MICROFLUIDICS FOR FLUID ANALYSIS IN OIL SANDS AND TIGHT

OILS

by

Aleem A. Hasham

A thesis submitted in conformity with the requirements

for the degree of Master of Applied Science

Graduate Department of Mechanical and Industrial Engineering

University of Toronto

© Copyright by Aleem A. Hasham 2017

ii

Microfluidics for Fluid Analysis in Oil Sands and Tight Oil

Aleem A. Hasham

Master of Applied Science

Graduate Department of Mechanical and Industrial Engineering

University of Toronto

2017

Abstract

Unconventional oil recovery has advanced over the decades as conventional oil supply declines.

In North America, unconventional oil has been commercialized in the oil sands and shale

formations. However, as oil prices collapse and emission concerns associated with hydrocarbon

recovery increases, producers are seeking cost-effective methods to improve economic and

environmental performance. Steam-Assisted Gravity Drainage (SAGD) and hydraulic fracturing

methods are hindered by massive water demands for stimulating formations. Microfluidics, a fluid

analysis tool benefiting from small sample volumes and precise quantification, has emerged as a

useful platform for hydrocarbon analysis, particularly for demanding, reservoir-relevant

conditions (high temperatures and pressures). In this vein, the presented work demonstrates two

microfluidic applications. The first method is a tube-based viscometer with in-line mixing relevant

to solvent-based recovery of oil sands. The second method is a physical model of nanopores

relevant to hydraulic fracturing with the aim to show fluid interactions at the pore-scale.

iii

Acknowledgments

I am thankful to Prof. David Sinton for the opportunity to contribute to the oil and gas industry in

the field microfluidics. Your dedication to me will not be forgotten.

I would like to thank Dr. Ali Abedini and Dr. Aaron Persad for their invaluable feedback and

insights during my projects. My work would be incomplete without Arnav Jatukaran and his

microfabrication insights – thank you. A sincere thank you to Dr. Soumo Mukherjee, who helped

me navigate the academic environment.

This thesis would have been impossible without the sponsorship of Suncor Energy, Neil Duncan

Thompson Fellowship, Russell A. Reynolds Graduate Fellowship in Thermodynamics, and the

Natural Sciences and Engineering Research Council of Canada (NSERC) for their funding support

through the Collaborative Research and Development Program, and the Discovery Grant Program.

Support through Alberta Innovates Energy and Environmental Solutions is also gratefully

acknowledged, as is infrastructure provided by the Canada Foundation for Innovation and the

Ontario Research Fund.

I owe an extreme debt of gratitude to my Ford Motor colleagues and most importantly to Dr. David

Stephenson, who inspired me to take on this endeavour. I fondly recall our discussions and

collaborations. Thank you for developing my empirical engineering skills and resilient work ethic.

To Pushan Lele, our time together in Sinton Lab was short-lived. I’m glad we connected on many

levels and I am extremely grateful for your company during viscosity testing. Jonathan Edwards,

I cherished our shared interest in hockey, especially during the 2017 NHL Playoffs.

Finally, I would like to thank my loved ones for their support. A special thank you to Sehar, who

has been a faithful companion to me over the years. I will never forget all that you have done.

All praise is due to The Almighty God, The All-Knowing, The Omniscient.

iv

Contents

Acknowledgments.......................................................................................................................... iii

Contents ......................................................................................................................................... iv

List of Figures ................................................................................................................................ vi

Chapter 1 Foreword ..................................................................................................................1

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

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

Chapter 2 Bitumen Viscosity as a Function of Temperature and Solvent Dilution .................3

2.1 Introduction ..........................................................................................................................3

2.2 Experiments .........................................................................................................................5

2.2.1 Fluids........................................................................................................................5

2.2.2 Governing Equations and Considerations ................................................................7

2.2.3 Capillary Viscometer Setup ...................................................................................12

2.2.4 Experimental Procedure .........................................................................................15

2.3 Results and Discussion ......................................................................................................16

2.4 Conclusions ........................................................................................................................18

Chapter 3 Visualization of Fracturing Fluid Imbibition in Sub-200 nm Nanofluidic Chip ...19

3.1 Introduction ........................................................................................................................19

3.2 Experiments .......................................................................................................................21

3.2.1 Fluids......................................................................................................................21

3.2.2 Nanofluidic Fabrication Method ............................................................................21

3.2.3 Nanofluidic Setup ..................................................................................................22

v

3.2.4 Experimental Procedure .........................................................................................23

3.2.5 Image Processing ...................................................................................................24

3.3 Results and Discussion ......................................................................................................24

3.4 Conclusions ........................................................................................................................29

3.5 Supplementary Information ...............................................................................................30

3.5.1 Image Processing ...................................................................................................30

Chapter 4 Conclusions and Future Directions ........................................................................31

References ......................................................................................................................................32

Appendix A ....................................................................................................................................36

MATLAB Code for Quantifying Shale Oil Saturation .............................................................36

vi

List of Figures

Fig. 1. Breakeven Costs per Barrel of Crude Oil as of March 2016 [10]. ...................................... 3

Fig. 2. S200 viscosity-temperature and differential pressure-flowrate comparison. ...................... 9

Fig. 3. Thermal entry length of Athabasca oil sand bitumen at 100 °C. ....................................... 12

Fig. 4. Piping and instrumentation diagram (P&ID) of the capillary viscometer. ........................ 13

Fig. 5. DAQ LabVIEW user interface. ......................................................................................... 14

Fig. 6. DAQ LabVIEW block diagram. ........................................................................................ 15

Fig. 7. Viscosity of bitumen as measured using the setup here, and compared with the ASTM

values. ........................................................................................................................................... 17

Fig. 8. Viscosity change in bitumen with condensate and temperature. ....................................... 18

Fig. 9. Experimental design of the imbibition nanofluidic chip. .................................................. 23

Fig. 10. Progression of the slick water imbibition experiment (i.e., crude oil filling, fracking fluid

infiltration, and drawdown) in both nanopore geometries. ........................................................... 25

Fig. 11. Results of the on-chip fluid imbibition testing: a) infiltration of each fracking fluid into

the nanopore networks during hydraulic fracturing; b) fracking fluid imbibition in the nanopore

networks after drawdown; c) recovered fracking fluid from nanopore networks after drawdown.

....................................................................................................................................................... 28

1

Chapter 1 Foreword

1.1 Motivation

In the second half of 2014, crude oil prices collapsed unexpectedly by over 50% after nearly five

years of stability [1]. The oil crash was led by oversupply in the market, relentless U.S. shale

production, and various geopolitical factors [1] [2]. As of this writing, the West Texas Intermediate

(WTI) crude oil benchmark hovers just below $50/bbl. At these prices, oil producers are forced to

develop cost-effective oil recovery methods or suspend operations and lose market share. The

Canadian oil fields are the third largest proven reserve in the world, behind Saudi Arabia and

Venezuela, and are obstructed by high production costs [3]. Thus, reducing operating costs is

paramount to the future of Canada’s oil and gas industry.

