



13



16



18


25


27


39


30


32


36


36



40



48


49



50


51

xi



52



53


58


60



72








85



88


88


90


91


92


93


94


96


96


97

xii


98


100


101


101


103


103


104


105


118


118


119


120


121


121


122


122


125


125


127

xiii


127


128


135


135


136


137


140


141


141


142


145




150


150


151

xiv


152


152


154


154


155


157


157


158


158


159


159


161


161


162


162


163


164


165


167

xv



21


27


44


45


46


55


55


59







94



114


114


116


117

xvi


140


145


146


146

1

CHAPTER 1




























2































3






of TOUGHREACT code

4


5




















6






























7


131 CO2 Injectivity



















for CCS









8


















methods














9































10
















reagent

11




12































13

















14




























15
















1412 Regular Mesh






16



right

1413 Polygonal Mesh




17




1414 Radial Mesh











18



















19












88+=] (13)


6 789 minus8

8+=]A$ (14)


6 789 minus8

8+=]AB$C (15)















20













solids and








in the model

$GH=05r (19)




saturation

21















A=λ n M Ao (110)






$ = D 7 JJK=1M

(111)






22








following equation










Q = QR (81emptyS)T










23









=$V

(114)











field scale









24

151 Permeability



























25















26









follows


(115)










Q = QRYDV (116)





27


Gleeson 2015)

Equation Equation

Number



Power

Law

Porosity

16 ampR m2 765e-17 153e-19 844e-23


Power

Law void

ratio

17 ampR m2 616e-17 154e-19 118e-21



28

















29


Cathles 1999)















30





amp Gleeson 2015)










31



















de1dS (121)











32










Chapter 2)



33








modelling resources














34

CHAPTER 2


Characterization


























35



















36





37









Reids Dome Beds

221 Reids Dome Beds














38













marine conditions

39




























40









41





















al 1969)









42






























43



















Sandstones










44



















Depth 105060 106230 106680 127500

Porosity () 32 65 86 61


Quart + Chert () 863 913 906 793

K-feldspar () 64 51 63 78


Mica () 03 - - -



45












in Baker 2008

Depth (m) 58888 94645

Porosity () 125 94





Mica () 03 03









46













Garnett et al 2013)



Depth 85454 91535 92022 94321 94376 94510

Porosity () 177 123 134 131 126 117

Permeability(mD) 330 520 322 321 121 080

Quart + Chert

()

881 757 751 849 817 806

K-feldspar () 50 149 130 78 107 88

Plagioclase () 07 39 45 21 27 33

Mica () - 03 - - - 03

Authigenic

Kaolin ()

27 11 07 50 51 28


47

















48

241 Sampling Sites















Mollan et al 1969)

49



50


Mount Catherine













51











52



al 1969)

53



1969)

54






























55



Sample Quartz

Wt

plusmn1

Kaolinite

Wt

plusmn1

Orthoclase

Wt plusmn1

Albite

Low

Wt

plusmn1

Muscovite

Wt plusmn1

Ammonio-

-Jarosite

Wt plusmn1

F1-1 81 7 1 2 9

F1-4 81 7 1 2 9

F4-2 81 7 1 2 9

F2-1 81 7 1 2 9

F2-3 81 7 1 2 9

I 1 63 9 5 4 18 2

I 2-1 62 6 3 4 24

NY-3 78 5 4 2 11

NY-4 72 10 5 1 12



Sample F1-3c

F2-1

F2-2b

(Fines)

wt sd wt sd wt sd


Quartz 652 1 672 04 - -




Kaolinite 227 03 139 02 81 55

Mica 45 05 37 0 18 12

56

















K-Feldspar Mica












57



Vb = ghij1ghkl

m (23)




Vb = π r2 h (24)





Vp = n]3o1n^pq

m (25)


Oslash = rsre

x 100 (26)










58










core F1-1

y = 13692x + 03846

Rsup2 = 0994

0

2

4

6

8

10

12

14

16

0 002 004 006 008 01 012

∆P

(p

si)

Q (ccmin)


(F1-1)

59



Sample

no

Length

(cm)

Porosity

()

Small

Chunk

Porosity

()

Core

Sample

Error Permeability

(mD)

Description

F1-1 99 2384 2325 +-01 0476 Good for exp



F1-5 63 - 23 +-08 13 Small

F2-1 15 2517 +-06 15 Sample broken


F2-2 144 - 242 +-06 495 Good for CFS exp


F4-1 206 - 217 - 150-500 Fines released






60


core F4-2















y = 00825x - 00375

Rsup2 = 09973

0

005

01

015

02

025

03

035

04

0 1 2 3 4 5 6

∆P

(psi

)

Q (ccmin)


(F4-2)

61







light

62








63








64









65








polarized light

66







67








68







69







70













deviations

71

CHAPTER 3




























72












73






















321 Experiment 2









74















Core Length

(cm)

Diameter

(cm)

Porosity

()

Permeability

(mD)

Pore Volume

(mL)

F2-2a 64 381 242 495 1766

F1-3a 6 381 2029 lt1 139

F1-3b1 51 381 1802 lt1 1046

F1-3b2 5 381 18 lt1 1026

F2-2b 52 381 242 1870 1435

75



76



77


322 Experiment 3












323 Experiment 4



78












324 Experiment 5





Core Conf

Pressure

(PSI)

Back

Pressure

(PSI)

oC

Form

Fluid

Injected

Fluid

pH Flow

Rate

mLmi

n

Stage 1 F1-3a 3000 2000 60 05M

NaCl

0001001

00001M

NaOH

1011

amp12

003

Stage 2 F1-3a 3000 2000 60 05 M

NaCl

0001001M

NaOH

10

12

003

Stage 3 F1-3a 3000 2000 60 05 M

NaCl

001M HCl 2 003

79











80





















81































82



















Standard

Concentration

[mgL]

1000

1000

1000

1000

1000

1000

1000

100

10


12mL of 2 HNO3

075mL each

element into

1275mL of 2

HNO3

Undiluted Undiluted

Calibration

Concentrations

[mgL]

50 20 10 350 075

50 20 10 350

075

100 50

30 10 2

10 5 3 1

02

83





84

CHAPTER 4


41 Experiment 2
























85




86




42 Experiment 3












20

25

30

35

40

45

0 20 40 60

silic

a (m

gl)

Hours

Experiment 2

87

















25degC 60degC



88




during experiment 3

0

2

4

6

8

10

12

14

0

15

30

45

60

75

90

105

120

0 20 40 60 80 100 120 140 160 180

pH

Con

c (

mg

l)

Hours

Experiment 3

SiAlCaFepH

pH Breakthrough

89

43 Experiment 4






















10 (Figure 431)








90













6

7

8

9

10

11

12

0

5

10

15

20

25

30

35

40

45

0 2 4 6 8 10 12 14

pH

Con

c (

mg

l)

Days


SiAlCaMgFeKpH

Stage 1a pH= 11

05M NaCl

Stage 1b pH= 12

05M NaCl

Stage 1c

pH= 101

05M NaCl

91



















6

7

8

9

10

11

12

0

10

20

30

40

50

60

14 16 18 20 22 24

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

Stage 2a

pH= 12

001M

NaCl

Stage 2b

pH= 10

05M NaCl Stage 2c

pH= 12

DI water

Stage 2d

pH= 75

05 M NaCl

92




















0

2

4

6

8

10

12

000

2000

4000

6000

8000

10000

12000

14000

30 32 34 36 38 40 42

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

pH= 2

001M HCl

93











01

014

018

022

026

03

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

Experiment 4

pH= 12

pH= 2pH= 75

pH= 11

Stage 2

Stage 1

Stage 3

94





Al 3054 mgL

Si 4968 mgL

K 048 mgL

Na+ 001375 moll

H+ 10e-12 moll

Fe Mg Ca 178e-6 mgL




respectively

-15

-10

-5

0

5

10

Quartz(SiO)

Chalcedony(SiO)

Kaolinite(AlSiO)

Boehmite(AlOH)

Gibbsite(AlOH)

Muscovite(KAlSiO)

FeO

Satu

ratio

n St

ates

(QK

)

Minerals


pH 2

pH 56

pH 12

95

44 Experiment 5




























experiment 5

96




(right)

97
















pH 4



98

45 Experiment 6a
















(Figure 451)



0

1

2

3

4

5

6

7

0

15

30

45

60

75

90

105

120

135

0 5 10

pH

Con

c (

mg

l)

Time (Days)

Exp 6a (pH 2)

AlCaFeKMgSipH

99

46 Experiment 6b






























100





101



-6

-5

-4

-3

-2

-1

0


Satu

ratio

n St

ates

(QK

)

Minerals


003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

-35

-3

-25

-2

-15

-1

-05

0

05


Satu

ratio

n St

ates

(QK

)

Minerals


0015mlmin(684min)

