Droplet Microfluidics for Additive Screening in Enhanced Oil Recovery · 2018. 1. 7. · Pushan...

Droplet Microfluidics for Additive Screening in Enhanced OilRecovery

by

Pushan Lele

A thesis submitted in conformity with the requirementsfor the degree of Master of Applied Science

Graduate Department of Mechanical EngineeringUniversity of Toronto

© Copyright 2015 by Pushan Lele

Abstract

Droplet Microfluidics for Additive Screening in Enhanced Oil Recovery

Pushan Lele

Master of Applied Science

Graduate Department of Mechanical Engineering

University of Toronto

2015

Enhanced oil recovery is a set of methods used to increase the productivity of a reservoir after it

is not possible to economically produce oil using hydrostatic reservoir pressure, artificial lift devices,

waterfloods or gas floods. Droplet microfluidics, the study and utilization of ordered multiphase flows

in closed microchannels, is predominantly used for biological or chemical reaction screening and particle

fabrication. This thesis focuses on using droplet microfluidics to inform enhanced oil recovery. A platform

was developed to assess the effect of a set of additives on droplet deformation. Droplets were found to

become more deformable with an increase in pH and less deformable with an increase in ionic strength

and salinity. These trends agree with behaviour reported in the literature.

ii

Acknowledgements

I owe a debt of gratitude to my supervisor, Prof. David Sinton, for the opportunity to work on this

project. Thank you for taking a chance on me.

I am thankful to Dr. Nader Mosavat and Dr. Jason Riordon for detailed reviews of early versions of

this thesis and for their invaluable feedback and insights. Thanks as well to Abdulhaseeb Syed. It was

great troubleshooting problems with you. I am also thankful to Phong Nguyen and Wen Song’s chip

fabrication insights.

I would like to thank Dr. Ladislav Derzsi and Dr. Piotr Garstecki for helpful email discussions.

The staff at TNFC-Pratt, specifically Dr. Edward Huaping Xu and Harlan Kuntz, have been most

helpful in providing fabrication advice and support and most patient with respect to my misadventures.

For this I am grateful.

I would also like to thank Ryan Mendell and the MC-78 machine shop for their fabrication advice.

Finally, I would like to thank my family for their support.

iii

Contents

1 Foreword 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Introduction 3

2.1 Relevant Petroleum Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Enhanced Oil Recovery (EOR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2.1 Capillary number (Ca) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.2 Mobility Ratio (M) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.3 Reservoir Wettability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.4 Chemical Flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.5 Environmental Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Microfluidics for Petroleum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.4 Digital/Droplet Microfluidics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 Droplet Microfluidics for Additive Assessment 9

3.1 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.2 Chip Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.3 Surface Modification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.4 Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.5 Chip Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.1 Alkalinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.2 Alkaline, Surfactant and Salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.3 Sodium Hydroxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2.4 Non-chemical Contributions to Deformation . . . . . . . . . . . . . . . . . . . . . . 16

3.2.5 Temperature and Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Platform Development 19

4.1 Manifold Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Chip Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2.1 Iteration 0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

iv

4.2.2 Iteration 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2.3 Iteration 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.4 Iteration 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.5 Iteration 4(a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.2.6 Iteration 4(b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.2.7 Iteration 5(a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.2.8 Iteration 5(b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5 Conclusion and Future Directions 29

Bibliography 30

A A note on etching BF33/Pyrex 7740 41

B A note on silanization 42

C Manifolds 43

D Image Analysis Scripts 44

E Sample Resistance Calculation 45

v

List of Figures

2.1 Wet vs. Non-wet surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Common flow focusing geometries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1 System schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Surface preparation workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Chip schematic and expander dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.4 Chip intersection schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.5 Droplet profiles after entering expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.6 Droplet deformation as a function of pH with additive R . . . . . . . . . . . . . . . . . . . 16

3.7 Comparison of droplet deformation due to alkaline, surfactant and salt . . . . . . . . . . . 17

3.8 Droplet velocities and areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.1 Iteration 0 intersection schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2 Response of peak hydraulic diameter to additive R . . . . . . . . . . . . . . . . . . . . . . 20

4.3 Stable water in oil droplet generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.4 Iteration 1 Chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.5 Iteration 2 Chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.6 Iteration 2 intersection schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.7 Iteration 2 droplet generator exhibiting uneven wetting . . . . . . . . . . . . . . . . . . . 23

4.8 Iteration 3 - stable droplet generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.9 Iteration 4(a) chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.10 Iteration 4 Chips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.11 Iteration 4(b)(i) intersection schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.12 Iteration 4(b)(ii) intersection schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.13 Iteration 5(a) Chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.14 Iteration 5(a) intersection schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

vi

Nomenclature

API American Petroleum Institute

ASP Alkaline-surfactant-polymer

ASTM Formerly the American Society for Testing and Materials

CHOPS Cold heavy oil production with sand

DEP Dielectrophoresis

EOR Enhanced oil recovery

EWD Electrowetting on a dielectric

FDTS Perfluorodecyltrichlorosilane

GOR Gas-to-oil ratio

HTS High throughput screening

IFT Interfacial tension

IPA Isopropyl alcohol

MEOR Microbial enhanced oil recovery

NA Napthenic Acid(s)

PAH Polyaromatic hydrocarbon(s)

PVT Pressure-volume-temperature

SAGD Steam-assisted gravity drainage

SAM Self-assembled monolayer

SOR Steam-to-oil ratio

TAN Total acid number

TIC Temperature indicator and controller (P&ID symbol)

TIOM Toluene insoluble organic matter

vii

Chapter 1

Foreword

1.1 Motivation

The consumption of conventional oil outpaces its discovery by roughly three times[1]. As a result, over the

last decade, the exploitation of unconventional oil resources has grown in importance. Unconventional

oils are challenging to produce and refine due to lower reservoir permeabilities, higher fluid viscosities,

densities, acid, metal and asphaltene content than conventional oil[2, 3]. Due to the nature of the

resource and partly due to the limits of currently available technology, unconventional oils also tend to

have lower energy returned on energy invested (EROEI)[4].

Significant cost is incurred in performing fluid property tests during the development and production

phases. Samples (on the order of litres) need to be periodically transported under reservoir conditions

from the wellhead to the nearest laboratory. In the laboratory, a pressure-volume-temperature (PVT)

cell is required for property testing at reservoir relevant conditions. This equipment is bulky and it takes

several hours[5] for PVT cells to settle at the temperature/pressure setting of interest to collect a single

data point.