Oil sands and shale operations rely on massive quantities of water to recover hydrocarbon products

[4]. Steam-Assisted Gravity Drainage (SAGD) operations use steam to produce in-situ bitumen

from the oil sands. However, small quantities of solvents have shown significant potential in

offsetting water demand and its associated steam generation costs [5] [6]. Similarly, hydraulic

fracturing operations require large volumes of fracking fluids, which are 98-99% fresh water [7].

Such operations suffer from fracking fluid loss due to imbibition in the shale formations. Current

research efforts are limited in analyzing the fluid interactions of water and various liquid phases

during SAGD and hydraulic fracturing operations.

Microfluidics has become a leading fluid analysis platform for petroleum and chemical

applications [8]. These techniques benefit from high-resolution quantification, pore-scale

visualization, small sample volumes, rapid prototyping and testing, and low operational costs that

are not achievable with traditional hydrocarbon analysis methods. Consequently, microfluidics is

well-suited to addressing the water requirements of SAGD and hydraulic fracturing operations.

This thesis aims to address the water requirements of SAGD and hydraulic fracturing by a)

evaluating the effects of heat and condensate on reducing bitumen viscosity, and b) visualizing

imbibition rates in shale reservoirs at the pore-scale using microfluidic methods.

2

1.2 Thesis Overview

This thesis is motivated by operational water demands of oil sands and shale oil production

methods. The two methods developed and applied here provide reservoir-relevant analyses of the

interactions between stimulation and in-situ fluids.

In Chapter 2, the effects of heat and hydrocarbon solvents on bitumen viscosity are studied using

a capillary viscometer. Significant emphasis is placed on developing a novel capillary viscometer

design for instantaneous condensate injection and real-time data processing. The viscosities of

three bitumen-condensate mixtures and pure bitumen are determined using the capillary

viscometer design and validated by third-party bitumen viscosity testing. These viscosity results

were provided to our industrial partner to inform their operations and aid in reducing their water

usage in oil sand operations.

In Chapter 3, a nanofluidic-based approach is demonstrated to assess and quantify fracking fluid

imbibition at the pore-scale during hydraulic fracturing operations. The imbibition of three

fracking fluids into a crude oil phase are assessed on a nanofluidic platform modelling a shale oil

reservoir. Fluorescent microscopy is used to quantify the fluid phases. The imbibition nanofluidic

platform is presented as a method of informing fracking fluid selection to limit fluid loss into the

reservoir.

Finally, Chapter 4 concludes with a discussion about future applications of these findings.

3

Chapter 2 Bitumen Viscosity as a Function of Temperature and

Solvent Dilution

2.1 Introduction

Global commerce relies heavily on hydrocarbon resources, such as oil and gas, to maintain

standard of living and industrial growth. As conventional oil reserves and discoveries decline, oil

producers are turning to heavy oil reservoirs to meet the world’s ever-growing energy demand [9].

Canada holds the third largest proven oil reserve in the world, totalling to 172 billion barrels of

oil, trailing Saudi Arabia (267 billion barrels) and Venezuela (301 billion barrels) [3]. It is evident

Canada is in a prime position to meet the ever-growing energy demand as uncertainty around

conventional oil increases. However, in the wake of the current oil recession, crude oil prices have

fallen 53% since 2014 to $43.34 per barrel (West Texas Intermediate average oil price 2016),

forcing producers to seek cost-effective solutions to maintain their market share. Canada’s oil

sands are hindered by high capital and operational costs, long development timelines, and limited

pipeline access to markets [10]. As shown in Fig. 1, 43% of oil sand breakeven costs, at $26.64/bbl,

was consumed by production expenditure [10]. Therefore, there is a significant opportunity to

improve oil sand profitability by reducing operational costs.

Fig. 1. Breakeven Costs per Barrel of Crude Oil as of March 2016 [10].

$6.66

$4.11

$10.48

$2.48

$6.42

$5.03

$1.55

$8.44

$22.67

$16.09

$13.10

$6.66

$9.69

$7.56

$13.76

$7.70

$7.65

$5.10

$5.03

$4.48

$3.50

$17.36

$9.45

$8.81

$7.94

$11.56

$5.85

$4.24

$5.15

$6.87

$2.98

$2.16

$1.94

$3.00

$4.30

$2.80

$2.97

$2.54

$2.92

$3.52

$3.12

$3.11

$3.63

$2.69

$2.47

$2.67

$2.49

United Kingdom

Brazil

Nigeria

Venezuela

Canada

U.S. shale

Norway

U.S. non-shale

Indonesia

Russia

Iraq

Iran

Saudi Arabia Gross Taxes

Capital Expenditure

Operational Expenditure

Administrative/transportation Costs

4

The Athabasca oil sands are a mixture of sand, clay, water, and bitumen, an extremely viscous

unconventional petroleum product. Most oil sand formations are located in Alberta, spanning

142,000 km2, and are developed using in-situ methods; although ~3% of oil sand pay zones are

suitable for surface mining [11]. However, a major production challenge is the viscosity of

Athabasca bitumen, which can exceed 1,000,000 cP [12]. Bitumen viscosity can be reduced in-

situ through heat, solvent dilution, solvent de-asphalting, and catalytic upgrading [12].

Steam-Assisted Gravity Drainage (SAGD) is a widely used in-situ recovery process that lowers

bitumen viscosity using heat in the form of steam. During SAGD operations, steam is injected into

the oil sand pay zone through a horizontal injector well located several meters above a horizontal

producing well [13] [14]. As the steam chamber expands around the injector, latent heat is released

into the oil sands to mobilize the bitumen component [13] [14]. Mobile bitumen and steam

condensate at the chamber edge flow, assisted by gravity, to the producing well, where it is

recovered to the surface [13] [14]. However, at current oil prices, SAGD is limited by expensive

operating costs; namely, the massive water resource and steam generation demands.

Steam requirements in SAGD operations can be offset by solvents, which are effective in reducing

bitumen viscosity. The Expanding-Solvent Steam-Assisted Gravity Drainage (ES-SAGD)

recovery process substitutes a fraction of the injected steam with a small amount of solvent [5] [6].

In addition to reducing the costs associated with steam usage, produced solvents can be recycled

and re-injected into the reservoir; thereby recovering additional operating costs [5] [6].

Furthermore, solvents can upgrade bitumen and produce pipeline-ready oil for improved

transportation [5] [6]. ES-SAGD is similar to the SAGD process and can be adapted to existing

well infrastructure. Nevertheless, bitumen viscosity behaviour in response to heat and solvents are

not well characterized in literature.