003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

102

47 Experiment 7a

















was below 1mgL

103






0

2

4

6

8

10

12

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30

pH

Con

c (

mg

l)

Hours

Experiment 7a_pH 12

Al

K

Si

pH


2 mlmin

-18

-16

-14

-12

-10

-8

-6

-4

-2

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7a_pH 12

05 mlmin(29min)

1 mlmin(14min)

2 mlmin(7min)

104

48 Experiment 7b
















0

2

4

6

8

10

12

0

10

20

30

40

50

60

0 20 40 60

pH

Con

c (

mg

l)

Hours

Experiment 7b_pH 2

Al

Ca

Fe

K

Mg

Si

pH

025 mlmin

0125 mlmin

05 mlmin1 mlmin

105




-25

-20

-15

-10

-5

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7b_pH 2

025mlmin(57min)

05 mlmin(29min)

1 mlmin(14min)

106

CHAPTER 5

5 DISCUSSION



























107





xylowast = (5)


















= (Sj)M (53)

108





weight of the core


























109

















Eq 53

110















pH rate




Quartz via Si


12 15e-12 61e-13

Kaolinite via Al


Ganor et al 1994

-

12 21e-15 98e-16

Muscovite via K



-

12 312e-16 21e-16

111































112































113


















be estimated

114




(mLmin)


4 (Stage 2a) 003 12 49 29 2

4 (Stage 2c) 003 12 49 29 2

4 (stage 3) 003 2 71 37 5

5 003 2 40 33 3

6a 003 2 44 28 15

6b 003 12 48 34 045



al 2015)

115






























later Section 513

116



















(mLmin)


7a

05

12

2165 95 05

1 11 59 025

2 76 385 0125

7b

025

2

79 64 07

05 395 32 035

1 2 165 025

117



















511)

118



rates

0

10

20

30

40

50

60

70

0 200 400 600 800

Silic

a (m

gl)


(Experiment 6b_Si)

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800

Alu

min

ium

(m

gl)



119


















0

2

4

6

8

10

12

0 10 20 30 40

Alu

min

ium

(m

gl)



120


















0

5

10

15

20

25

0 10 20 30 40

Silic

a (m

gl)



121



0

01

02

03

04

05

06

0 10 20 30 40

Pota

ssiu

m (

mg

l)



005

115

225

335

445

5

0 20 40 60 80

Alu

min

ium

(m

gl)



122



0

2

4

6

8

10

12

0 20 40 60 80

Sili

ca (m

gl)



0

01

02

03

04

05

06

07

08

0 20 40 60 80

Pota

ssiu

m (

mg

l)



123

514 Error Analysis





results

























124






















125





0

05

1

15

2

25

3

35

-01

0

01

02

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14

Si a

t pH

12

(mg

l)

Si a

t pH

2 (

mg

l)

pH


0

0002

0004

0006

0008

001

0001

001

01

1

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Quartz

126






























127







0

2

4

6

8

10

12

Kaolinite Muscovite

Surf

ace

Are

a (m

2 g)


XRD XRD+5 XRD+10

0

1

2

3

4

5

6

7

8

9

10

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14

Al a

t pH

12

(mg

l)

Al a

t pH

2 (

mg

l)

pH


128

















8

10

12

14

16

18

20

22

24

8

10

12

14

16

18

20

22

24

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Kaolinite

129




























laboratory scale

130































131




















experiment 5











132



















1)

133

CHAPTER 6




























134



























135



0

5

10

15

20

25

0 2 4 6 8 10 12 14 16 18 20 22 24

silic

a (m

gl)

Time (Hours)


Model_Si Exp_Si

012345678910

0

1

2

3

4

5

6

7

0 5 10 15 20 25

pH

alum

inum

(m

gl)

Time (Hours)



136



















(pH 12 injection)

0

10

20

30

40

50

0 2 4 6 8

silic

a (m

gl)

Time (Hours)


Exp_Si Model_Si

137


injection













7

8

9

10

11

12

13

0

2

4

6

8

10

12

14

16

0 2 4 6 8

pH

alum

inum

(m

gl)

Time (Hours)



138





mineral analysis


























139



























140








0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)





Weight Percent ()

Calcite 500 0025

Dolomite 4000 0050

Magnesite

500 0150

600 0180

141









0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)



142



















0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



143







622 Model Setup













al 2013)









144





88+=] (62)


88+=]A

$ (63)


88+=]AB

$C (64)




















145



2013)

146





Minerals A

(m2 g-1)

k25

(mol m2 s-1)

Ea

(KJ mol-1)


Albite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

Ankerite 0006 126e-9 6276 6457e-4 361 05 - - -

Anorthite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

K-feldspar 0006 389e-13 38 871e-11 517 05 631e-22 941 -0823

Quartz 0006 398e-14 218 - - - 513e-17 259 -05

Kaolinite 16 6918e-14 222 4898e-12 659 077 8913e-18 179 -0472

Muscovite 71 282e-14 22 14e-12 22 037 282e-15 22 -022

147




















148



149























within a few metres

150


injection




3000

3500

4000

4500

5000

5500

6000

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

pH

Distance



7

8

9

10

11

12

13

0 10 20 30 40

pH

Distance(m)


pH Drop

151


















-20

-15

-10

-5

0

5

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)



152




000E+00

500E-03

100E-02

150E-02

200E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



-160E-04

-140E-04

-120E-04

-100E-04

-800E-05

-600E-05

-400E-05

-200E-05

000E+00

0 5 10 15 20 25 30 35

∆V

olum

e Fr

actio

n

Distance (m)



153
























154





32

37

42

47

52

57

62

67

0 2 4 6 8

Perm

eabi

lity

(m

D)

Distance (m)




8

85

9

95

10

105

11

115

12

125

0 20 40 60 80

pH

Distance (m)



155


injection period

















119

1192

1194

1196

1198

12

1202

1204

1206

0 2 4 6 8

pH

Distance (m)


pH_30 days

pH_60 days

pH_90 days

pH_120 days

156

























permeability

157


injection



32

37

42

47

52

57

62

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


12 kgs

5 kgs

10 kgs

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

158





and dissolution

8

85

9

95

10

105

11

115

12

0 10 20 30 40 50 60 70 80 90

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

-27

-22

-17

-12

-7

-2

3

0 2 4 6 8 10

Ca

(mol

kg)

Satu

ratio

n In

dex

Distance (m)



159





000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

140E-02

160E-02

180E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



624E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Con

c (

mol

kg)

Distance (m)

SiO2 vs Inj Rates


160


























(Figure 6222)

161





0

1

2

3

4

5

6

7

8

9

30

31

32

33

34

35

36

37

38

0 5 10 15 20 25 30

pH

Perm

eabi

lity

(mD

)

Distance (m)


Permeability pH

-50

-40

-30

-20

-10

0

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)




162





000E+00

100E-03

200E-03

300E-03

400E-03

500E-03

600E-03

700E-03

-700E-04

-600E-04

-500E-04

-400E-04

-300E-04

-200E-04

-100E-04

000E+00

0 5 10 15 20 25 30

Vol

Fra

ctio

n (a

nker

ite)

∆V

olum

e Fr

actio

n

Distance (m)

20 Days_pH 2


600E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 10 20 30 40

Con

c (

mol

l)

Distance (m)

SiO2 Concentration

SiO2_NaOH SiO2_HCl

163

















3200

3700

4200

4700

5200

5700

6200

6700

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


NaOH_pH 12 HCl_pH 2

164
















631)



02

04

06

08

1

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

pH 2 Injection

CFS_Exp

TR_008_30

TR_01_30

TR_015_16

165













000

5000

10000

15000

20000

25000

30000

0 2 4 6 8 10

Per

mea

bil

ity

(m

D)

Distance (m)


166































167



















215

220

225

230

235

240

245

250

0 50 100 150 200 250 300

Pres

sure

(Bar

s)

Distance (m)



168

CHAPTER 7


71 Conclusion

























169











sandstones



















170































171








72 Recommendations


derived


















172








173
















174















175
















176















177















178















179















180















181















182

















183













Date2018



iii













iv





















been successful

v














151 Permeability 24

















vi

















321 Experiment 2 73

322 Experiment 3 77

323 Experiment 4 77

324 Experiment 5 78






41 Experiment 2 84

42 Experiment 3 86

43 Experiment 4 89

44 Experiment 5 95

45 Experiment 6a 98

46 Experiment 6b 99



vii

5 DISCUSSION 106














137



622 Model Setup 143









71 Conclusion 168




viii

GLOSSARY



mineralm3mineral)



medium)




ix




x



5


11



13



16



18


25


27


39


30


32


36


36



40



48


49



50


51

xi



52



53


58


60



72








85



88


88


90


91


92


93


94


96


96


97

xii


98


100


101


101


103


103


104


105


118


118


119


120


121


121


122


122


125


125


127

xiii


127


128


135


135


136


137


140


141


141


142


145




150


150


151

xiv


152


152


154


154


155


157


157


158


158


159


159


161


161


162


162


163


164


165


167

xv



21


27


44


45


46


55


55


59







94



114


114


116


117

xvi


140


145


146


146

1

CHAPTER 1




























2































3






of TOUGHREACT code

4


5




















6






























7


131 CO2 Injectivity



















for CCS









8


















methods














9































10
















reagent

11




12































13

















14




























15
















1412 Regular Mesh






16



right

1413 Polygonal Mesh




17




1414 Radial Mesh











18



















19












88+=] (13)