Microfluidic reactors are being actively developed for high pressure chemical synthesis[6]. An op-

portunity exists to transfer this technology to the petroleum research space and leverage its strengths

(e.g. low sample requirements, rapid equilibriation) to decrease costs of reservoir fluid property testing.

Reduction in testing time will speed up the optimization of production planning. Any improvements in

recovery efficiency will be accompanied by reductions in emissions. Work in this vein is already under

way[7, 8, 9, 10, 11, 12, 13], however there are no reports of a microfluidic method to assess the interfacial

tension between oleic and aqueous phases as a function of additives.

1

Chapter 1. Foreword 2

1.2 Thesis Overview

The objective of this thesis was to develop a fast droplet microfluidic method for the assessment of

additives for enhanced unconventional oil recovery.

Chapter 2 is a brief overview of topics relevant to this project. Enhanced oil recovery and relevant

petroleum properties, work done to date on petroleum microfluidics and droplet microfluidics are

introduced.

Chapter 3 is an adaptation of a manuscript under preparation for submission to Lab on a Chip. A

flow focusing chip is used to generate water in heavy oil emulsions. The emulsions are sent through

an expansion and deformation is observed and used as a qualitative proxy for interfacial tension.

Results are presented and discussed for tests with alkaline additives, a surfactant and brine.

Chapter 4 is an overview of the platform development process. Manifold and chip iterations are

summarized.

Chapter 5 presents conclusions and suggests future directions for further applications of these

findings.

Appendix A is a note on factors affecting glass etching.

Appendix B is a note on silane coating.

Appendix C contains schematics for high pressure manifolds.

Appendix D contains Fiji/ImageJ, MATLAB scripts.

Appendix E contains a sample resistance calculation

Chapter 2

Introduction

This chapter is a brief overview of concepts relevant to oil recovery and discrete microfluidics. Oil

properties and evaluation metrics, non-dimensional numbers, enhanced oil recovery mechanisms, and

petroleum microfluidics are discussed.

2.1 Relevant Petroleum Properties

The API gravity scale is a measure of the relative density of a given oil. It is defined as follows:

API =141.5

SG− 131.5 (2.1)

A fluid as dense as water is 10°API. Unconventional oils are lower than 10°API[2] and have high

viscosities at reservoir conditions[3]. After density and viscosity, the properties that are most important

for enhanced oil recovery are the interfacial tension (IFT) and the Total Acid Number (TAN). The total

acid number is defined as the amount of KOH (in mg) require to neutralize 1 g of oil.

2.2 Enhanced Oil Recovery (EOR)

Primary recovery refers to oil that is extracted by hydrostatic reservoir pressure or by using pumps

(e.g. pumpjacks). Secondary recovery is oil extraction by a subsequent water or gas flood, after which

about 60-80% of the original oil in place remains[14]. Oil characteristics such as viscosity and fluid

interaction parameters such as IFT remain unchanged during primary and secondary recovery phases.

Tertiary recovery, or Enhanced Oil Recovery (EOR), targets the residual oil which is trapped at the

end of secondary recovery. There are three main categories of EOR: thermal, gas injection and chemical

injection. In Alberta, primary and secondary recovery is not possible for a large portion of unconventional

resource i.e. oil sands, and thermal recovery (predominantly Steam Assisted Gravity Drainage - SAGD).

is the only economic means of extraction available. In Saskatchewan, primary recovery is possible for

high-pressure reserves in the form of Cold Heavy Oil Production with Sand (CHOPS). However, primary

recovery extracts 10% - 12% of the original oil in place[3, 15] and secondary and tertiary recovery are

necessary.

Fluid displacement in the reservoir and oil recovery is strongly influenced by three parameters:

3

Chapter 2. Introduction 4

1. Capillary number (Ca)[16]

2. Mobility ratio (M)[16, 17]

3. Reservoir wettability

All three can be altered using EOR methods.

2.2.1 Capillary number (Ca)

The capillary number is defined as

Ca =vµ

γ(2.2)

where v is the Darcy velocity, µ is the displacement fluid viscosity and γ is the interfacial tension between

the two phases. The trend observed in reservoir engineering is that residual oil decreases as the capillary

number increases[16, 18].

2.2.2 Mobility Ratio (M)

The mobility ratio, as the name suggests, is defined as the ratio of mobilities of the displacing fluid to

the displaced fluid:

M =λingλed

(2.3)

where ing refers to the displacing fluid and ed refers to the displaced fluid.

The mobility of a fluid is defined as:

λi =kiµi

(2.4)

where i refers to the fluid of interest (e.g. oil or water), k is the effective permeability and is the viscosity

of a given fluid. A mobility ratio of close to 1 is favourable. If M � 1, the displacing fluid will bypassoil through substantial fingering phenomenon leading to poor sweep efficiency and a lower oil recovery

factor.

2.2.3 Reservoir Wettability

Wettability of the reservoir surface greatly affects extraction. Wettability describes the preference of the

interior surfaces of a formation to be in contact with a given fluid[19]. The contact angle between the

fluid interface and the solid surface at the base of a droplet on a solid is used to compare the wettability

of different fluids.

As seen in Figure 2.1, a fluid is said to wet the surface if the contact angle is between 0°and 70°.

Between 70°and 110°, the substrate is considered mixed wet and for angles between 110°and 180°, the

substrate is considered non-wetting. Although reservoirs are often classified as oil-wet, water-wet or

mixed-wet, in reality the two extreme cases ( = 0°, and = 180°) are rarely observed, and most solid-

liquid systems exhibit intermediate behaviour. While the favoured fluid may form thin films on the

surface(s), it is possible for the pores of the formation to be saturated by the non-wetting fluid[19].

In addition to local surface composition, wettability is a function of system temperature, pressure,

formation oil chemical composition, formation water chemistry, and saturation history. Oil composition

plays a large part in changing the wettability of a formation, since oil contains fractions (asphaltenes


Figure 2.1: (a) Wet vs. (b) Non-wet surface[20]

and resins) that can act as surfactants. These surfactants can alter the pore surface to favour oil if they

are allowed to precipitate[21] (e.g under specific salinity and pH conditions[19]).

2.2.4 Chemical Flooding

The capillary number can be increased by increasing the viscosity of the waterflood by injecting a polymer

solution (polymer flooding). If the oil has a high TAN, a base may be injected to generate surfactants

in situ by neutralizing acids present in petroleum[22]. Alternatively, surfactant may be injected to

reduce IFT directly. An optimal approach involving all three components is often found to be the most

economical. This is known as Alkaline-Surfactant-Polymer (ASP) flooding[23]. At low IFT values, there

will be greater emulsification of oil in water. This is beneficial in the context of heavy oil because the

effective viscosity of the oil is reduced after emulsification which makes entrainment easier. Additionally,

emulsions can block pores along high permeability/low resistance paths which are often cleared by

waterfloods. Blockage of these paths diverts water and emulsion flow to lower permeability/higher

resistance paths[24]. Thus, entrapment improves the mobility ratio/sweep. Further, alkaline/surfactant

additives can alter the wettability of the reservoir through adsorbing/desorbing surfactants.