Presently, viscosity measurements are performed by oilfield service companies using capillary

viscometers designed specifically for heavy oil analysis. Such capillary viscometers can measure

viscosities in the range of 1 to 500,000 cP, at an operating temperature of 10 to 200 °C and at

atmospheric to ~70 MPa pressure conditions. Heavy oil viscosity (µ) is determined using equation

1.1 by transferring a 40-mL sample across a capillary coil several times at a constant flowrate

5

(Qcoil) and environmental temperature (represented by the calibration coefficient of the capillary

coil, k) until the differential pressure stabilizes (∆Pcoil).

𝜇 = 𝑘𝛥𝑃𝑐𝑜𝑖𝑙

𝑄𝑐𝑜𝑖𝑙 (1.1)

This relation is limited to Newtonian fluids under laminar flow conditions when the Reynolds

number is less than 2100; and when the Dean number is less than 6, such that radial velocity

components are negligible. At the pore-scale in reservoirs, the Reynolds number is very low (Re

< 1-10) [15] [16], dynamic effects are negligible, and the flow is dominated by viscous effects.

Bitumen is – in general – a non-Newtonian fluid with shear-thinning behaviour at low temperatures

(T < 40 ºC) as it is a complex mixture of short and long hydrocarbons and complex aromatics. At

the temperatures of interest however (100 < T < 200 ºC) Athabasca bitumen is commonly

approximated as a Newtonian fluid [17] [18] [19].

The objective of this chapter is to measure the viscosity of Athabasca bitumen and the effects of

liquid condensate and heat on its viscosity. A novel capillary viscometer was designed by the

author to observe these effects. The capillary viscometer features instantaneous condensate

concentration control, dynamic temperature control, and real-time viscosity results and analysis.

2.2 Experiments

2.2.1 Fluids

Athabasca oil sand bitumen and liquid condensate samples were provided by an industrial partner

for viscosity testing. The bitumen sample was processed to remove water and sand. Composition

analysis and fluid properties of the condensate sample are presented in table 1 and table 2. While

the condensate sample contains a wide range of components up to C30, ~75 wt% of the condensate

consists of alkanes pentane to heptane, which are very volatile at ambient conditions. Viscosity

testing was performed for pure bitumen, and bitumen-condensate mixtures with volume ratios of

5:1, 5:2, and 5:3. Bitumen-condensate concentrations were mixed in a mixing line prior to entering

the metered capillary coil.

6

Table 1. Composition analysis of the condensate.

Component Liquid wt % mole %

CO2 0.000 0.000 H2S 0.000 0.000 N2 0.000 0.000 C1 0.000 0.000 C2 0.000 0.000 C3 0.049 0.090 i-C4 0.471 0.651 n-C4 2.397 3.311 i-C5 25.915 28.832 n-C5 24.410 27.157 C6 26.459 25.350 C7 11.362 9.348 C8 3.975 2.870 C9 1.087 0.740 C10 0.822 0.493 C11 0.475 0.259 C12 0.347 0.173 C13 0.295 0.135 C14 0.240 0.101 C15 0.219 0.085 C16 0.199 0.072 C17 0.169 0.057 C18 0.156 0.050 C19 0.134 0.041 C20 0.113 0.033 C21 0.098 0.027 C22 0.083 0.022 C23 0.070 0.018 C24 0.059 0.014 C25 0.049 0.011 C26 0.044 0.010 C27 0.039 0.008 C28 0.037 0.008 C29 0.032 0.006 C30+ 0.197 0.027

Table 2. Properties of the condensate.

Components Mole % Mass % Density (g/cc)

C7+ 20.510 26.410 0.779

C10+ 1.650 3.870 0.826

C12+ 0.900 2.580 0.850

C20+ 0.180 0.820 0.906

C30+ 0.030 0.200 1.010

7

2.2.2 Governing Equations and Considerations

2.2.2.1 Viscosity

In the viscosity experiment, the viscosity is measured in relation to temperature, flowrate, and

differential pressure by a capillary viscometer. This relation is described by equation 2.1, where

the viscosity is a function of pressure at a given flowrate and temperature encompassed in the k-

value (as noted in the section above, a Newtonian fluid assumption is applied here):

𝜇 = 𝑘𝛥𝑃𝑐𝑜𝑖𝑙

𝑄𝑐𝑜𝑖𝑙 (2.1)

The k-value, or calibration constant, was developed by comparing the known viscosity-

temperature profile of a Cannon S200 viscosity standard to its experimentally determined pressure-

flowrate relation. Table 3 shows the viscosity-temperature profile of the Cannon S200 viscosity

standard. Using this data, a full viscosity-temperature profile was modelled to accurately determine

the k-value from the S200 pressure-flowrate tests using the ASTM D341-09 Standard Practice for

Viscosity-Temperature Charts for Liquid Petroleum Products [20]. A similar calibration was done

using a Cannon HT390 viscosity standard at higher temperatures, however, the viscosity-

temperature profile of this standard only included data at 100 ºC and 150 ºC. Thus, the S200 curve

was deemed to be more reliable.

Table 3. Viscosity profile of Cannon S200 viscosity standard provided by manufacturer [21].

Temperature Kin. Viscosity (ν) Dyn. Viscosity (µ) Density Saybolt Viscosity

°C °F mm2/s (cSt) mPa.s (cP) g/cm3 (g/mL) seconds

20.00 68.00 532.40 447.00 0.8397

25.00 77.00 395.50 330.90 0.8366

37.78 100.00 198.80 164.80 0.8290 921 SUS

40.00 104.00 178.40 147.60 0.8276

50.00 122.00 113.50 93.23 0.8216

80.00 176.00 37.99 30.53 0.8036

98.89 210.00 22.36 17.72 0.7923 109 SUS

100.00 212.00 21.76 17.23 0.7916

The ASTM D341-09 standard projects the kinematic viscosity of petroleum oil and liquid

hydrocarbon products at any temperature given two known kinematic viscosities at two

temperatures [20]. In this method, extrapolation is the most accurate if the known viscosity-

8

temperature points are far apart; the lower bound is limited to the cloud point temperature and the

upper bound is limited to the initial boiling point temperature [20]. However, viscosity

extrapolation is most accurate in the high-temperature region [20]. The ASTM viscosity-

temperature model is governed by equations 2.2 to 2.4, where kinematic viscosity (υ) is in

centistokes (cSt), temperature (T) is in Kelvin (K), and constants A and B are determined by the

known points [20]:

log (log 𝑍) = 𝐴 − 𝐵 log 𝑇 (2.2)

𝑍 = 𝜐 + 0.7 + exp(−1.47 − 1.84𝜐 − 0.51𝜐2) (2.3)

𝜐 = [𝑍 − 0.7] − exp(−0.7487 − 3.295[𝑍 − 0.7] + 0.6119[𝑍 − 0.7]2

− 0.3193[𝑍 − 0.7]3) (2.4)

The dynamic viscosity profile can be determined by equation 2.5 in relation to a fluid’s linear

density-temperature profile:

𝜈 =𝜇

𝜌 (2.5)

Fig. 2 shows the resulting ASTM-modelled dynamic viscosity-temperature profile in comparison

to the experimental differential pressure-flowrate results of the S200 viscosity standard. Given the

relationship between dynamic viscosity and pressure and flowrate, described in equation 2.1, the

9

k-value was determined to be a function of temperature in degrees Celsius, as shown in equation

2.6:

𝑘 = 1.565 × 10−15 𝑇−0.064 (2.6)

Thus, the capillary viscometer is fully calibrated and the experimental dynamic viscosity results

are governed by equation 2.7:

𝜇 = 1.565 × 10−15𝛥𝑃𝑐𝑜𝑖𝑙

𝑄𝑐𝑜𝑖𝑙 𝑇0.064 (2.7)

Fig. 2. S200 viscosity-temperature and differential pressure-flowrate comparison.