6 789 minus8

8+=]A$ (14)


6 789 minus8

8+=]AB$C (15)















20













solids and








in the model

$GH=05r (19)




saturation

21















A=λ n M Ao (110)






$ = D 7 JJK=1M

(111)






22








following equation










Q = QR (81emptyS)T










23









=$V

(114)











field scale









24

151 Permeability



























25















26









follows


(115)










Q = QRYDV (116)





27


Gleeson 2015)

Equation Equation

Number



Power

Law

Porosity

16 ampR m2 765e-17 153e-19 844e-23


Power

Law void

ratio

17 ampR m2 616e-17 154e-19 118e-21



28

















29


Cathles 1999)















30





amp Gleeson 2015)










31



















de1dS (121)











32










Chapter 2)



33








modelling resources














34

CHAPTER 2


Characterization


























35



















36





37









Reids Dome Beds

221 Reids Dome Beds














38













marine conditions

39




























40









41





















al 1969)









42






























43



















Sandstones










44



















Depth 105060 106230 106680 127500

Porosity () 32 65 86 61


Quart + Chert () 863 913 906 793

K-feldspar () 64 51 63 78


Mica () 03 - - -



45












in Baker 2008

Depth (m) 58888 94645

Porosity () 125 94





Mica () 03 03









46













Garnett et al 2013)



Depth 85454 91535 92022 94321 94376 94510

Porosity () 177 123 134 131 126 117

Permeability(mD) 330 520 322 321 121 080

Quart + Chert

()

881 757 751 849 817 806

K-feldspar () 50 149 130 78 107 88

Plagioclase () 07 39 45 21 27 33

Mica () - 03 - - - 03

Authigenic

Kaolin ()

27 11 07 50 51 28


47

















48

241 Sampling Sites















Mollan et al 1969)

49



50


Mount Catherine













51











52



al 1969)

53



1969)

54






























55



Sample Quartz

Wt

plusmn1

Kaolinite

Wt

plusmn1

Orthoclase

Wt plusmn1

Albite

Low

Wt

plusmn1

Muscovite

Wt plusmn1

Ammonio-

-Jarosite

Wt plusmn1

F1-1 81 7 1 2 9

F1-4 81 7 1 2 9

F4-2 81 7 1 2 9

F2-1 81 7 1 2 9

F2-3 81 7 1 2 9

I 1 63 9 5 4 18 2

I 2-1 62 6 3 4 24

NY-3 78 5 4 2 11

NY-4 72 10 5 1 12



Sample F1-3c

F2-1

F2-2b

(Fines)

wt sd wt sd wt sd


Quartz 652 1 672 04 - -




Kaolinite 227 03 139 02 81 55

Mica 45 05 37 0 18 12

56

















K-Feldspar Mica












57



Vb = ghij1ghkl

m (23)




Vb = π r2 h (24)





Vp = n]3o1n^pq

m (25)


Oslash = rsre

x 100 (26)










58










core F1-1

y = 13692x + 03846

Rsup2 = 0994

0

2

4

6

8

10

12

14

16

0 002 004 006 008 01 012

∆P

(p

si)

Q (ccmin)


(F1-1)

59



Sample

no

Length

(cm)

Porosity

()

Small

Chunk

Porosity

()

Core

Sample

Error Permeability

(mD)

Description

F1-1 99 2384 2325 +-01 0476 Good for exp



F1-5 63 - 23 +-08 13 Small

F2-1 15 2517 +-06 15 Sample broken


F2-2 144 - 242 +-06 495 Good for CFS exp


F4-1 206 - 217 - 150-500 Fines released






60


core F4-2















y = 00825x - 00375

Rsup2 = 09973

0

005

01

015

02

025

03

035

04

0 1 2 3 4 5 6

∆P

(psi

)

Q (ccmin)


(F4-2)

61







light

62








63








64









65








polarized light

66







67








68







69







70













deviations

71

CHAPTER 3




























72












73






















321 Experiment 2









74















Core Length

(cm)

Diameter

(cm)

Porosity

()

Permeability

(mD)

Pore Volume

(mL)

F2-2a 64 381 242 495 1766

F1-3a 6 381 2029 lt1 139

F1-3b1 51 381 1802 lt1 1046

F1-3b2 5 381 18 lt1 1026

F2-2b 52 381 242 1870 1435

75



76



77


322 Experiment 3












323 Experiment 4



78












324 Experiment 5





Core Conf

Pressure

(PSI)

Back

Pressure

(PSI)

oC

Form

Fluid

Injected

Fluid

pH Flow

Rate

mLmi

n

Stage 1 F1-3a 3000 2000 60 05M

NaCl

0001001

00001M

NaOH

1011

amp12

003

Stage 2 F1-3a 3000 2000 60 05 M

NaCl

0001001M

NaOH

10

12

003

Stage 3 F1-3a 3000 2000 60 05 M

NaCl

001M HCl 2 003

79











80





















81































82



















Standard

Concentration

[mgL]

1000

1000

1000

1000

1000

1000

1000

100

10


12mL of 2 HNO3

075mL each

element into

1275mL of 2

HNO3

Undiluted Undiluted

Calibration

Concentrations

[mgL]

50 20 10 350 075

50 20 10 350

075

100 50

30 10 2

10 5 3 1

02

83





84

CHAPTER 4


41 Experiment 2
























85




86




42 Experiment 3












20

25

30

35

40

45

0 20 40 60

silic

a (m

gl)

Hours

Experiment 2

87

















25degC 60degC



88




during experiment 3

0

2

4

6

8

10

12

14

0

15

30

45

60

75

90

105

120

0 20 40 60 80 100 120 140 160 180

pH

Con

c (

mg

l)

Hours

Experiment 3

SiAlCaFepH

pH Breakthrough

89

43 Experiment 4






















10 (Figure 431)








90













6

7

8

9

10

11

12

0

5

10

15

20

25

30

35

40

45

0 2 4 6 8 10 12 14

pH

Con

c (

mg

l)

Days


SiAlCaMgFeKpH

Stage 1a pH= 11

05M NaCl

Stage 1b pH= 12

05M NaCl

Stage 1c

pH= 101

05M NaCl

91



















6

7

8

9

10

11

12

0

10

20

30

40

50

60

14 16 18 20 22 24

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

Stage 2a

pH= 12

001M

NaCl

Stage 2b

pH= 10

05M NaCl Stage 2c

pH= 12

DI water

Stage 2d

pH= 75

05 M NaCl

92




















0

2

4

6

8

10

12

000

2000

4000

6000

8000

10000

12000

14000

30 32 34 36 38 40 42

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

pH= 2

001M HCl

93











01

014

018

022

026

03

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

Experiment 4

pH= 12

pH= 2pH= 75

pH= 11

Stage 2

Stage 1

Stage 3

94





Al 3054 mgL

Si 4968 mgL

K 048 mgL

Na+ 001375 moll

H+ 10e-12 moll

Fe Mg Ca 178e-6 mgL




respectively

-15

-10

-5

0

5

10

Quartz(SiO)

Chalcedony(SiO)

Kaolinite(AlSiO)

Boehmite(AlOH)

Gibbsite(AlOH)

Muscovite(KAlSiO)

FeO

Satu

ratio

n St

ates

(QK

)

Minerals


pH 2

pH 56

pH 12

95

44 Experiment 5




























experiment 5

96




(right)

97
















pH 4



98

45 Experiment 6a
















(Figure 451)



0

1

2

3

4

5

6

7

0

15

30

45

60

75

90

105

120

135

0 5 10

pH

Con

c (

mg

l)

Time (Days)

Exp 6a (pH 2)

AlCaFeKMgSipH

99

46 Experiment 6b






























100





101



-6

-5

-4

-3

-2

-1

0


Satu

ratio

n St

ates

(QK

)

Minerals


003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

-35

-3

-25

-2

-15

-1

-05

0

05


Satu

ratio

n St

ates

(QK

)

Minerals


0015mlmin(684min)

003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

102

47 Experiment 7a

















was below 1mgL

103






0

2

4

6

8

10

12

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30

pH

Con

c (

mg

l)