2.2.5 Environmental Impact

In Canadian heavy oil and bitumen recovery, water inputs, energy and emissions intensity have the

greatest environmental impact[25]. Process water is recycled to the greatest extent possible, but ap-

proximately 10% make-up water is required from an external source[26, 27]. Energy and emissions

intensities are directly related to cold water requirements. Energy is needed to generate steam as well

as to purify produced water to a state acceptable as feedwater for steam generators. Heavy oil projects

are assessed using the steam-to-oil ratio (SOR) and CO2 emissions per unit energy produced. Typical

values for the SOR and CO2 emissions intensity per unit net energy produced are 2.8 m3/m3 and 20

g/MJ respectively[28]. Both can be much higher in challenging reservoirs and in the early stages of the

life of a well pair. A combination of chemical and thermal EOR has the potential to reduce SORs and

CO2 intensities by producing more oil with a given energy input[9, 29, 30].

For both produced and injected fluids, there is a risk of groundwater contamination and surface

spills. The former is mitigated through routine casing integrity checks, and the latter is mitigated

through regular surface facility inspections. Produced water is an environmental hazard[31] if not safely

managed. Heavy metals[32], aromatic species (e.g. benzene) and polyaromatic hydrocarbons (PAH)[33],

acids and bases in the produced water have an adverse effect on the environment. It is well known that

the acid-base balance of an environment is important to ecological stability. There is a risk of injected


bases or bases extracted by the aqueous phase from the oil affecting the environment by upsetting the

local acid-base balance[34, 35]. The pH can also alter the solubility and toxicity of metals[36]. Further,

bases react directly with fatty acids in organisms and form soaps. The extent of ecological damage due

to surfactants depends on the concentration and chemical structure of the surfactant(s) used[37, 38, 39].

In the Canadian oil sands, napthenic acids (NA) native to the oil are the main toxic constitutents[40, 41].

To reduce the risk of contamination and improve operational costs, pilot studies have been conducted

to investigate the feasibility of detection and reuse of ASP additives[42, 43, 44]. In addition, there is

research interest in identifying less harmful, and ideally biodegradable, chemical substitutes[45, 46]. The

screening platform presented herein could be instrumental in this effort.

2.3 Microfluidics for Petroleum

This section summarizes work done to date on applying microfluidics to petroleum related problems.

Bowden[47] et. al. used diffusion-based extraction in a H-cell for sample preparation for GC-MS of

a North Sea oil. The sample quality was found to be similar to a sample prepared using a silica gel

adsorption column. In this study, oil viscosity was less than 100 cP, and the sample was further diluted

with hexane. They extended the use of their H-cell platform[48]to simultaneously quantify asphaltene

and carboxylic acid content of a North Sea heavy oil (> 10, 000 cP). Microfluidic viscosity measurement of

heavy oil-toluene solutions was shown to be in close agreement with measurements using a commercially

available rheometer by de Haas[9] et. al. This device relied on parallel flows in the same channel which did

not mix appreciably due to laminar flow. Fadaei et. al. demonstrated techniques for rapid quantification

of the diffusion coefficients[7] of CO2[7] and toluene[8] in Athabasca bitumen. They were able to reduce

sample size from 0.5 L to 1 nL and processing time from tens of hours to 10 min.The Mostowfi group

at Schlumberger has demonstrated similar gains in efficiency in their work on miniaturization of the

conventional PVT cell. They have shown how microfluidic chips can be used to reach phase equilibrium

quickly in order to map an entire phase envelope in a day[49, 11] and measure the gas-oil-ratio (GOR)

more accurately[10]. In order to complement or displace existing technology, microfluidic methods must

provide results that are congruent with established standards. To this end, Schneider[50] et. al. and

Alabi[12] et. al. have shown that their methods yield results comparable to ASTM D6560 and ASTM

D4124, respectively.

2.4 Digital/Droplet Microfluidics

Both names refer to the study, generation and manipulation of ordered discrete[51, 52, 53, 54, 55] multi-

phase fluid streams. The discrete phase can be used for the purpose of chemical synthesis, amplification

or analysis. Droplet trains are most commonly driven by pressure[56], magnetic fields[57], electrowet-

ting on a dielectric (EWD)[58, 59] and dielectrophoresis (DEP)[60]. The first two actuation techniques

require a continuous fluid to carry droplets along a channel and to separate them from each other;

EWOD/DEP-based devices can function without a carrier fluid as long as humidity is controlled to

prevent evaporation. Although there have been references to EWD/DEP-based methods exclusively as

”digital microfluidics”[59, 58, 61], there is no generally accepted consensus of this distinction. This work

will use both terms interchangably.

Advantages of miniaturization include dramatic decreases in processing time[62] and sample re-


quirements for high-value chemicals[63, 52] (usually of biological origin), combined with greater time

resolution[64]. Droplet microfluidics has enabled high throughput screening (HTS) for drug discovery[65]

and the study of crystallization conditions[66, 67]. It has also enabled small-scale on-demand synthesis

of high-value substances such as quantum dots[6, 68], particles with tailored morphologies[69, 70, 71]

and biological tissue mimics[72, 73, 74, 75].

Multiple emulsions consist of droplets within droplets. For example, a double emulsion is a fluid in

which there are water drops inside oil drops which are suspended in water. They are useful in facilitating

the timed release of their contents, such as in targeted drug delivery[76, 77]. Microfluidic technology

is precise enough to easily and reliably generate high order multiple emulsions with tuned phase and

interfacial properties[78, 79, 80, 81].

Although it is not yet common in industry, technology developed for high temperature particle

fabrication systems has been translated to the study of reservoir fluids in a research setting[82, 83, 84, 13].

Abolhasani et. al. have developed a platform for studying the miscibility of carbon dioxide under

reservoir conditions. Song et. al. developed a platform for determining the dewpoint of carbon dioxide

mixtures, which is an important problem for carbon dioxide transport. Nguyen et. al. developed a

platform for assessing the minimum miscibility pressure of carbon dioxide-petroleum systems. Droplet

generators are central to droplet microfluidics. Four main types of geometries exist and are shown in

Figure 2.2.