2.2.2.2 Thermal Considerations

The capillary viscometer measures fluid viscosity in a capillary coil at high temperatures (100 °C

to 200 °C), which can result in thermal expansion of the capillary tubing. Thermal expansion of a

thin ring, pipe, or tube is described by the change in circumference due to heat. The initial and

final circumference of the tubing are expressed as:

y = 9E+17x-2.39

R² = 0.999

y = 1418.3x-2.455

R² = 0.99960

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0

1E+13

2E+13

3E+13

4E+13

5E+13

6E+13

7E+13

50 60 70 80 90 100 110

μ[P

a.s

]

∆P/Q

Temperature [°C]

dP/Q at 500uL/min dP/Q at 700uL/min ASTM Viscosity Prediction

10

𝑐0 = 𝜋𝑑0 (2.8)

𝑐1 = 𝜋𝑑1 (2.9)

Where c0 is the initial circumference and d0 is the initial diameter, similarly, c1 is the final

circumference and d1 is the final diameter; these dimensions are represented in meters. The change

in circumference due to thermal expansion is described as:

𝑐1 − 𝑐0 = 𝜋𝑑0𝑑𝑇𝛼 (2.10)

In this relation, dT is the change in temperature in Kelvin and α is the linear expansion coefficient,

which is 16×10-6 m/mK for stainless-steel 316. Equation 2.10 can be modified using equations 2.8

and 2.9:

𝑑1 = 𝑑0(𝑑𝑇𝛼 + 1) (2.11)

Similarly, the thermal expansion of the tubing length is described in equation 2.12:

𝑑𝑙 = 𝛼𝐿0𝑑𝑇 (2.12)

Thermal expansion of the capillary coil at 100°C and 200°C resulted in an increase of 0.12% and

0.28%, respectively in both dimensions. Thus, thermal expansion was considered negligible during

the operation of the capillary viscometer.

Bitumen and condensate were mixed in a heated mixing line preceding the capillary coil where the

mixture viscosity is measured. Thus, it is crucial to obtain a fully developed velocity and

temperature profile in the mixing line. Since bitumen and condensate meets in the mixing line at

different flowrates, the mixture velocity profile is not fully developed. Nor is the mixture

temperature profile yet fully developed because condensate inlet has an initial room temperature

condition. However, a fully developed velocity and temperature length can be determined by the

11

thermal entry length, as velocity boundary layer development occurs faster than thermal boundary

layer development for large Prandtl number fluids (Pr ≥ 5) [22].

The Prandtl number for bitumen was determined to be 5400 at 100°C and 173 at 200°C using the

properties in table 4 and equation 2.13. Therefore, the fully developed velocity and temperature

profiles can be determined by the thermal entry length.

Table 4. Properties of Athabasca oil sand bitumen.

Temperature Specific heat (cP) Dyn. Viscosity (µ) Density (ρ) Thermal conductivity (k)

°C kJ/kg∙K mPa.s (cP) kg/m3 W/m∙K

0 1.67 3.47 x 107 1026

0.14 100 1.89 400 970

200 2.1 12 906

𝑃𝑟 = 𝜐

𝛼=

𝑐𝑝𝜇

𝑘 (2.13)

At an operational flowrate between 100 to 700 µL/min, the Reynolds number ranged from 0.01 to

2, indicating a laminar flow regime between 100°C and 200°C.

𝑅𝑒𝐷 = 𝜌v𝐷

𝜇 (2.14)

The thermal entry length is determined by the average Nusselt number (equation 2.15) and the

Graetz number (equation 2.16), which is plotted in Fig. 3. Velocity and temperature are considered

fully developed when the inverse of the Graetz number is 0.05 [22]. Therefore, the thermal and

velocity boundary layer is fully developed in the first 10 mm and 15 mm of the mixing line, when

the flowrates are 100 µL/min and 700 µL/min, respectively.

12

𝑁𝑢𝐷̅̅ ̅̅ ̅̅ = 3.66 +

0.0668 𝐺𝑧𝐷

1 + 0.04 𝐺𝑧𝐷2 3⁄

(2.15)

𝐺𝑧𝐷 = 𝑅𝑒𝐷𝑃𝑟

𝑥/𝐷 (2.16)

Fig. 3. Thermal entry length of Athabasca oil sand bitumen at 100 °C.

2.2.3 Capillary Viscometer Setup

2.2.3.1 Hardware

Fig. 4 shows the piping and instrumentation diagram (P&ID) for the capillary viscometer designed

by the author. The capillary viscometer measures the viscosity of two fluids in a capillary coil that

are first mixed in a mixing line. The purpose of this experiment is to measure the viscosity of pure

bitumen and bitumen-condensate mixtures. Condensate is supplied from a volume displacement

cell driven by a Teledyne-Isco 260D syringe pump. Similarly, bitumen is supplied from a volume

displacement cell driven by a Teledyne-Isco 260D syringe pump. Bitumen is unable to flow at

room temperature, due to its high viscosity (over 106 cP at room temperature). Therefore, its

volume displacement cell and supply lines are heated to 90°C by a flexible rope heater. The supply

lines and mixing line are 1/8” smooth-bore seamless 316 stainless-steel tubing with an internal

diameter of 0.028”. The mixing line blends bitumen and condensate into a homogeneous mixture

0

2

4

6

8

10

12

14

0 0.02 0.04 0.06 0.08 0.1

Nu

D_bar

1/GzD

100 uL/min200 uL/min300 uL/min400 uL/min500 uL/min600 uL/min700 uL/min

13

at a constant temperature maintained by a high-temperature silicon bath (regulating ±1°C from set

temperature). Mixture viscosity is measured across a capillary line submerged in the silicon bath

and metered by an Omega DPG409-500DWU digital differential pressure gauge with 10 Vdc

analog output. The capillary line is a 1.5 m 1/16” smooth-bore seamless 316 stainless-steel

capillary tubing with an internal diameter of 0.020” and a coil radius of 10 cm and is connected to

a back-pressure regulator (BPR) at the outlet.

Fig. 4. Piping and instrumentation diagram (P&ID) of the capillary viscometer.