Hours

Experiment 7a_pH 12

Al

K

Si

pH


2 mlmin

-18

-16

-14

-12

-10

-8

-6

-4

-2

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7a_pH 12

05 mlmin(29min)

1 mlmin(14min)

2 mlmin(7min)

104

48 Experiment 7b
















0

2

4

6

8

10

12

0

10

20

30

40

50

60

0 20 40 60

pH

Con

c (

mg

l)

Hours

Experiment 7b_pH 2

Al

Ca

Fe

K

Mg

Si

pH

025 mlmin

0125 mlmin

05 mlmin1 mlmin

105




-25

-20

-15

-10

-5

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7b_pH 2

025mlmin(57min)

05 mlmin(29min)

1 mlmin(14min)

106

CHAPTER 5

5 DISCUSSION



























107





xylowast = (5)


















= (Sj)M (53)

108





weight of the core


























109

















Eq 53

110















pH rate




Quartz via Si


12 15e-12 61e-13

Kaolinite via Al


Ganor et al 1994

-

12 21e-15 98e-16

Muscovite via K



-

12 312e-16 21e-16

111































112































113


















be estimated

114




(mLmin)


4 (Stage 2a) 003 12 49 29 2

4 (Stage 2c) 003 12 49 29 2

4 (stage 3) 003 2 71 37 5

5 003 2 40 33 3

6a 003 2 44 28 15

6b 003 12 48 34 045



al 2015)

115






























later Section 513

116



















(mLmin)


7a

05

12

2165 95 05

1 11 59 025

2 76 385 0125

7b

025

2

79 64 07

05 395 32 035

1 2 165 025

117



















511)

118



rates

0

10

20

30

40

50

60

70

0 200 400 600 800

Silic

a (m

gl)


(Experiment 6b_Si)

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800

Alu

min

ium

(m

gl)



119


















0

2

4

6

8

10

12

0 10 20 30 40

Alu

min

ium

(m

gl)



120


















0

5

10

15

20

25

0 10 20 30 40

Silic

a (m

gl)



121



0

01

02

03

04

05

06

0 10 20 30 40

Pota

ssiu

m (

mg

l)



005

115

225

335

445

5

0 20 40 60 80

Alu

min

ium

(m

gl)



122



0

2

4

6

8

10

12

0 20 40 60 80

Sili

ca (m

gl)



0

01

02

03

04

05

06

07

08

0 20 40 60 80

Pota

ssiu

m (

mg

l)



123

514 Error Analysis





results

























124






















125





0

05

1

15

2

25

3

35

-01

0

01

02

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14

Si a

t pH

12

(mg

l)

Si a

t pH

2 (

mg

l)

pH


0

0002

0004

0006

0008

001

0001

001

01

1

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Quartz

126






























127







0

2

4

6

8

10

12

Kaolinite Muscovite

Surf

ace

Are

a (m

2 g)


XRD XRD+5 XRD+10

0

1

2

3

4

5

6

7

8

9

10

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14

Al a

t pH

12

(mg

l)

Al a

t pH

2 (

mg

l)

pH


128

















8

10

12

14

16

18

20

22

24

8

10

12

14

16

18

20

22

24

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Kaolinite

129




























laboratory scale

130































131




















experiment 5











132



















1)

133

CHAPTER 6




























134



























135



0

5

10

15

20

25

0 2 4 6 8 10 12 14 16 18 20 22 24

silic

a (m

gl)

Time (Hours)


Model_Si Exp_Si

012345678910

0

1

2

3

4

5

6

7

0 5 10 15 20 25

pH

alum

inum

(m

gl)

Time (Hours)



136



















(pH 12 injection)

0

10

20

30

40

50

0 2 4 6 8

silic

a (m

gl)

Time (Hours)


Exp_Si Model_Si

137


injection













7

8

9

10

11

12

13

0

2

4

6

8

10

12

14

16

0 2 4 6 8

pH

alum

inum

(m

gl)

Time (Hours)



138





mineral analysis


























139



























140








0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)





Weight Percent ()

Calcite 500 0025

Dolomite 4000 0050

Magnesite

500 0150

600 0180

141









0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)



142



















0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



143







622 Model Setup













al 2013)









144





88+=] (62)


88+=]A

$ (63)


88+=]AB

$C (64)




















145



2013)

146





Minerals A

(m2 g-1)

k25

(mol m2 s-1)

Ea

(KJ mol-1)


Albite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

Ankerite 0006 126e-9 6276 6457e-4 361 05 - - -

Anorthite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

K-feldspar 0006 389e-13 38 871e-11 517 05 631e-22 941 -0823

Quartz 0006 398e-14 218 - - - 513e-17 259 -05

Kaolinite 16 6918e-14 222 4898e-12 659 077 8913e-18 179 -0472

Muscovite 71 282e-14 22 14e-12 22 037 282e-15 22 -022

147




















148



149























within a few metres

150


injection




3000

3500

4000

4500

5000

5500

6000

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

pH

Distance



7

8

9

10

11

12

13

0 10 20 30 40

pH

Distance(m)


pH Drop

151


















-20

-15

-10

-5

0

5

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)



152




000E+00

500E-03

100E-02

150E-02

200E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



-160E-04

-140E-04

-120E-04

-100E-04

-800E-05

-600E-05

-400E-05

-200E-05

000E+00

0 5 10 15 20 25 30 35

∆V

olum

e Fr

actio

n

Distance (m)



153
























154





32

37

42

47

52

57

62

67

0 2 4 6 8

Perm

eabi

lity

(m

D)

Distance (m)




8

85

9

95

10

105

11

115

12

125

0 20 40 60 80

pH

Distance (m)



155


injection period

















119

1192

1194

1196

1198

12

1202

1204

1206

0 2 4 6 8

pH

Distance (m)


pH_30 days

pH_60 days

pH_90 days

pH_120 days

156

























permeability

157


injection



32

37

42

47

52

57

62

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


12 kgs

5 kgs

10 kgs

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

158





and dissolution

8

85

9

95

10

105

11

115

12

0 10 20 30 40 50 60 70 80 90

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

-27

-22

-17

-12

-7

-2

3

0 2 4 6 8 10

Ca

(mol

kg)

Satu

ratio

n In

dex

Distance (m)



159





000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

140E-02

160E-02

180E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



624E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Con

c (

mol

kg)

Distance (m)

SiO2 vs Inj Rates


160


























(Figure 6222)

161





0

1

2

3

4

5

6

7

8

9

30

31

32

33

34

35

36

37

38

0 5 10 15 20 25 30

pH

Perm

eabi

lity

(mD

)

Distance (m)


Permeability pH

-50

-40

-30

-20

-10

0

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)




162





000E+00

100E-03

200E-03

300E-03

400E-03

500E-03

600E-03

700E-03

-700E-04

-600E-04

-500E-04

-400E-04

-300E-04

-200E-04

-100E-04

000E+00

0 5 10 15 20 25 30

Vol

Fra

ctio

n (a

nker

ite)

∆V

olum

e Fr

actio

n

Distance (m)

20 Days_pH 2


600E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 10 20 30 40

Con

c (

mol

l)

Distance (m)

SiO2 Concentration

SiO2_NaOH SiO2_HCl

163

















3200

3700

4200

4700

5200

5700

6200

6700

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


NaOH_pH 12 HCl_pH 2

164
















631)



02

04

06

08

1

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

pH 2 Injection

CFS_Exp

TR_008_30

TR_01_30

TR_015_16

165













000

5000

10000

15000

20000

25000

30000

0 2 4 6 8 10

Per

mea

bil

ity

(m

D)

Distance (m)


166































167



















215

220

225

230

235

240

245

250

0 50 100 150 200 250 300

Pres

sure

(Bar

s)

Distance (m)



168

CHAPTER 7


71 Conclusion

























169











sandstones



















170































171








72 Recommendations


derived


















172








173
















174















175
















176















177















178















179















180















181















182

















183













Date2018



iv





















been successful

v














151 Permeability 24

















vi

















321 Experiment 2 73

322 Experiment 3 77

323 Experiment 4 77

324 Experiment 5 78






41 Experiment 2 84

42 Experiment 3 86

43 Experiment 4 89

44 Experiment 5 95

45 Experiment 6a 98

46 Experiment 6b 99



vii

5 DISCUSSION 106














137



622 Model Setup 143









71 Conclusion 168




viii

GLOSSARY



mineralm3mineral)



medium)




ix




x



5


11



13



16



18


25


27


39


30


32


36


36



40



48


49



50


51

xi



52



53


58


60



72








85



88


88


90


91


92


93


94


96


96


97

xii


98


100


101


101


103


103


104


105


118


118


119


120


121


121


122


122


125


125


127

xiii


127


128


135


135


136


137


140


141


141


142


145




150


150


151

xiv


152


152


154


154


155


157


157


158


158


159


159


161


161


162


162


163


164


165


167

xv



21


27


44


45


46


55


55


59







94



114


114


116


117

xvi


140


145


146


146

1

CHAPTER 1




























2































3






of TOUGHREACT code

4


5




















6






























7


131 CO2 Injectivity



















for CCS









8


















methods














9































10
















reagent

11




12































13

















14




























15
















1412 Regular Mesh






16



right

1413 Polygonal Mesh




17




1414 Radial Mesh











18



















19












88+=] (13)