Figure 2.2: Common flow focusing geometries a) T-junction[85] b) flow-focusing[86] c) axisymmetricflow-focusing[87] d) co-flow[88]

There are three[88, 89] main generation regimes: squeezing, dripping and jetting. In the dripping

regime, the dispersed fluid thread grows and physically blocks the throat of the intersection. Pressure

builds up in the continuous phase behind the droplet thread until it is high enough for the continuous

phase to close in on the thread and break the drop off. In the dripping regime, droplets break off as a

combination of both shear exerted by the continuous phase[90] and due to capillary waves[91, 92].Since

droplet size and generation frequency are not regular in the jetting regime, most devices operate either

in the squeezing or dripping regimes.


In liquid-liquid systems, fluids are usually injected with syringe pumps. Droplet size is a function of

flow rates, fluid viscosities, interfacial tension, junction geometry and the concentration of surfactants, if

present[93]. For reliable generation of droplets, the chip substrate must preferentially wet the continuous

phase[89, 94, 95, 96].

Although there is no standard definition of capillary number in this context[89, 95], regime maps

exist[89]. Generation regimes are strongly related to the capillary number but thresholds for change from

one regime to another depend on the type of geometry. As a consequence, regime maps for one type

of junction are not applicable to another[89]. There are analytical expressions that predict droplet size

in the squeezing regime in T-junctions[97] and and flow focusing devices[98], but none in the dripping

regime. However, Anna et. al. reported a correlation[91] to that fits both the squeezing and dripping

regime in flow focusing geometries rather well[99]. This correlation has to be modified[100] to include

properties of both fluids if the channel depth is less than 30 µm. In flow focusing with a viscoelastic

continuous phase[101], it was found that regime transitions occur at lower capillary numbers and that

the continuous phase stabilizes dispersed fluid threads. Regular generation, however was also found to

be intermittent.

In this work, a proof of concept method is demonstrated to assess the effect of additives on the

interfacial tension between a Saskatchewan heavy oil and water. A planar flow focusing droplet generator

was used to form water-in-heavy oil emulsions. Experiments were performed at 80 °C and at the minimum

pressure that resulted in stable droplet generation. Additives known to alter the interfacial tension

between the two phases were dissolved into the aqueous phase. After formation, droplets were routed

through a series of expansion windows and the deformation was observed as a function of time after

entry into the expansion window. Maximum deformation was used as a proxy for interfacial tension to

assess whether the trends observed were in agreement with expectations.

Chapter 3

Droplet Microfluidics for Additive

Assessment

Reduction of interfacial tension is one way of increasing the capillary number, and therefore, oil recovery[16,

18]. A low interfacial tension is known to increase emulsification and sweep[24] in conventional reser-

voirs. In unconventional plays, a low interfacial tension also reduces fingering of the aqueous phase in

crude oil[102] to the same effect. Interfacial tension is a result of the interactions between the species

that report to the interface between fluids. Since there is a time and energy requirement for adsorp-

tion of surfactants to the interface, the dynamic interfacial tension can vary greatly with respect to the

equilibrium value as a function of interfacial age[103].

The current method of interfacial tension measurement under reservoir pressure and temperature

conditions is the pendant/sessile drop apparatus[104, 105]. In this method, a drop is slowly injected

via needle into a cell at the pressure and temperature of interest. A transparent window allows optical

access to the drop, and injection pressure and drop profile are monitored using a computerized system.

Interfacial tension is calculated using the drop profile and fluid properties[103]. Both equilibrium and

dynamic IFT can be measured using this technique. While effective, this technology is time intensive

and requires several hours to produce replicate measurements. In addition, measurement is difficult with

highly volatile oils and solvents with high solubility because the interface is poorly defined. Versions

of the pendant/sessile drop apparatus without the expensive pressure/temperature cell are common in

academic settings. In this case, unconventional oils cannot be tested directly and must be diluted with

toluene or xylene. The data collected is thus a function of dilution ratio. For all pendant/sessile drop

systems, the scale of droplets studied (mL) is far from the fL-nL typically found in reservoirs. Since

interfacial and surface tensions are surface forces, their effects become more pronounced as the surface-

to-volume ratio is increased. Methods that analyze drops at sub-mL scale would thus produce more

accurate data.

Several approaches to microfluidic tensiometry have been reported for static systems. Gu[106] et.

al. and Zhou[107] et. al. built devices with tapered channels to which a known pressure difference

is applied. The curvature of the interface at equilibrium is related to interfacial tension through the

Young-Laplace equation. Their results agreed well with literature values and Gu et. al. demonstrated

monitoring of assessment of interfacial tension as a function of surfactant adsorption. However, these

devices depend on gravity heads. Pressure control at higher pressures is not as straightforward.

9

Chapter 3. Droplet Microfluidics for Additive Assessment 10

Microdroplet-based methods could potentially reduce sampling time since droplets (and fresh inter-

faces) are created several times per second. It could also further reduce sample volume requirements and

allow access to information sampled at the sub-mL scale. Microdroplet-based methods could potentially

reduce sampling time since droplets (and fresh interfaces) are created several times per second. It could

also further reduce sample volume requirements and allow access to information sampled at the sub-mL

scale. Moran[108] et. al. were the first to measure the interfacial tension of bitumen and process water

(water recovered from extraction vessels for reuse in the recovery process) at room temperature with-

out dilution. In contrast to traditional methods, they were able to study sub-millimetre sized droplets

without diluting bitumen. They trapped emulsions between slides and manipulated individual drops

using micropipettes, and calculated the interfacial tension through a force-displacement model. The

droplet profile needs to equilibrate for every force measurement, so this method likely returns only the

equilibrium IFT. In addition, the use of micropipettes implies that it is inexpedient.

Microfluidic chips are well-suited for simulating reservoir conditions and many droplet microfluidic

methods have been reported for assessing interfacial tension. Nguyen[109, 110] et. al. generated air

bubbles in water and used embedded fibres to sense the difference in intensity of transmitted light for

estimating bubble generation frequency. They found bubble generation frequency to scale with flow rate

and surfactant concentration. Xu[111] et. al. derived a model to predict interfacial tension based on

droplet size in axisymmetric flow focusing devices. Wang[112] et. al. extended the model to T-junctions

with embedded needles to supply the dispersed phase. Similarly, Steegmans[113] et. al. developed a

model and demonstrated a measurement device with a Y-junction and channels with very low aspect

ratio. Their approach also used droplet size as a signal and was able to estimate IFT with a sub-

millisecond time resolution. All of the above approaches measure IFT at the time of droplet snap-off.

They are suitable for systems which reach equilibrium faster than drop break-up. Further, they cannot

monitor IFT as a function of interfacial age.