2.2.3.2 Data Acquisition System

The capillary viscometer measures viscosity as a function of the differential pressure across the

capillary coil at a given flowrate and temperature. Due to the variability in pressure during the

viscosity experiment, a data acquisition system (DAQ) is required to record the differential

pressure. As mentioned above, the capillary coil is metered by an Omega differential pressure

gauge with an analog output. This pressure gauge outputs a 0 to 10 Vdc voltage, which represents

the differential pressure, where 0 Vdc is 0 psi and 10 Vdc is 500 psi. The DAQ visualizes the real-

time voltage change in LabVIEW using a National Instruments USB-6212 that accepts the Omega

voltage output.

During the viscosity experiments, the differential pressure readings were recorded by the DAQ

and displayed on a LabVIEW application developed for real-time viscosity measurement, as

shown in Fig. 5. Real-time monitoring and recording of the viscosity experiments are essential to

14

troubleshooting and accurate data collection. In addition to recording the Omega voltage output,

the DAQ also converts the voltage into differential pressure by voltage scaling and calculates the

mixture viscosity in real-time. Viscosity is calculated in the DAQ by equation 2.1, where

temperature and flowrate are specified by the user in the user interface (UI), shown in Fig. 5. The

graphical source code of this UI is demonstrated in Fig. 6.

Fig. 5. DAQ LabVIEW user interface.

15

Fig. 6. DAQ LabVIEW block diagram.

2.2.4 Experimental Procedure

Prior to each test, the capillary viscometer was purged and cleaned using toluene and deionized

(DI) water at room temperature to prevent contamination and to maintain equipment integrity. The

high-temperature silicon bath was turned on and set to the operating temperature range once the

capillary viscometer was reassembled and the volume displacement cells were filled with their

respective fluids. After the silicon bath temperature had stabilized, the BPR was set to 2 MPa and

the bitumen supply rope heater was turned on and set to 90 °C.

Bitumen and condensate supply valves were opened once bitumen was thoroughly heated and fully

mobile. The bitumen and condensate syringe pumps were operated under constant flow mode to

ensure steady mixing and continuous flow into the metered capillary coil. Flowrates of each fluid

were adjusted to achieve the evaluated bitumen-condensate concentrations of 5:1, 5:2, 5:3 volume

ratios and of pure bitumen. Silicon bath temperature was also controlled between 100 °C and 200

16

°C to generate the bitumen-condensate viscosity profiles. Experimental data was collected using

the DAQ, which visualized fluid viscosity in real-time.

Unlike commercial viscometers for bitumen applications, the capillary viscometer presented here

benefits from variable fluid mixing and real-time viscosity response. Thus, viscosity data was

collected at a given temperature and flowrate (bitumen-condensate concentration), after which, the

concentration or temperature was changed during operation to collect the next viscosity point. The

resulting viscosity point required less than 10 minutes to stabilize after changes in bitumen-

condensate concentration or temperature. In addition, the sample volume used in the capillary

viscometer was notably lower than that for commercial viscometers.

2.3 Results and Discussion

Bitumen and bitumen-condensate tests were performed at 500 µL/min and 700 µL/min (twice

each) to ensure consistency in viscosity results. The data reported in Fig. 7 and Fig. 8 are an

average of the viscosity readings collected by the capillary viscometer for 10 minutes at a 100 Hz;

the average, upper, and lower bounds are determined from four independent experiments for each

temperature and condensate concentration. These results were not affected by higher flowrates as

the differential pressure responded well to flowrate increases. Viscosity data was recorded for all

bitumen-condensate mixtures prior to increasing the silicon bath temperature. Sample volume

ranged from 6 to 10 mL for each viscosity-temperature measurement, including volume expended

during stabilization.

Fig. 7 shows the viscosity of pure bitumen as a function of temperature with the ASTM-developed

viscosity provided the industrial partner. The third-party bitumen data in this case came from a

report from Schlumberger Canada Limited. The resulting error between the capillary viscometer

and third-party testing was less than 5%. Bitumen is incredibly viscous, and can be over 1,000,000

cP at room temperature. The bitumen viscosity decreased logarithmically while temperature

increased during viscosity testing, as expected. At 100 °C, the bitumen viscosity was 434 cP and

was much more mobile. From 434 cP, bitumen viscosity was reduced 71% at 125 °C, 89% at 150

°C, 95% at 175 °C, and finally 97% at 200 °C where viscosity was 12 cP. More generally, these

values are in line with those reported in the literature for Athabasca bitumen – albeit with

differences due to reservoir locations and sample retrieval conditions. Relevant literature

17

comparisons include measurements in the range 200 cP to 500 cP at 100 °C and less than 50 cP at

200 °C [23]. While the most meaningful comparison remains the same as indicated in Fig. 7, it is

noteworthy that the values and trends in general reflect those in the broader literature.

Fig. 7. Viscosity of bitumen as measured using the setup here, and compared with the ASTM values.

Condensate was introduced to the capillary viscometer under constant flow conditions to ensure

adequate mixing with bitumen in the mixing line. The viscosity results of bitumen-condensate

mixtures are compared at 100 °C and 200 °C in Fig. 8. It is evident, that higher concentrations of

condensate decreased the mixture viscosity logarithmically. In comparison to bitumen at 100 °C,

condensate reduced bitumen viscosity by 14%, 29%, and 37% for each increase in condensate

component volume. However, for the same bitumen-condensate concentrations, bitumen viscosity

was reduced by over 50% at 200 °C. Bitumen-condensate viscosity was lowered by 68%, 78%,

and 83% in contrast to bitumen as condensate concentration increased. The effects of temperature

are clearly illustrated in Fig. 7, where bitumen viscosity is decreased by 97% from 100 °C to 200

°C. Similarly, each bitumen-condensate mixture experienced a viscosity decrease greater than 98%

from 100 °C to 200 °C.

10

100

75 100 125 150 175 200

Dyn.

Vis

cosity

[cP

]

Temperature [C]

ASTM Bitumen

Experimental Bitumen

Third-Party Bitumen Data

18

Fig. 8. Viscosity change in bitumen with condensate and temperature.

2.4 Conclusions

A capillary viscometer was developed to determine bitumen and bitumen-condensate viscosities

at high temperatures. The operation of the capillary viscometer relied on the instantaneous mixing

of bitumen and condensate by adjusting the fluid supply flowrates to achieve the desired

volumetric ratio. Due to the cold inlet and lower flowrate of the condensate supply, the mixture

temperature and velocity boundary layer were fully developed within the first 15 mm of the mixing

line. Furthermore, viscosity-temperature measurements required less than 10 mL of sample, 75%

less than leading commercial viscometers. The capillary viscometer results were validated within

5% by third-party bitumen viscosity data. The effects of condensate and temperature were found

to have a profound effect on bitumen viscosity.

0

50

100

150

200

250

300

350

400

450

500

100°C

Dyn.

Vis

cosity

[cP

]

0

2

4

6

8

10

12

14

16

18

20

200°C

Dyn.