6 789 minus8

8+=]A$ (14)


6 789 minus8

8+=]AB$C (15)















20













solids and








in the model

$GH=05r (19)




saturation

21















A=λ n M Ao (110)






$ = D 7 JJK=1M

(111)






22








following equation










Q = QR (81emptyS)T










23









=$V

(114)











field scale









24

151 Permeability



























25















26









follows


(115)










Q = QRYDV (116)





27


Gleeson 2015)

Equation Equation

Number



Power

Law

Porosity

16 ampR m2 765e-17 153e-19 844e-23


Power

Law void

ratio

17 ampR m2 616e-17 154e-19 118e-21



28

















29


Cathles 1999)















30





amp Gleeson 2015)










31



















de1dS (121)











32










Chapter 2)



33








modelling resources














34

CHAPTER 2


Characterization


























35



















36





37









Reids Dome Beds

221 Reids Dome Beds














38













marine conditions

39




























40









41





















al 1969)









42






























43



















Sandstones










44



















Depth 105060 106230 106680 127500

Porosity () 32 65 86 61


Quart + Chert () 863 913 906 793

K-feldspar () 64 51 63 78


Mica () 03 - - -



45












in Baker 2008

Depth (m) 58888 94645

Porosity () 125 94





Mica () 03 03









46













Garnett et al 2013)



Depth 85454 91535 92022 94321 94376 94510

Porosity () 177 123 134 131 126 117

Permeability(mD) 330 520 322 321 121 080

Quart + Chert

()

881 757 751 849 817 806

K-feldspar () 50 149 130 78 107 88

Plagioclase () 07 39 45 21 27 33

Mica () - 03 - - - 03

Authigenic

Kaolin ()

27 11 07 50 51 28


47

















48

241 Sampling Sites















Mollan et al 1969)

49



50


Mount Catherine













51











52



al 1969)

53



1969)

54






























55



Sample Quartz

Wt

plusmn1

Kaolinite

Wt

plusmn1

Orthoclase

Wt plusmn1

Albite

Low

Wt

plusmn1

Muscovite

Wt plusmn1

Ammonio-

-Jarosite

Wt plusmn1

F1-1 81 7 1 2 9

F1-4 81 7 1 2 9

F4-2 81 7 1 2 9

F2-1 81 7 1 2 9

F2-3 81 7 1 2 9

I 1 63 9 5 4 18 2

I 2-1 62 6 3 4 24

NY-3 78 5 4 2 11

NY-4 72 10 5 1 12



Sample F1-3c

F2-1

F2-2b

(Fines)

wt sd wt sd wt sd


Quartz 652 1 672 04 - -




Kaolinite 227 03 139 02 81 55

Mica 45 05 37 0 18 12

56

















K-Feldspar Mica












57



Vb = ghij1ghkl

m (23)




Vb = π r2 h (24)





Vp = n]3o1n^pq

m (25)


Oslash = rsre

x 100 (26)










58










core F1-1

y = 13692x + 03846

Rsup2 = 0994

0

2

4

6

8

10

12

14

16

0 002 004 006 008 01 012

∆P

(p

si)

Q (ccmin)


(F1-1)

59



Sample

no

Length

(cm)

Porosity

()

Small

Chunk

Porosity

()

Core

Sample

Error Permeability

(mD)

Description

F1-1 99 2384 2325 +-01 0476 Good for exp



F1-5 63 - 23 +-08 13 Small

F2-1 15 2517 +-06 15 Sample broken


F2-2 144 - 242 +-06 495 Good for CFS exp


F4-1 206 - 217 - 150-500 Fines released






60


core F4-2















y = 00825x - 00375

Rsup2 = 09973

0

005

01

015

02

025

03

035

04

0 1 2 3 4 5 6

∆P

(psi

)

Q (ccmin)


(F4-2)

61







light

62








63








64









65








polarized light

66







67








68







69







70













deviations

71

CHAPTER 3




























72












73






















321 Experiment 2









74















Core Length

(cm)

Diameter

(cm)

Porosity

()

Permeability

(mD)

Pore Volume

(mL)

F2-2a 64 381 242 495 1766

F1-3a 6 381 2029 lt1 139

F1-3b1 51 381 1802 lt1 1046

F1-3b2 5 381 18 lt1 1026

F2-2b 52 381 242 1870 1435

75



76



77


322 Experiment 3












323 Experiment 4



78












324 Experiment 5





Core Conf

Pressure

(PSI)

Back

Pressure

(PSI)

oC

Form

Fluid

Injected

Fluid

pH Flow

Rate

mLmi

n

Stage 1 F1-3a 3000 2000 60 05M

NaCl

0001001

00001M

NaOH

1011

amp12

003

Stage 2 F1-3a 3000 2000 60 05 M

NaCl

0001001M

NaOH

10

12

003

Stage 3 F1-3a 3000 2000 60 05 M

NaCl

001M HCl 2 003

79











80





















81































82



















Standard

Concentration

[mgL]

1000

1000

1000

1000

1000

1000

1000

100

10


12mL of 2 HNO3

075mL each

element into

1275mL of 2

HNO3

Undiluted Undiluted

Calibration

Concentrations

[mgL]

50 20 10 350 075

50 20 10 350

075

100 50

30 10 2

10 5 3 1

02

83





84

CHAPTER 4


41 Experiment 2
























85




86




42 Experiment 3












20

25

30

35

40

45

0 20 40 60

silic

a (m

gl)

Hours

Experiment 2

87

















25degC 60degC



88




during experiment 3

0

2

4

6

8

10

12

14

0

15

30

45

60

75

90

105

120

0 20 40 60 80 100 120 140 160 180

pH

Con

c (

mg

l)

Hours

Experiment 3

SiAlCaFepH

pH Breakthrough

89

43 Experiment 4






















10 (Figure 431)








90













6

7

8

9

10

11

12

0

5

10

15

20

25

30

35

40

45

0 2 4 6 8 10 12 14

pH

Con

c (

mg

l)

Days


SiAlCaMgFeKpH

Stage 1a pH= 11

05M NaCl

Stage 1b pH= 12

05M NaCl

Stage 1c

pH= 101

05M NaCl

91



















6

7

8

9

10

11

12

0

10

20

30

40

50

60

14 16 18 20 22 24

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

Stage 2a

pH= 12

001M

NaCl

Stage 2b

pH= 10

05M NaCl Stage 2c

pH= 12

DI water

Stage 2d

pH= 75

05 M NaCl

92




















0

2

4

6

8

10

12

000

2000

4000

6000

8000

10000

12000

14000

30 32 34 36 38 40 42

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

pH= 2

001M HCl

93











01

014

018

022

026

03

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

Experiment 4

pH= 12

pH= 2pH= 75

pH= 11

Stage 2

Stage 1

Stage 3

94





Al 3054 mgL

Si 4968 mgL

K 048 mgL

Na+ 001375 moll

H+ 10e-12 moll

Fe Mg Ca 178e-6 mgL




respectively

-15

-10

-5

0

5

10

Quartz(SiO)

Chalcedony(SiO)

Kaolinite(AlSiO)

Boehmite(AlOH)

Gibbsite(AlOH)

Muscovite(KAlSiO)

FeO

Satu

ratio

n St

ates

(QK

)

Minerals


pH 2

pH 56

pH 12

95

44 Experiment 5




























experiment 5

96




(right)

97
















pH 4



98

45 Experiment 6a
















(Figure 451)



0

1

2

3

4

5

6

7

0

15

30

45

60

75

90

105

120

135

0 5 10

pH

Con

c (

mg

l)

Time (Days)

Exp 6a (pH 2)

AlCaFeKMgSipH

99

46 Experiment 6b






























100





101



-6

-5

-4

-3

-2

-1

0


Satu

ratio

n St

ates

(QK

)

Minerals


003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

-35

-3

-25

-2

-15

-1

-05

0

05


Satu

ratio

n St

ates

(QK

)

Minerals


0015mlmin(684min)

003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

102

47 Experiment 7a

















was below 1mgL

103






0

2

4

6

8

10

12

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30

pH

Con

c (

mg

l)

Hours

Experiment 7a_pH 12

Al

K

Si

pH


2 mlmin

-18

-16

-14

-12

-10

-8

-6

-4

-2

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7a_pH 12

05 mlmin(29min)

1 mlmin(14min)

2 mlmin(7min)

104

48 Experiment 7b
















0

2

4

6

8

10

12

0

10

20

30

40

50

60

0 20 40 60

pH

Con

c (

mg

l)