It is possible to monitor dynamic IFT in a device in which the deformation of a droplet is analyzed

at different points downstream of the generator. In this case, the residence time is related to the interfa-

cial age. Hudson and Cabral[114] were the first to demonstrate a continuous droplet-based microfluidic

method for IFT assessment in binary systems. They sent spherical droplets through a series of constric-

tions and used Taylor’s theory of ellipsoidal deformation[115, 116] to evaluate interfacial tension in real

time. A potential operational challenge is that the drop size and vertical position in channel must be

carefully tuned[117] to ensure that the droplet is in a flow dominated by extension. Operation can be

simplified if expansions are used instead of contractions as done by Brosseau et. al. and Polenz et. al.

to study surfactant adsorption[118] and interfacial reaction kinetics[119]. However, these studies used

polymer-based chips which cannot be used at reservoir-relevant temperatures and pressures. Further,

they are incompatible with crude oil. In this work, we present a platform for analysis of the deformation

of water droplets in high viscosity crude oil. Usually in droplet microfluidics, the fluids are either pure

liquids or binary mixtures. In biological droplet reactor experiments, the substrate and the continuous

fluid are often carefully chosen to tailor wetting properties. This is the first report of a droplet generator

that handles a high viscosity crude oil, by nature a complex fluid, without dilution. A wet-etched glass

chip is used to assess the feasibility of using expansion segments in low aspect ratio channels to screen

the effect of additives. The substrate is coated with silane to render it hydrophobic, but the properties

of the continuous phase are not controlled. In the following sections, device fabrication and system

architecture is described, followed by results of additive experiments. Drop deformation is used as a


proxy for interfacial tension. The trend is similar to results from conventional measurements: higher pH

results in more deformable droplets.

3.1 Materials and Methods

3.1.1 Hardware

The system schematic is presented in Figure 3.1. A heat film was used to heat the base of the manifold;

a thermocouple was fixed to a point near the droplet generator with high temperature Kapton tape.

Stainless steel (SS316) tubing was used throughout.

Figure 3.1: System Schematic

3.1.2 Chip Fabrication

The chip was fabricated using BF33 borosilicate glass photomasks (Telic) with a 300 nm chrome coating

and 530 nm AZ1513 photoresist. The wet etching procedure used is largely documented elsewhere[120,

8, 121]. In this instance, the etchant composition was 43 vol% HF (49% stock):31 vol% HNO3 (70%

stock):26 vol% deionized water[122, 123]. The average observed etch rate was 2.2 µm/min. The heating

profile of one thermal bonding cycle was 3 hours at 300 °C followed by 6 hours at 645 °C.

3.1.3 Surface Modification

The modification procedure is summarized in Figure 3.2. The channels on a newly fabricated chip were

rinsed with hydrogen peroxide (30 vol%) to oxidize the surface and generate silanol groups[124]. The


channels were then rinsed with deionized water and dried under vacuum. A 1 mM solution of FDTS was

prepared in toluene[125, 126, 127] and the channels were coated for 3 minutes by injecting silane from

the exit[128]. The chip was then rinsed with solvents of increasing polarity[129]: toluene, IPA, methanol,

ethanol. The chip was subsequently sonicated in ethanol for 10 minutes, then rinsed with ethanol again.

Finally, it was vacuum dried and annealed at 100°C for 30 minutes[130].

Figure 3.2: Surface preparation workflow

3.1.4 Operation

The portion of the chip downstream of the intersection was soaked in heavy oil for at least 2 hours prior

to conducting experiments. Deionized water was degassed prior to making all solutions. Experiments

were performed at 80°C. As seen in Figure ??, the viscosity of the oil at this temperature was ˜20 cP

so the viscosity ratio (λ = µcontµdisp ) was approximately 60. Syringe pumps (KDS Legato) were used to

inject fluids. The heavy oil flow rate was approximately 5 µL/min. The water flow rate was set to

the minimum value that would allow for steady generation of a well-spaced droplet train. Images were

captured with a high speed camera (PCO) at an exposure time of 5 × 10−3 s and analyzed with custommacros written in Fiji, MATLAB and R. The number of replicates is as follows: pH 7 (9), pH 9 (10),

pH 10 (4 - additive R, 12 - NaOH ), pH 11.4 (6), NaCl (14), SDS (8). Each video had approximately

800 frames.

3.1.5 Chip Geometry

The geometry of major features on the chip is presented in Figures 3.3 and 3.4. The chip has three

expansion windows. They are 17 mm (E0), 27 mm (E1) and 74 mm (E2) downstream of the intersection,

respectively. The residence time that these distances correspond to vary with droplet velocity. The


dimensions of the flow focusing intersection are summarized in Figure 3.4. Heavy oil is injected in the

side chanels (d) and the aqueous phase is injected in the main channel (a).

Figure 3.3: Chip schematic and expansion window (a = 310 ± 5 µm, b = 130 ± 5 µm,c = 610 ± 5 µm)

3.2 Results and Discussion

A droplet entering an expansion window deforms due to changes in the flow field in the transition from

a smaller channel width to a larger channel width[131]. The continuous fluid velocity in the expansion

window is less than the velocity in the contraction immediately upstream. When a droplet exits the

contraction, its leading interface moves at a slower velocity than its trailing interface. This velocity

difference causes the fluid in the middle to be pushed out to the sides (above and below the centreline).


Figure 3.4: Chip intersection schematic (a = 165 ± 5 µm, b = 50 ± 5 µm, c = 110 ± 5 µm, d = 205 ±5 µm, e = 310 ± 5 µm, f = 0, g = 310 ± 5 µm, θ1 = 90°, θ2 = 45°, depth = 55 ± 5 µm)

Away from the centreline, the interface is sheared by oil flowing around the droplet. As done by

Rosenfeld[132] et. al., the deformation was defined as:

X =Perim

2√Area · π

(3.1)

The deformation was evaluated at each frame after a droplet entered an expansion window. The

minimum distance between the leading and trailing interface at the centreline depends on droplet volume

since in droplets with more volume, there is more fluid to sweep out of the way. In addition, a droplet

with greater projected area will experience more drag due to the top and bottom channel walls. The

above deformation metric compares observed perimeter to the perimeter expected of a circle with area

equal to the observed area. If X > 1 and the projected area of a droplet at maximum deformation is

equal to its projected area immediately before entering the expansion window, it can be inferred that

interfacial area was being created in the dimension perpendicular to the plane of the expansion window.

In other words, this ratio is a measure of the new area generated during deformation. The following

section discusses observations made in the first expansion E0. Standard deviations were used for the

error bars.