Vis

cosity

[cP

]

Bitumen

Bitumen-Condensate 5:1

Bitumen-Condensate 5:2

Bitumen-Condensate 5:3

19

Chapter 3 Visualization of Fracturing Fluid Imbibition in Sub-

200 nm Nanofluidic Chip

3.1 Introduction

Recent advancements in horizontal drilling and hydraulic fracturing (or fracking) have unlocked

shale oil production potential [24] [25]. Domestic shale production has become a sustaining factor

in US energy supply, all the while reducing foreign energy dependence [26]. Shale oil refers to

light oil with low Sulphur content trapped in extreme low permeability formations at relatively

high pressures and temperatures [27]. Microstructure and transport phenomena in shale formations

have been largely unexplored until recently strong rise in shale oil production. The mean pore radii

observed in shale pore size distributions are overwhelmingly sub-100 nm. However, it has been

demonstrated that while pores with a radius of 3 to 6 nm represent 95% of the total distribution,

they contribute only 4% to the total volume; whereas over 50% of the total volume is contributed

by pores with a radius of 100 to 250 nm with less than 1% of the pore distribution [28].

Hydrocarbons stored in pore networks are accessed using multistage hydraulic fracturing. In a

typical hydraulic fracturing, large volumes of fracking fluids are injected into a reservoir at high

pressure to generate high permeability fracture networks by overcoming the rock fracture gradient.

In the subsequent drawdown (or flow back) stage, the injection pressure is reduced and the internal

formation pressure drives flow to the surface; recovering both the injected fracking fluids and

reservoir fluids. Hydrocarbon recovery occurs after a shut-in period following drawdown. Slick

water is a commonly used fracking fluid, which is a mixture of water and additives with 1–2 wt%

[7]. Presence of additives improves hydrocarbon flow by reducing flow resistance/friction

pressure, discouraging microbial growth, modifying surface and interfacial tension, preventing

emulsions, deterring scale, and maintaining fracture width [7]. However, imbibition of fracking

fluids into low permeability shale is a major cause of fluid loss and is a function of interfacial

tension, rock wettability, and pore radius [4] [29]. Imbibition of fracking fluid can cause reservoir

damage and restrict hydrocarbon flow, limiting profitability. In addition, fracking fluid loss is a

huge capital expense given the massive volume of fresh water used in fracking operations: ~2–6

million gallons [4]. For instance, fracking fluid recovery ranges from 5% (Haynesville dry gas) to

48% (Mississippi Lime oil) [4] [7].

20

Although the recent increase in shale production has prompted the study of shale properties and

pore structures, fluid interactions at the nanoscale are not fully understood. Present imbibition

experiments involve immersing a desiccated shale sample in a fluid followed by measuring the

mass difference to determine fluid retention [4] [29] [30] [31]. However, these conventional

methods cannot visualize imbibition at the nanoscale. Furthermore, they are unable to observe the

imbibition mechanisms with a second liquid phase present [4]. The revolution in shale production

technology has occurred rapidly yet there remains a lack of knowledge regarding the fundamental

phenomena surrounding this complex geometry with complex fluid interactions, at high pressures

and nanoscale confinements.

A growing set of micro/nanofluidics-based measurements have been developed recently for

petroleum and chemical applications [8] [32]. Specifically, for petroleum processes, there are

precedents for employing microfluidics for fluid phase measurements and pore-scale analysis of

displacement processes. A variety of microfluidic chips have been developed to determine fluid

phase properties such as bubble/dew points, phase envelope, solubility, diffusivity, miscibility,

rheology, and asphaltene/wax deposition [33] [34] [35] [36] [37] [38]. In addition, micromodels

have been used to probe the pore-scale of variety of oil recovery processes for light and heavy oil

systems [35] [39] [40] [41] [42] [43]. Micro/nanofluidics provide a unique unprecedent controlled

environment together with high-resolution quantification, not practical with current methods. Due

to very small volume of fluid samples required for micro/nanofluidic tests, such methods offer fast

quantification, easier control over extreme conditions (e.g., high pressures and temperatures), and

significantly lower operational cost. While the majority of microfluidic works demonstrated to

date focus on the phase measurements and fluid transport in micro-confinements replicating the

conventional oil and gas resources, there is limited transport data in nanoconfinements

representing the shale formations.

In this chapter, the author presents a nanofluidic-based approach to visualize and quantify the

imbibition of a fracking fluid with a crude oil phase at the pore-scale (i.e., sub-200 nm

confinement). The objective of this chapter is to determine the imbibition rates of deionized (DI)

water, brine (KCl solution), and slick water using a nanofluidic platform replicating a shale oil

reservoir. The nanofluidic chip features nanopore networks at a depth of 175 nm; representing the

median pore size of the uppermost volume contributing pores [28]. Leveraging the inherent

21

fluorescence characteristic of crude oils, fluorescent microscopy is used to differentiate and

quantify the oil and fracking fluid phase saturations. This work presents a unique nanofluidic

application for shale oil production, as a part of a growing suite of nanofluidic platforms that have

been developed for unconventional hydrocarbon resources.

3.2 Experiments

3.2.1 Fluids

West Texas crude oil was used as a representative of the shale oil phase for the imbibition

experiments. Three distinct liquids were assessed as fracking fluids: DI water, brine solution, and

slick water. The brine solution was composed of 2% by volume potassium chloride (KCl) and DI

water. Slick water was developed for the experiment using the formulation by King (2012): 98

vol% DI water, 0.1 vol% glutaraldehyde solution (biocide or disinfectant), 0.05 vol%

methylphosphonic acid (scale inhibitor), and 0.05 vol% polyacrylamide (friction reducer).

However, proppants are absent in this solution as the microchannels and networks do not need to

be braced open and their inclusion would obstruct flow.

3.2.2 Nanofluidic Fabrication Method

A silicon-glass microfluidic chip was designed with two main segments: 1) microchannels with a

depth of 25 µm resembling hydraulically fractured microfractures, and 2) nanopore networks with

a depth of 175 nm which is the median pore size of the highest volume contributing pores [28].

The microfluidic chip underwent two stages of exposure and dry etching to form these segments.

A 4-inch silicon wafer was baked on a 100 °C hot plate for 2 minutes to prepare the surface for

photoresist; a thin, uniform photosensitive layer. Hexamethyldisilazane (HMDS) was applied to

the wafer surface to promote photoresist adhesion and was spun at 2000 rpm (± 50 rpm) for 90

seconds. Spin-coating was repeated for a Microposit S1818 G2 photoresist layer. Next, the silicon

wafer was baked on a 100°C hot plate for 8 minutes. The desired etching pattern was then

imprinted onto the photoresist-coated wafer by UV exposure through a photomask at an intensity

of 200 mJ/cm2. The wafer was immediately submerged in a solution of 1:1 Microposit MF-312

Developer and DI water for 1 minute, then bathed in DI water for 1 minute, and dried using

nitrogen gas.