Hours

Experiment 7b_pH 2

Al

Ca

Fe

K

Mg

Si

pH

025 mlmin

0125 mlmin

05 mlmin1 mlmin

105




-25

-20

-15

-10

-5

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7b_pH 2

025mlmin(57min)

05 mlmin(29min)

1 mlmin(14min)

106

CHAPTER 5

5 DISCUSSION



























107





xylowast = (5)


















= (Sj)M (53)

108





weight of the core


























109

















Eq 53

110















pH rate




Quartz via Si


12 15e-12 61e-13

Kaolinite via Al


Ganor et al 1994

-

12 21e-15 98e-16

Muscovite via K



-

12 312e-16 21e-16

111































112































113


















be estimated

114




(mLmin)


4 (Stage 2a) 003 12 49 29 2

4 (Stage 2c) 003 12 49 29 2

4 (stage 3) 003 2 71 37 5

5 003 2 40 33 3

6a 003 2 44 28 15

6b 003 12 48 34 045



al 2015)

115






























later Section 513

116



















(mLmin)


7a

05

12

2165 95 05

1 11 59 025

2 76 385 0125

7b

025

2

79 64 07

05 395 32 035

1 2 165 025

117



















511)

118



rates

0

10

20

30

40

50

60

70

0 200 400 600 800

Silic

a (m

gl)


(Experiment 6b_Si)

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800

Alu

min

ium

(m

gl)



119


















0

2

4

6

8

10

12

0 10 20 30 40

Alu

min

ium

(m

gl)



120


















0

5

10

15

20

25

0 10 20 30 40

Silic

a (m

gl)



121



0

01

02

03

04

05

06

0 10 20 30 40

Pota

ssiu

m (

mg

l)



005

115

225

335

445

5

0 20 40 60 80

Alu

min

ium

(m

gl)



122



0

2

4

6

8

10

12

0 20 40 60 80

Sili

ca (m

gl)



0

01

02

03

04

05

06

07

08

0 20 40 60 80

Pota

ssiu

m (

mg

l)



123

514 Error Analysis





results

























124






















125





0

05

1

15

2

25

3

35

-01

0

01

02

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14

Si a

t pH

12

(mg

l)

Si a

t pH

2 (

mg

l)

pH


0

0002

0004

0006

0008

001

0001

001

01

1

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Quartz

126






























127







0

2

4

6

8

10

12

Kaolinite Muscovite

Surf

ace

Are

a (m

2 g)


XRD XRD+5 XRD+10

0

1

2

3

4

5

6

7

8

9

10

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14

Al a

t pH

12

(mg

l)

Al a

t pH

2 (

mg

l)

pH


128

















8

10

12

14

16

18

20

22

24

8

10

12

14

16

18

20

22

24

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Kaolinite

129




























laboratory scale

130































131




















experiment 5











132



















1)

133

CHAPTER 6




























134



























135



0

5

10

15

20

25

0 2 4 6 8 10 12 14 16 18 20 22 24

silic

a (m

gl)

Time (Hours)


Model_Si Exp_Si

012345678910

0

1

2

3

4

5

6

7

0 5 10 15 20 25

pH

alum

inum

(m

gl)

Time (Hours)



136



















(pH 12 injection)

0

10

20

30

40

50

0 2 4 6 8

silic

a (m

gl)

Time (Hours)


Exp_Si Model_Si

137


injection













7

8

9

10

11

12

13

0

2

4

6

8

10

12

14

16

0 2 4 6 8

pH

alum

inum

(m

gl)

Time (Hours)



138





mineral analysis


























139



























140








0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)





Weight Percent ()

Calcite 500 0025

Dolomite 4000 0050

Magnesite

500 0150

600 0180

141









0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)



142



















0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



143







622 Model Setup













al 2013)









144





88+=] (62)


88+=]A

$ (63)


88+=]AB

$C (64)




















145



2013)

146





Minerals A

(m2 g-1)

k25

(mol m2 s-1)

Ea

(KJ mol-1)


Albite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

Ankerite 0006 126e-9 6276 6457e-4 361 05 - - -

Anorthite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

K-feldspar 0006 389e-13 38 871e-11 517 05 631e-22 941 -0823

Quartz 0006 398e-14 218 - - - 513e-17 259 -05

Kaolinite 16 6918e-14 222 4898e-12 659 077 8913e-18 179 -0472

Muscovite 71 282e-14 22 14e-12 22 037 282e-15 22 -022

147




















148



149























within a few metres

150


injection




3000

3500

4000

4500

5000

5500

6000

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

pH

Distance



7

8

9

10

11

12

13

0 10 20 30 40

pH

Distance(m)


pH Drop

151


















-20

-15

-10

-5

0

5

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)



152




000E+00

500E-03

100E-02

150E-02

200E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



-160E-04

-140E-04

-120E-04

-100E-04

-800E-05

-600E-05

-400E-05

-200E-05

000E+00

0 5 10 15 20 25 30 35

∆V

olum

e Fr

actio

n

Distance (m)



153
























154





32

37

42

47

52

57

62

67

0 2 4 6 8

Perm

eabi

lity

(m

D)

Distance (m)




8

85

9

95

10

105

11

115

12

125

0 20 40 60 80

pH

Distance (m)



155


injection period

















119

1192

1194

1196

1198

12

1202

1204

1206

0 2 4 6 8

pH

Distance (m)


pH_30 days

pH_60 days

pH_90 days

pH_120 days

156

























permeability

157


injection



32

37

42

47

52

57

62

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


12 kgs

5 kgs

10 kgs

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

158





and dissolution

8

85

9

95

10

105

11

115

12

0 10 20 30 40 50 60 70 80 90

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

-27

-22

-17

-12

-7

-2

3

0 2 4 6 8 10

Ca

(mol

kg)

Satu

ratio

n In

dex

Distance (m)



159





000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

140E-02

160E-02

180E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



624E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Con

c (

mol

kg)

Distance (m)

SiO2 vs Inj Rates


160


























(Figure 6222)

161





0

1

2

3

4

5

6

7

8

9

30

31

32

33

34

35

36

37

38

0 5 10 15 20 25 30

pH

Perm

eabi

lity

(mD

)

Distance (m)


Permeability pH

-50

-40

-30

-20

-10

0

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)




162





000E+00

100E-03

200E-03

300E-03

400E-03

500E-03

600E-03

700E-03

-700E-04

-600E-04

-500E-04

-400E-04

-300E-04

-200E-04

-100E-04

000E+00

0 5 10 15 20 25 30

Vol

Fra

ctio

n (a

nker

ite)

∆V

olum

e Fr

actio

n

Distance (m)

20 Days_pH 2


600E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 10 20 30 40

Con

c (

mol

l)

Distance (m)

SiO2 Concentration

SiO2_NaOH SiO2_HCl

163

















3200

3700

4200

4700

5200

5700

6200

6700

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


NaOH_pH 12 HCl_pH 2

164
















631)



02

04

06

08

1

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

pH 2 Injection

CFS_Exp

TR_008_30

TR_01_30

TR_015_16

165













000

5000

10000

15000

20000

25000

30000

0 2 4 6 8 10

Per

mea

bil

ity

(m

D)

Distance (m)


166































167



















215

220

225

230

235

240

245

250

0 50 100 150 200 250 300

Pres

sure

(Bar

s)

Distance (m)



168

CHAPTER 7


71 Conclusion

























169











sandstones



















170































171








72 Recommendations


derived


















172








173
















174















175
















176















177















178















179















180















181















182

















183













Date2018



v














151 Permeability 24

















vi

















321 Experiment 2 73

322 Experiment 3 77

323 Experiment 4 77

324 Experiment 5 78






41 Experiment 2 84

42 Experiment 3 86

43 Experiment 4 89

44 Experiment 5 95

45 Experiment 6a 98

46 Experiment 6b 99



vii

5 DISCUSSION 106














137



622 Model Setup 143









71 Conclusion 168




viii

GLOSSARY



mineralm3mineral)



medium)




ix




x



5


11



13



16



18


25


27


39


30


32


36


36



40



48


49



50


51

xi



52



53


58


60



72








85



88


88


90


91


92


93


94


96


96


97

xii


98


100


101


101


103


103


104


105


118


118


119


120


121


121


122


122


125


125


127

xiii


127


128


135


135


136


137


140


141


141


142


145




150


150


151

xiv


152


152


154


154


155


157


157


158


158


159


159


161


161


162


162


163


164


165


167

xv



21


27


44


45


46


55


55


59







94



114


114


116


117

xvi


140


145


146


146

1

CHAPTER 1




























2































3






of TOUGHREACT code

4


5




















6






























7


131 CO2 Injectivity



















for CCS









8


















methods














9































10
















reagent

11




12































13

















14




























15
















1412 Regular Mesh






16



right

1413 Polygonal Mesh




17




1414 Radial Mesh











18



















19












88+=] (13)