3.2.1 Alkalinity

An alkaline additive (Additive R)[9] was tested the chip. Figures 3.5 and 3.6 below compare droplet

deformation as a function of aqueous phase pH in E0. Figure 3.5 presents images of droplets after entering

the expansion window. As seen in Figure 3.6(a), maximum deformation occurs around 0.025 s for all

cases except pH 10. For pH 10, maximum deformation occurs closer to 0.06 s. After peak deformation,

the droplets revert back to a circular shape as the difference between the leading and trailing interface

velocity diminishes. The deformation value that all trendlines revert to is 1.05; as would be expected,

this is the value in the upstream channel prior to entry into the constriction at the inlet of the expansion

window.

For clarity, the peaks in each trace in 3.6(a) are reproduced in 3.6(b). Drops at pH 11.4 deformed


Figure 3.5: Droplet profiles in the first 0.1 seconds after entering expansion as a function of pH

more than the other three cases. Moran[108] et. al. saw a dramatic difference between pH 10 and pH

11 (with NaOH used as the modifier). Zhang[133] et. al. found that IFT was minimized at pH 10.8 in

a Saskatchewan heavy crude oil. Thus, this new data is in agreement with literature. The relatively low

response at pH 9 and 10 could be explained by insufficient residence time. These droplets may become

more deformable by the time they reach E1 and E2 but they are expected to remain less deformable

than the pH 11.4 drops.

3.2.2 Alkaline, Surfactant and Salinity

Figure 3.7 (a) compares the deformation of a deionized water (pH 7) droplet to deformations of droplets

with the alkaline additive (pH 11.4), a common anionic surfactant (sodium dodecylsulphate) at 20

CMC and brine (NaCl at 2000 ppm). Deionized water serves as a laboratory control case. Additive

R droplets were more deformable than droplets with surfactant. Results of tests of this sort could

aid in identifying synergetic alkaline-surfactant blends[134]. Since surfactants are more expensive than

caustics, minimizing sufactant can greatly impact the economics of EOR.

Formation water and water used in waterfloods is closer to brine in composition. The NaCl-doped

droplets were about half as deformable as deionized water droplets. This observation is compatible with

reports that interfacial tension between oil and brine with 2000 ppm NaCl is higher than the intefacial

tension between oil and water without salt[135, 136]. It is believed that salinity alters the solubility of

organic species in the aqueous phase. It is likely that at this concentration NaCl draws surface active

species away from the interface[137, 138], thereby increasing interfacial tension. Thus, although inorganic

salts are not interfacially active, they interact with interfacially active species. Salinity (due to NaCl or

other salts) affects the performance of additives injected to stimulate production. The effect of brine in

altering IFT is complicated and varies with the hydrocarbon system under consideration. This platform

may aid in locating optimal salinity windows, which is an important task in industrial EOR treatment


Figure 3.6: (a) Deformation as a function of time and pH with an alkaline additive (b) Maximumdeformation as a function of pH

research and design.

3.2.3 Sodium Hydroxide

Sodium Hydroxide (NaOH) is a common caustic used in EOR treatments and research. At pH 10, drops

with additive R were more deformable than NaOH drops. There are reports of different responses to

different alkalis in the literature[139]. The ratio of charge to surface area of the cation and the strength

of the base can explain this difference. The former has an effect on the local electric field around the

interface, which can influence adsorption/desorption of surfactants[137]. The latter case, a weaker base

will maintain pH as the interfacial concentration of acid is neutralized. Due to the presence of an

equilibrium, there will also be a greater supply of reagent than with a strong base at a given pH.

3.2.4 Non-chemical Contributions to Deformation

Centroid velocities were calculated using image analysis. Velocities at maximum deformation are pre-

sented in Figure 3.8(a) for each additive condition tested. There is no direct relationship between the

trends seen in deformation and those seen in 3.8(a). The droplets at pH 9 had the highest velocity but

their deformation was not very different from deionized water or additive R at pH 10. At pH 10, NaOH


Figure 3.7: Maximum Deformation as a Function of Additive (b) Comparison between NaOH andadditive R

droplets had a higher velocity than additive R droplets, but deformed less. pH 7 and pH 11.4 droplets

moved at roughly the same speed but exhibited starkly different deformations. The major conclusion

that can be drawn from this is that the observed deformation is not strongly dependent on the droplet

velocity at maximum deformation.

During deformation, if the interface curvature changes in the plane perpendicular to the plane of the

chip, the visible area at maximum deformation will be less than the area upstream. Figure 3.8(b) plots

the ratio projected droplet area at maximum deformation to the droplet area upstream for each additive

condition. The values are all nearly equal to 1. This indicates that curvature in the depth dimension is

minimal. Interestingly, the standard deviation increases with pH. It is possible that this could be used

as a signal also.

3.2.5 Temperature and Pressure

Typical reservoir pressures in Canada are 1-3 MPa[28], which correspond to saturated steam tempera-

tures of 180°C - 234°C. The IFT between oil and water is more sensitive to temperature than pressure.

The effect of pressure on the IFT depends on the system under consideration[136]. Interfacial tension

decreases with increasing temperature. Mutual solubilities of components in the oleic/aqueous phases

or the solubility of gas(es) in oil are a function of the pressure as well as temperature[140]. The temper-


Figure 3.8: Droplet (a) velocities and (b) areas for each additive

ature also affects activity coefficients of alkalis and napthenic acids, association/dissociation coefficients

of interfacial reaction products, in addition to diffusion to and from the interfacial sublayers.

The IFT is more sensitive to oil composition and aqueous phase composition and concentration than

to temperature[136, 141]. The response to alkaline addition is a function of the acidity (TAN) of the

oil[142]. Below pH 10, IFT is unaffected by presence of alkali[141]. Both contact time and contact area

matter in the reservoir setting. The oil:aqueous phase volume ratio is O(102) in sessile/pendant/spinning

drop tests, but closer to 1 in a reservoir setting[141, 143]. Although the tests above were conducted at

a temperature much lower than reservoir conditions, it is common in the literature[144] and the general

trends should still hold. This experiment serves as a stepping stone to more accurate studies.

3.3 Conclusion

Results generated by the platform are in agreement with what is known in the literature about the

behaviour of the additives considered. A sharp increase in peak deformation was observed beteen pH 10

and pH 11.4 for additive R. At pH 10, additive R droplets were more deformable than NaOH droplets.

The deformability of brine droplets was starkly lower than deformability of deionized water droplets.

This work demonstrates a viable proof of concept for dynamic IFT assessment of unconventional heavy

crude oils.