22

Reactive-ion etching (RIE) was used to form the micro and nano geometries by exposing the wafer

to chemically reactive plasma; where high-energy ions subtract material from the pattern. The

etched depth was then measured by a profilometer. After etching, the wafer was cleaned in a

piranha solution – an exothermic solution of 3:1 sulfuric acid (H2SO4) and hydrogen peroxide

(H2O2). The wafer was rinsed in DI water and dried using nitrogen gas once the piranha solution

had cooled and was safe to handle. The photoresist to piranha steps were also repeated for the

micro pattern.

Inlet ports were bored through the wafer using a diamond cylinder, 120 grit, 1/8” shank, 0.030” x

3/16” drill bit. The wafer was submerged in a sononator bath for 10 minutes to dislodge any drilling

debris and then dried with nitrogen gas. Finally, the prepared silicon wafer was anodically bonded

to a glass surface. The bonding process relies on an electrostatic field and does not require an

intermediate bonding layer to fuse the surfaces together.

3.2.3 Nanofluidic Setup

Fig. 9 shows the microfluidic chip and testing platform designed by the author for the imbibition

tests. The microfluidic chip consists of 48 nanopore networks at a depth of 175 nm with two grid

geometries: square and circular posts, as seen later in Fig. 10. Square networks have a porosity of

30%, whereas circular networks have a porosity of 49%. Each nanopore network is supplied by a

crude oil microchannel and a fracking fluid microchannel. The 25 µm deep microchannels are

designed to uniformly fill all 48 networks simultaneously, where crude oil fills from the bottom of

the network and fracking fluid from the top, as shown in Fig. 9.

The microfluidic chip is mounted in a stainless-steel manifold which is connected to the crude oil

and fracking fluid supply lines by 1/16” smooth-bore seamless 316 stainless-steel tubing with an

internal diameter of 0.022”. Fracking fluid is supplied directly from a Teledyne-Isco 260D syringe

pump because of its high-water content. However, crude oil is supplied from a volume

displacement cell to prevent contamination and mechanical damage to its Teledyne-Isco 260D

syringe pump.

The imbibition experiment is captured by a Leica MC 170 HD camera connected to an Olympus

BXFM microscope with an X-CITE 120 LED light source. The illustration in Fig. 9 demonstrates

23

the stages of the imbibition experiment: crude oil filling of the network, fracking fluid infiltration,

and drawdown. Fluorescent images of individual nanopore networks are taken at each stage of the

experiment to accurately identify the fluid interfaces. In the illustration, crude oil and fracking

fluids are depicted in black and blue, respectively. Under fluorescent conditions, crude oil

fluoresces bright green while the rest of the network remains black.

Fig. 9. Experimental design of the imbibition nanofluidic chip.

3.2.4 Experimental Procedure

3.2.4.1 Crude Oil Filling

Prior to each test, the microfluidic chip was vacuumed for one hour at room temperature to prevent

accidental air bubbles from entering the system during the experiment. The initial filling of the

microfluidic chip was critical to the success of the experiments. Due to the small internal volume

(0.005 to 0.008 nL) and initial vacuum condition, liquids will flow rapidly into the system and

overwhelm the opposing liquid flow. To avoid overfilling, the syringe pumps filled the supply

lines while the valves remain closed under constant flow mode at 200 µL/min until the line

pressure was 30 bar. The crude oil supply valve was then opened while observing the crude oil

microchannel under the microscope. The fracking fluid valve was opened immediately after crude

oil flow has been observed. Under these conditions, crude oil filled the microchannels and

nanopore networks while the fracking fluid formed an interface with the crude oil just outside the

nanopore network. Once an interface had been formed, the syringe pumps were switched to

constant pressure mode and pressurized to 40 bar.

24

3.2.4.2 Fracking Fluid Infiltration

Fracking fluid pressure was increased to 50 bar while maintaining crude oil pressure at 40 bar to

simulate hydraulic fracturing. The 10 bar difference between the opposing fluids allowed the

fracking fluid to infiltrate the nanopore networks and displace the crude oil back into the crude oil

microchannel supply network. After the initial rapid fluid infiltration, the interface in the nanopore

network will approach steady state due to constant oil pressure resisting flow.

3.2.4.3 Drawdown

Fracking fluid pressure was reduced to 30 bar while the oil pressure remained at 40 bar, resulting

in a negative pressure flow or drawdown. Due to the sudden decrease in fracking fluid pressure by

20 bar, the displaced crude oil flooded into the nanopore network displacing both the infiltrated

fracking fluid and remaining oil into the fracking fluid microchannels. This stage replicates actual

hydraulic fracturing operations which recover produced water after drawdown.

3.2.5 Image Processing

Each stage of the imbibition test was captured using fluorescent microscopy, as it displayed the

best contrast between the crude oil (bright green) and the rest of the microfluidic chip (dark). A

MATLAB code was developed to convert the fluorescent images, where the pixel values ranged

between 0 and 255, to a binary image with pixel values ranging from 0 to 1. The resulting binary

images displayed all fluorescing pixels as white (pixel value: 1) and the rest of the microfluidic

chip as black (pixel value: 0). Thus, the volume of crude oil in a nanopore network could be

determined by quantifying the number of white pixels. A sample image processing performed here

is shown in the Supporting Information.

3.3 Results and Discussion

The results of three independent experiments for each fracking fluid evaluation are reported in this

section. A sample of the slick water imbibition test is shown in Fig. 10 to demonstrate the images

captured during the imbibition experiments. Crude oil filled the networks and formed an interface

with the fracking fluid above the network. Once the interface had stabilized, the fracking fluid

supply pressure was increased to simulate hydraulic fracturing. The fracking fluid flooded the

nanopore network and displaced the crude oil into the oil supply microchannels . After 10 minutes,

25

the interface stabilized in the networks and the fracking fluid supply pressure was reduced to

initiate drawdown. The resulting negative pressure flow caused crude oil and fracking fluid to rush

into the microchannels above the networks. As expected, drawdown resulted in produced water

(slugs of crude oil and fracking fluid) flowing through the microchannels above the nanopore

networks while some fracking fluid remained trapped in the networks.

The hydraulic fracturing stage of the imbibition experiment, shown in Fig. 10, illustrates how the

fracking fluid displaces the crude oil phase. The interface resembles an inverted triangle because

the fracking fluid enters the nanopore network from a single nanochannel centered above the

network. During infiltration, the interface has a ‘blooming’ effect as it advances into the network.

The blooming effect can be described as an expanding hemispherical front of fracking fluid

originating from a single point – similar to a single point sourced ripple. However, unlike a

curvilinear interface, the fracking fluid forms a tip at the bottom of the nanopore network, resulting

in an inverted triangular shape. This shape is caused by a crude oil microchannel centered at the

bottom of the nanopore network, where the displaced crude oil retracts.