6 789 minus8

8+=]A$ (14)


6 789 minus8

8+=]AB$C (15)















20













solids and








in the model

$GH=05r (19)




saturation

21















A=λ n M Ao (110)






$ = D 7 JJK=1M

(111)






22








following equation










Q = QR (81emptyS)T










23









=$V

(114)











field scale









24

151 Permeability



























25















26









follows


(115)










Q = QRYDV (116)





27


Gleeson 2015)

Equation Equation

Number



Power

Law

Porosity

16 ampR m2 765e-17 153e-19 844e-23


Power

Law void

ratio

17 ampR m2 616e-17 154e-19 118e-21



28

















29


Cathles 1999)















30





amp Gleeson 2015)










31



















de1dS (121)











32










Chapter 2)



33








modelling resources














34

CHAPTER 2


Characterization


























35



















36





37









Reids Dome Beds

221 Reids Dome Beds














38













marine conditions

39




























40









41





















al 1969)









42






























43



















Sandstones










44



















Depth 105060 106230 106680 127500

Porosity () 32 65 86 61


Quart + Chert () 863 913 906 793

K-feldspar () 64 51 63 78


Mica () 03 - - -



45












in Baker 2008

Depth (m) 58888 94645

Porosity () 125 94





Mica () 03 03









46













Garnett et al 2013)



Depth 85454 91535 92022 94321 94376 94510

Porosity () 177 123 134 131 126 117

Permeability(mD) 330 520 322 321 121 080

Quart + Chert

()

881 757 751 849 817 806

K-feldspar () 50 149 130 78 107 88

Plagioclase () 07 39 45 21 27 33

Mica () - 03 - - - 03

Authigenic

Kaolin ()

27 11 07 50 51 28


47

















48

241 Sampling Sites















Mollan et al 1969)

49



50


Mount Catherine













51











52



al 1969)

53



1969)

54






























55



Sample Quartz

Wt

plusmn1

Kaolinite

Wt

plusmn1

Orthoclase

Wt plusmn1

Albite

Low

Wt

plusmn1

Muscovite

Wt plusmn1

Ammonio-

-Jarosite

Wt plusmn1

F1-1 81 7 1 2 9

F1-4 81 7 1 2 9

F4-2 81 7 1 2 9

F2-1 81 7 1 2 9

F2-3 81 7 1 2 9

I 1 63 9 5 4 18 2

I 2-1 62 6 3 4 24

NY-3 78 5 4 2 11

NY-4 72 10 5 1 12



Sample F1-3c

F2-1

F2-2b

(Fines)

wt sd wt sd wt sd


Quartz 652 1 672 04 - -




Kaolinite 227 03 139 02 81 55

Mica 45 05 37 0 18 12

56

















K-Feldspar Mica












57



Vb = ghij1ghkl

m (23)




Vb = π r2 h (24)





Vp = n]3o1n^pq

m (25)


Oslash = rsre

x 100 (26)










58










core F1-1

y = 13692x + 03846

Rsup2 = 0994

0

2

4

6

8

10

12

14

16

0 002 004 006 008 01 012

∆P

(p

si)

Q (ccmin)


(F1-1)

59



Sample

no

Length

(cm)

Porosity

()

Small

Chunk

Porosity

()

Core

Sample

Error Permeability

(mD)

Description

F1-1 99 2384 2325 +-01 0476 Good for exp



F1-5 63 - 23 +-08 13 Small

F2-1 15 2517 +-06 15 Sample broken


F2-2 144 - 242 +-06 495 Good for CFS exp


F4-1 206 - 217 - 150-500 Fines released






60


core F4-2















y = 00825x - 00375

Rsup2 = 09973

0

005

01

015

02

025

03

035

04

0 1 2 3 4 5 6

∆P

(psi

)

Q (ccmin)


(F4-2)

61







light

62








63








64









65








polarized light

66







67








68







69







70













deviations

71

CHAPTER 3




























72












73






















321 Experiment 2









74















Core Length

(cm)

Diameter

(cm)

Porosity

()

Permeability

(mD)

Pore Volume

(mL)

F2-2a 64 381 242 495 1766

F1-3a 6 381 2029 lt1 139

F1-3b1 51 381 1802 lt1 1046

F1-3b2 5 381 18 lt1 1026

F2-2b 52 381 242 1870 1435

75



76



77


322 Experiment 3












323 Experiment 4



78












324 Experiment 5





Core Conf

Pressure

(PSI)

Back

Pressure

(PSI)

oC

Form

Fluid

Injected

Fluid

pH Flow

Rate

mLmi

n

Stage 1 F1-3a 3000 2000 60 05M

NaCl

0001001

00001M

NaOH

1011

amp12

003

Stage 2 F1-3a 3000 2000 60 05 M

NaCl

0001001M

NaOH

10

12

003

Stage 3 F1-3a 3000 2000 60 05 M

NaCl

001M HCl 2 003

79











80





















81































82



















Standard

Concentration

[mgL]

1000

1000

1000

1000

1000

1000

1000

100

10


12mL of 2 HNO3

075mL each

element into

1275mL of 2

HNO3

Undiluted Undiluted

Calibration

Concentrations

[mgL]

50 20 10 350 075

50 20 10 350

075

100 50

30 10 2

10 5 3 1

02

83





84

CHAPTER 4


41 Experiment 2
























85




86




42 Experiment 3












20

25

30

35

40

45

0 20 40 60

silic

a (m

gl)

Hours

Experiment 2

87

















25degC 60degC



88




during experiment 3

0

2

4

6

8

10

12

14

0

15

30

45

60

75

90

105

120

0 20 40 60 80 100 120 140 160 180

pH

Con

c (

mg

l)

Hours

Experiment 3

SiAlCaFepH

pH Breakthrough

89

43 Experiment 4






















10 (Figure 431)








90













6

7

8

9

10

11

12

0

5

10

15

20

25

30

35

40

45

0 2 4 6 8 10 12 14

pH

Con

c (

mg

l)

Days


SiAlCaMgFeKpH

Stage 1a pH= 11

05M NaCl

Stage 1b pH= 12

05M NaCl

Stage 1c

pH= 101

05M NaCl

91



















6

7

8

9

10

11

12

0

10

20

30

40

50

60

14 16 18 20 22 24

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

Stage 2a

pH= 12

001M

NaCl

Stage 2b

pH= 10

05M NaCl Stage 2c

pH= 12

DI water

Stage 2d

pH= 75

05 M NaCl

92




















0

2

4

6

8

10

12

000

2000

4000

6000

8000

10000

12000

14000

30 32 34 36 38 40 42

pH

Con

c (

mg

l)

Days


Si

Al

Ca

Mg

Fe

K

pH

pH= 2

001M HCl

93











01

014

018

022

026

03

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

Experiment 4

pH= 12

pH= 2pH= 75

pH= 11

Stage 2

Stage 1

Stage 3

94





Al 3054 mgL

Si 4968 mgL

K 048 mgL

Na+ 001375 moll

H+ 10e-12 moll

Fe Mg Ca 178e-6 mgL




respectively

-15

-10

-5

0

5

10

Quartz(SiO)

Chalcedony(SiO)

Kaolinite(AlSiO)

Boehmite(AlOH)

Gibbsite(AlOH)

Muscovite(KAlSiO)

FeO

Satu

ratio

n St

ates

(QK

)

Minerals


pH 2

pH 56

pH 12

95

44 Experiment 5




























experiment 5

96




(right)

97
















pH 4



98

45 Experiment 6a
















(Figure 451)



0

1

2

3

4

5

6

7

0

15

30

45

60

75

90

105

120

135

0 5 10

pH

Con

c (

mg

l)

Time (Days)

Exp 6a (pH 2)

AlCaFeKMgSipH

99

46 Experiment 6b






























100





101



-6

-5

-4

-3

-2

-1

0


Satu

ratio

n St

ates

(QK

)

Minerals


003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

-35

-3

-25

-2

-15

-1

-05

0

05


Satu

ratio

n St

ates

(QK

)

Minerals


0015mlmin(684min)

003mlmin(342min)

006mlmin(171min)

012mlmin(85min)

024mlmin(43min)

102

47 Experiment 7a

















was below 1mgL

103






0

2

4

6

8

10

12

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30

pH

Con

c (

mg

l)

Hours

Experiment 7a_pH 12

Al

K

Si

pH


2 mlmin

-18

-16

-14

-12

-10

-8

-6

-4

-2

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7a_pH 12

05 mlmin(29min)

1 mlmin(14min)

2 mlmin(7min)

104

48 Experiment 7b
















0

2

4

6

8

10

12

0

10

20

30

40

50

60

0 20 40 60

pH

Con

c (

mg

l)