Chapter 4

Platform Development

The following chapter documents the decisions made and the lessons learned in developing the platform.

4.1 Manifold Development

The seals on Manifold 2[120] failed at higher oil flow rates. This problem was addressed by reducing

the depth of the o-ring glands to 0.5 mm with the intent of increasing depth until a suitable depth was

identified. A depth increase was found not to be required. The schematics for the manifold used in the

present study and Nguyen[13] et. al.’s minimum miscibility pressure measurement are included in the

appendix. Schematics for the MEOR manifold and stage adapter are included as well.

4.2 Chip Development

In iterations 0-3, tests were conducted below 80 °C. Attempts were made to render the substrate hy-

drophobic by soaking in crude oil, since this mode of wettability alteration mimics what is believed to

occur in the reservoir. This approach did not yield repeatable and reliable results. In iterations 4-5,

chips were silanized to reduce the attraction between the aqueous phase and the channel walls.

4.2.1 Iteration 0

Specifications/Features: The first chip tested was developed by Soheil Talebi and Hossein Fadaei.

The dimensions of the intersection geometry are in (Figure 4.1). The substrate was Schott D263. During

operation, two syringe pumps (Pump 11, Harvard Aparatus) were used to inject fluids. All tubing was

stainless steel 316 (SS 316) and was connected using 1/16 PEEK fittings (IDEX). In this design, oil had

to be injected separately into each side arm of the flow focusing geometry. A PEEK tee was used to

divert flow to both arms from a single oil syringe. A Dino-lite USB camera (Dino-lite AM4113ZTL) was

used for imaging and a hotplate (Torrey Pines Scientific) was used as a heat source.

Successes: Particle blockages were easier to clear in this design since each side arm can be plugged

individually.

19

Chapter 4. Platform Development 20

Figure 4.1: Iteration 0 intersection schematic (a = 300 ± 5 µm, b = 30 ± 5 µm, c = 25 ± 5 µm, d =210 ± 5 µm , e = 20 ± 5 µm, f = 10 ± 5 µm , g = 300 ± 5 µm, θ1 = 45°, θ2 = 20°, depth = 50 ± 5 µm)

Figure 4.2: Response of peak hydraulic diameter to additive R as a function of dispersed phase flow rate

Lessons: In this arrangement, balancing the flow rate in each arm was often tricky and time consuming.

The flow focusing intersection was too close to the manifold. This positioning made startup difficult as

it was difficult to see how fast the fluid front was moving. Oil would often invade the water line and vice

versa. The total chip thickness was 2 mm, which was the standard used in the lab at the time. Chips of

this thickness function well in Manifold 1[84], and with Nanoports (IDEX), but snap easily in Manifold

2[120]

Notes: A metastable, periodic droplet generation regime was observed and found to be reproducible.


Attempts were made to eliminate oscillations by increasing resistance of the water line. PMMA resistors

were used first, then glass. In both cases, the resistors delaminated after 15 minutes of operation and

neither approach was successful. Figure 4.2 shows the hydraulic diameter of drops was measured at

the same point in the generation cycle as a function of pH. These preliminary results were encouraging

because they seemed to suggest that emulsion size responded to pH.

At higher oil flow rates, stable droplet generation was observed (Figure 4.3). However, the droplet

train moved too quickly for the USB camera. After 10 minutes at an oil flow rate of approximately 12

µ L/min, the o-ring seals on the manifold failed and oil started to leak. This problem was addressed

through manifold redesign as discussed in the previous section.

Figure 4.3: Stable water in oil droplet generation (oil flow rate: 3 µ L/min, water flow rate 11 µ L/min)

4.2.2 Iteration 1

Specifications/Features: The first major change in this design was the inclusion of serpentine resis-

tors in both oil and water lines (Figure 4.4). The substrate was Schott D263 (2x 4).

Figure 4.4: Iteration 1 Chip (100 µm orifice width shown)

Successes: N/A

Lessons:These devices could not be bonded, which highlighted the importance of including bonding

squares[84].


Notes: Dimensions are omitted since this device could not be tested.

4.2.3 Iteration 2

Specifications/Features: The total chip area was reduced to make bonding easier (Figures 4.5 and

4.6). Resistor length was maximized for each line without specifying a particular value. Channel depth

was ˜40 µm. This chip was fabricated in soda lime while waiting for a borofloat photomask blank (Telic)

shipment to arrive. This was the first set of chips in the 3 mm format.

Successes: The chips were successfully bonded and tested. This iteration showed that the minimum

spacing between feature edges that could be implemented was 2 mm. The chips were much more resis-

tant to snapping during installation after increasing the chip thickness.

Figure 4.5: Iteration 2 Chip (100 µm neck shown)

Lessons: Droplet generation could not be slowed enough to capture droplets on the USB camera with-

out blurring. The resultant blurring indicated that a high speed camera capable of exposure times lower

than 0.07 s would be required. It was learned that densely packed features are not a substitute for

bonding squares.

Notes: As Figure 4.7 shows, this chip initially identified that channel wetting may not adequately be

addressed by soaking the devices in heavy oil for a few hours.

4.2.4 Iteration 3

Specifications/Features: This design was identical to Iteration 2, except the substrate was Schott

Borofloat 33 (BF33). BF33 was selected because D263 is not available in thicknesses larger than 1 mm.

BF33 is an economic alternative to Pyrex 7740. Pyrex 7740 is widely used in laboratory glassware and


Figure 4.6: Iteration 2 intersection schematic (a = 140 ± 5 µm, b = 0 ± 5 µm, c = 140 ± 5 µm, d =125 ± 5 µm, e = 100 ± 5 µm, f = 370 ± 5 µm, g = 280 ± 5 µm, θ1 = 0°, θ2 = 20°, depth = 40 ± 5 µm)

Figure 4.7: Iteration 2 droplet generator exhibiting uneven wetting i) Dispersed thread extends intoexpansion ii) Thread is attracted to the wall. iii) Droplet breaks off and thread retracts. Dropletremains attached to wall until sheared off.

its chemistry is well understood. Further, it was thought that since BF33 is mostly SiO2, oil would form

a more stable coating. The chip dimensions are identical to Figure 4.6, except the feature depth was 20

± 5 µm.

Successes: Stable generation of oil in water emulsions demonstrated at 80 °C (Figure 4.8).

Lessons: For extended periods of operation (˜1 hr), the water phase wets the substrate. Without

intentional alteration of the surface energy of the channel walls to cause them to become uniformly

non-wetting to water, the formation of a stable droplet train remained a challenge. It was also learned

during this iteration that BF33 responds very weakly to the etchant used in the standard lab operating

procedure[120].