Hours

Experiment 7b_pH 2

Al

Ca

Fe

K

Mg

Si

pH

025 mlmin

0125 mlmin

05 mlmin1 mlmin

105




-25

-20

-15

-10

-5

0


Satu

rati

on S

tate

s (Q

K)

Minerals

Experiment 7b_pH 2

025mlmin(57min)

05 mlmin(29min)

1 mlmin(14min)

106

CHAPTER 5

5 DISCUSSION



























107





xylowast = (5)


















= (Sj)M (53)

108





weight of the core


























109

















Eq 53

110















pH rate




Quartz via Si


12 15e-12 61e-13

Kaolinite via Al


Ganor et al 1994

-

12 21e-15 98e-16

Muscovite via K



-

12 312e-16 21e-16

111































112































113


















be estimated

114




(mLmin)


4 (Stage 2a) 003 12 49 29 2

4 (Stage 2c) 003 12 49 29 2

4 (stage 3) 003 2 71 37 5

5 003 2 40 33 3

6a 003 2 44 28 15

6b 003 12 48 34 045



al 2015)

115






























later Section 513

116



















(mLmin)


7a

05

12

2165 95 05

1 11 59 025

2 76 385 0125

7b

025

2

79 64 07

05 395 32 035

1 2 165 025

117



















511)

118



rates

0

10

20

30

40

50

60

70

0 200 400 600 800

Silic

a (m

gl)


(Experiment 6b_Si)

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800

Alu

min

ium

(m

gl)



119


















0

2

4

6

8

10

12

0 10 20 30 40

Alu

min

ium

(m

gl)



120


















0

5

10

15

20

25

0 10 20 30 40

Silic

a (m

gl)



121



0

01

02

03

04

05

06

0 10 20 30 40

Pota

ssiu

m (

mg

l)



005

115

225

335

445

5

0 20 40 60 80

Alu

min

ium

(m

gl)



122



0

2

4

6

8

10

12

0 20 40 60 80

Sili

ca (m

gl)



0

01

02

03

04

05

06

07

08

0 20 40 60 80

Pota

ssiu

m (

mg

l)



123

514 Error Analysis





results

























124






















125





0

05

1

15

2

25

3

35

-01

0

01

02

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14

Si a

t pH

12

(mg

l)

Si a

t pH

2 (

mg

l)

pH


0

0002

0004

0006

0008

001

0001

001

01

1

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Quartz

126






























127







0

2

4

6

8

10

12

Kaolinite Muscovite

Surf

ace

Are

a (m

2 g)


XRD XRD+5 XRD+10

0

1

2

3

4

5

6

7

8

9

10

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14

Al a

t pH

12

(mg

l)

Al a

t pH

2 (

mg

l)

pH


128

















8

10

12

14

16

18

20

22

24

8

10

12

14

16

18

20

22

24

0 2 4 6 8 10 12 14

ESA

_pH

12

(m2

g)

ESA

_pH

2 (

m2

g)

pH

ESA_Kaolinite

129




























laboratory scale

130































131




















experiment 5











132



















1)

133

CHAPTER 6




























134



























135



0

5

10

15

20

25

0 2 4 6 8 10 12 14 16 18 20 22 24

silic

a (m

gl)

Time (Hours)


Model_Si Exp_Si

012345678910

0

1

2

3

4

5

6

7

0 5 10 15 20 25

pH

alum

inum

(m

gl)

Time (Hours)



136



















(pH 12 injection)

0

10

20

30

40

50

0 2 4 6 8

silic

a (m

gl)

Time (Hours)


Exp_Si Model_Si

137


injection













7

8

9

10

11

12

13

0

2

4

6

8

10

12

14

16

0 2 4 6 8

pH

alum

inum

(m

gl)

Time (Hours)



138





mineral analysis


























139



























140








0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)





Weight Percent ()

Calcite 500 0025

Dolomite 4000 0050

Magnesite

500 0150

600 0180

141









0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



0

20

40

60

80

100

120

140

160

0 2 4 6 8

calc

ium

(m

gl

)

Time (Days)



142



















0

20

40

60

80

100

0 2 4 6 8

ma

gn

esiu

m (

mg

l)

Time (Days)



143







622 Model Setup













al 2013)









144





88+=] (62)


88+=]A

$ (63)


88+=]AB

$C (64)




















145



2013)

146





Minerals A

(m2 g-1)

k25

(mol m2 s-1)

Ea

(KJ mol-1)


Albite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

Ankerite 0006 126e-9 6276 6457e-4 361 05 - - -

Anorthite 0006 2754e-13 698 6918e-11 65 0457 2512e-16 71 -0572

K-feldspar 0006 389e-13 38 871e-11 517 05 631e-22 941 -0823

Quartz 0006 398e-14 218 - - - 513e-17 259 -05

Kaolinite 16 6918e-14 222 4898e-12 659 077 8913e-18 179 -0472

Muscovite 71 282e-14 22 14e-12 22 037 282e-15 22 -022

147




















148



149























within a few metres

150


injection




3000

3500

4000

4500

5000

5500

6000

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

pH

Distance



7

8

9

10

11

12

13

0 10 20 30 40

pH

Distance(m)


pH Drop

151


















-20

-15

-10

-5

0

5

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)



152




000E+00

500E-03

100E-02

150E-02

200E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



-160E-04

-140E-04

-120E-04

-100E-04

-800E-05

-600E-05

-400E-05

-200E-05

000E+00

0 5 10 15 20 25 30 35

∆V

olum

e Fr

actio

n

Distance (m)



153
























154





32

37

42

47

52

57

62

67

0 2 4 6 8

Perm

eabi

lity

(m

D)

Distance (m)




8

85

9

95

10

105

11

115

12

125

0 20 40 60 80

pH

Distance (m)



155


injection period

















119

1192

1194

1196

1198

12

1202

1204

1206

0 2 4 6 8

pH

Distance (m)


pH_30 days

pH_60 days

pH_90 days

pH_120 days

156

























permeability

157


injection



32

37

42

47

52

57

62

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


12 kgs

5 kgs

10 kgs

118

1185

119

1195

12

1205

121

0 2 4 6 8 10

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

158





and dissolution

8

85

9

95

10

105

11

115

12

0 10 20 30 40 50 60 70 80 90

pH

Distance (m)


20days(12kgs)

20days(5kgs)

20days(10kgs)

000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

-27

-22

-17

-12

-7

-2

3

0 2 4 6 8 10

Ca

(mol

kg)

Satu

ratio

n In

dex

Distance (m)



159





000E+00

200E-03

400E-03

600E-03

800E-03

100E-02

120E-02

140E-02

160E-02

180E-02

0 2 4 6 8 10 12 14 16 18 20

Vol

ume

Frac

tion

()

Distance (m)



624E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Con

c (

mol

kg)

Distance (m)

SiO2 vs Inj Rates


160


























(Figure 6222)

161





0

1

2

3

4

5

6

7

8

9

30

31

32

33

34

35

36

37

38

0 5 10 15 20 25 30

pH

Perm

eabi

lity

(mD

)

Distance (m)


Permeability pH

-50

-40

-30

-20

-10

0

10

0 2 4 6 8 10

Satu

ratio

n In

dex

Distance (m)




162





000E+00

100E-03

200E-03

300E-03

400E-03

500E-03

600E-03

700E-03

-700E-04

-600E-04

-500E-04

-400E-04

-300E-04

-200E-04

-100E-04

000E+00

0 5 10 15 20 25 30

Vol

Fra

ctio

n (a

nker

ite)

∆V

olum

e Fr

actio

n

Distance (m)

20 Days_pH 2


600E-05

506E-03

101E-02

151E-02

201E-02

251E-02

301E-02

0 10 20 30 40

Con

c (

mol

l)

Distance (m)

SiO2 Concentration

SiO2_NaOH SiO2_HCl

163

















3200

3700

4200

4700

5200

5700

6200

6700

0 2 4 6 8 10

Perm

eabi

lity

(mD

)

Distance (m)


NaOH_pH 12 HCl_pH 2

164
















631)



02

04

06

08

1

0 10 20 30 40

Perm

eabi

lity

(mD

)

Days

pH 2 Injection

CFS_Exp

TR_008_30

TR_01_30

TR_015_16

165













000

5000

10000

15000

20000

25000

30000

0 2 4 6 8 10

Per

mea

bil

ity

(m

D)

Distance (m)


166































167



















215

220

225

230

235

240

245

250

0 50 100 150 200 250 300

Pres

sure

(Bar

s)

Distance (m)



168

CHAPTER 7


71 Conclusion

























169











sandstones



















170































171








72 Recommendations


derived


















172








173
















174















175
















176















177















178















179















180















181















182

















183













Date2018



Determining the Effective Surface Area of Minerals and the ...

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Transcript of Determining the Effective Surface Area of Minerals and the ...