Notes: This iteration dramatically highlighted the need for a stronger etchant. Etchant development is

documented in the appendix.


Figure 4.8: Stable droplet generation at 80 °C with both flow rates equal to 1 µL/min

4.2.5 Iteration 4(a)

Specifications/Features: The major change in this iteration was that a post array was added down-

stream of a tee-junction (Figure 4.9). As an alternative to the droplet generator, the post array was

expected to disperse fluid slugs as reported by Amstad[145] et. al.

Figure 4.9: Iteration 4(a) chip with post array (inset)

The dispersed fluid channel was 300 ± 2 m wide and the continuous fluid channel was 450 ± 2 µmwide. The array had rows of 9-10 posts with the size adjusted so that the pore throats would be 110 ±2 µm wide. The substrate was BF33.

Successes: N/A

Lessons: Stable generation could only be sustained for approximately 7 minutes, after which oil invaded

the water injection line. Although the same flow rates were used, it was not possible to generate drops

that were smaller than a pore volume. This was attributed to the fact that the ratio of oil viscosity to

interfacial tension was not sufficiently raised with the additive. In contrast, Amstad[145] et. al. used an

excess of surfactant in their emulsification device.


Notes: Using excess surfactant was not suitable for the present study since the objective was to test

the efficacy of additives, as opposed to rapid generation of fine emulsions.

4.2.6 Iteration 4(b)

Specifications/Features: Two intersection geometries were implemented (Figure 4.10): (i) was in-

spired by Tan’s work in the break up of highly viscous fluids[146], whereas (ii) was as repeat of the

intersection geometry from Iteration 0. The gradual expansion employed in Generations 1-3 was re-

moved to prevent the accumulation of droplets immediately downstream of the generator. The first was

intended to be a dedicated oil in water emulsion chip and the second was intended to be a dedicated

water in oil emulsion chip. The intersection dimensions are presented in Figures 4.11 and 4.12. An

example of a detailed line resistance calculation is presented in the appendix. The substrate was BF33.

Figure 4.10: Iteration 4(b) Chips O/W (right) and W/O (left)

Successes: After soaking in 1 M NaOH for 10 hrs and rinsing in deionized water, oil in water emulsions

could be generated stably.

Lessons: No change in droplet size was observed in response to the additive. The periodic instability

was still observed in the water emulsion chip.

Notes: Emulsion size did not show a response to additive likely because viscosity dominated over the

interfacial tension. In other words, the change in interfacial tension was not sufficient to offset the high

viscosity of the dispersed phase.

4.2.7 Iteration 5(a)

Specifications/Features: Three metre long water injection line was implemented in an attempt to

reduce/mitigate periodic instability during water in oil droplet generation[147] (Figures 4.13 and 4.14).

It was thought that introducing a large resistor on the water side would (a) reduce the probability of oil


Figure 4.11: Iteration 4(b)(i) intersection schematic (a = 200 ± 5 µm, b = 0 ± 5 µm, c = 200 ± 5 µm,d = 200 ± 5 µm , e = 110 ± 5 µm, f = 0 ± 5 µm , g = 375 ± 5 µm, θ1 = 0°, θ2 = 20°, depth = 40 ± 5µm). Channels d are at a 30°angle to channel a.

invasion into the water line by causing the path of least resistance to be the downstream line by a large

margin and (b) reduce the difference in resistance in the two injection lines so that the pressure drop

along both upstream lines would be similar. The chip substrate was BF33.

Successes: Stable water droplet train generated in heavy oil and recorded with a high speed camera.

Lessons: It was time consuming to switch additives due to the high resistance in the water line. For

all previous versions, it was possible to reduce the temperature to a point where the oil became largely

immobile and flush the residual aqueous phase in the manifold and chip with water followed by the next

additive. This maneuvre could not be performed manually with this iteration. The pump could not be

set at a high rate either because the motor would stall. Moreover, despite the resistance, stable droplet

generation did not begin until the water line pressure reached a threshold value. This threshold value

was determined by the oil phase flow rate.

Notes: N/A

4.2.8 Iteration 5(b)

Specifications/Features: The expansion was changed from a 1:3 to a 1:2 ratio to lessen accumulation

of droplets in the centre of the expansion window.

Successes: This set of chips enabled the observation of deformation experienced by individual droplets

with minimal interference from neighbours.

Lessons: N/A

Notes: Results collected from this generation of chips are discussed in the previous chapter.


Figure 4.12: Iteration 4(b)(ii) intersection schematic ii (a = 280 ± 5 µm, b = 0 ± 5 µm, c = 200 ± 5µm, d = 280 ± 5 µm , e = 90 ± 5 µm, f = N/A , g = 280 ± 5 µm, θ1 = 15°, θ2 = 45°, depth = 40 ± 5µm)

Figure 4.13: Iteration 5(a) Chip featuring 3 m long aqueous phase injector


Figure 4.14: Generation 5(a) intersection schematic (a = 280 ± 5 µm, b = 0 ± 5 µm, c = 200 ± 5 µm,d = 280 ± 5 µm , e = 90 ± 5 µm, f = N/A , g = 280 ± 5 µm, θ1 = 15°, θ2 = 45°, depth = 40 ± 5 µm)

Chapter 5

Conclusion and Future Directions

In this work, it was demonstrated that it was possible to qualitatively screen differences due to additives

in a microfluidic format.

Logical next steps to increase the reliability of the technique would be to conduct replicate testing of

an unconventional pilot study using identical fluids, temperatures and pressures. A direct comparison

to a pilot will help establish a connection between droplet deformability and production data.

As with micromodel studies, the addition of fines and other particulates to the aqueous phase would

return results that are more relevant to the field. As an example, asphaltenes, resins or toluene insoluble

organic matter (TIOM)[148] can deposit and adsorb onto particles and alter their wettability. Like in

Pickering emulsions, these particles can greatly stabilize an interface once they adsorb to it. Additionally,

studies with intentionally functionalized nanoparticles would also be of interest. There is growing interest

in EOR research in using nanofluids and ionic fluids instead of an aqeuous phase. It would be interesting

to investigate the effects of these fluids on interfacial deformability.

Additionally, a numerical study of droplet deformation with this geometry and viscosity ratio would

be an appropriate first step towards making this technique quantitative. For strong bases at pH levels

between 10 and 11, it may be of interest to include an indicator in the aqueous phase to monitor the

rate/extent of the interfacial reaction. Taking this idea a step further, it may be worthwhile add a

fibre-based Raman spectrometer to the chip. Contents of the aqueous phase and/or the film between

the chip and non-oleic phase droplet could then be investigated.

29

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