Once drawdown was initiated, the displaced crude oil quickly flowed back into the nanopore

networks and in turn displaced the occupying fluids. The result of the drawdown stage is shown in

Fig. 10, where fracking fluid has imbibed into the nanopore networks. It is observed that there is

a random and heterogeneous distribution of fluids in the networks, as expected during imbibition.

Such a visualization of imbibed fluids at the nanoscale is a first for shale oil imbibition tests.

Crude Oil Filling Poil > Pfrack fluid

Hydraulic Fracturing Poil < Pfrack fluid

Drawdown Poil > Pfrack fluid

Circula

r

Nano

pore

s

Squ

are

Nano

pore

s

Fig. 10. Progression of the slick water imbibition experiment (i.e., crude oil filling, fracking fluid infiltration, and

drawdown) in both nanopore geometries.

26

Fig. 11a shows the infiltration of each fracking fluid into the nanopore networks during the

hydraulic fracturing stage of the imbibition experiment. In all cases, the fracking fluid flooded the

nanopore networks and displaced over 50% of the occupying crude oil into the crude oil

microchannels. Fracking fluid infiltration was most effective in the circular nanopore networks

due to the higher porosity and permeability of the network in comparison to the square networks.

As a result, there was an increase in fracking fluid infiltration of 6-11% between the two nanopore

geometries. Furthermore, there was a distinct difference in fracking fluid infiltration between the

tested fluids. The fracking fluids performed consistently relative to each other in both nanopore

geometries. Brine solution displaced the largest amount of crude oil of the three fracking fluids,

followed by slick water and DI water. However, the infiltration rate of slick water into the nanopore

network was closest to the performance of the brine solution. The difference between the

infiltration rates of brine solution and slick water was ~5% and ~2% for the square and circular

nanopore geometries, respectively. However, DI water performs ~8% and ~12% worse than slick

water for the square and circular networks, respectively.

Drawdown is simulated after hydraulic fracturing in the imbibition experiment. The imbibition

rates of each fracking fluid are shown in Fig. 11b. In comparison to the hydraulic fracturing stage,

the drawdown stage of the imbibition experiment resulted in less fracking fluid volume in the

nanopore networks, as expected. Fracking fluid imbibition was most prevalent in the square

nanopore networks, though marginally so. Overall, the square nanopore networks retained less

than 2% more fracking fluid than the circular networks. This difference is attributed to the lower

porosity and permeability of the square nanopore networks. However, the imbibition trends of the

fracking fluids are similar for both geometries. Brine solution imbibed the least during the

imbibition experiment, followed by slick water and DI water. The imbibition difference between

brine solution and slick water was close to ~3% in both nanopore network geometries.

Furthermore, the difference in imbibition between slick water and DI water was around ~2% for

both geometries. Though there is a clear trend in the imbibition rates of the fracking fluids, the

data range and small quantifiable difference suggests that the performance of the fluids are similar.

Instead, the fracking fluid performance should be evaluated by the recovered fracking fluid volume

– or the difference between the infiltration and imbibition of the fluids. This metric determines the

effectiveness of a fracking fluid by considering its infiltration potential and imbibition behavior.

27

Fig. 11c plots the amount of fracking fluid recovered from the nanopore networks during the

imbibition experiment and is determined by subtracting the imbibed volume from the infiltrated

volume. There is an 8–13% increase in fracking fluid recovery in the circular nanopore networks

compared to the square networks. This increase is because of the effects of porosity and

permeability, as explained above. It is evident from Fig. 11c, that the brine solution has the most

fracking fluid recovery. In all tests, the brine solution was the most effective at displacing crude

oil during hydraulic fracturing and imbibed the least during drawdown. Brine solution

outperformed slick water by ~7% and ~4% in the square and circular nanopore networks,

respectively. There was a drastic difference in the performance of DI water relative to slick water

as slick water recovery was ~9% and ~13% greater in square and circular networks, respectively.

It is apparent that the performance of a fracking fluid is best determined by its recovery potential,

as it encompasses a fracking fluid’s infiltration and imbibition capability.

Although brine solution is the most effective fracking fluid in terms of infiltration, imbibition, and

recovery, slick water performs similarly in the imbibition tests. However, slick water has many

advantages over brine solution, despite its experimental results. The additives in slick water are

essential to preventing the scaling, maintaining reservoir conditions and promoting shale oil flow.

Such advantages should be considered in addition to imbibition test performance when

determining a suitable fluid for hydraulic fracturing operations.

28

a)

b)

c)

Fig. 11. Results of the on-chip fluid imbibition testing: a) infiltration of each fracking fluid into the nanopore networks

during hydraulic fracturing; b) fracking fluid imbibition in the nanopore networks after drawdown; c) recovered

fracking fluid from nanopore networks after drawdown.

29

3.4 Conclusions

A nanofluidics-based method was developed to and applied to directly visualize and quantify the

dynamics of fracking fluids at the pore-scale (sub-200 nm confinement representing an oil shale

formation) during hydraulic fracturing. The infiltration, imbibition, and fluid recovery for three

different fluids including deionized (DI) water, brine (KCl solution), and slick water were

independently evaluated at two different porous media with circular and square grain shapes.

Comparing all fluids with two different geometries under the same operational pressure, showed

that the KCl solution infiltrates the most during the hydraulic fracking stage, with the minimum

imbibition and maximum recovery during the drawdown stage. In contrast, DI water has the least

efficiency in both infiltration and imbibition, resulting in the minimum fluid recovery in the

drawdown stage. Although the data and fluid comparison are notable contributions here, the

method presented here shows that the nanofluidic approach is well-suited to probe the pore-scale

of fluid dynamics in nanoconfined geometries with potential application for shale oil and tight

reservoirs. Most notably, nanofluidics can provide a window into this otherwise opaque, complex

process – a process that is reshaping energy worldwide with associated economic and

environmental implications.

30

3.5 Supplementary Information

3.5.1 Image Processing

Fig. S-1 below shows the processed binary image obtaining from original fluorescent image. In

the binary image, crude oil and fracking fluid are in white and black color respectively.

Fig. S-1: Image processing of a nanopore network imbibed with slick water after drawdown. Left: original fluorescent

image, crude oil in green, fracking fluid and chip geometry in black. Right: processed binary image, crude oil in white,

fracking fluid and chip geometry in black.

31

Chapter 4 Conclusions and Future Directions

1) Bitumen viscosity as a function of temperature and solvent concentration was measured using

a capillary viscometer. The novel capillary viscometer design demonstrated 95% accuracy

with third-party viscosity data and consumed 75% less sample volume for analysis.

Furthermore, the capillary viscometer featured instantaneous condensate mixing and real-time

viscosity results, reducing the labour requirements for viscosity testing. As a result, the effects

of temperature from 100 °C to 200 °C were evaluated under several bitumen-condensate

mixtures and compared to the viscosity profile of pure bitumen.

2) Though the capillary viscometer was validated by third-party data, the accuracy of viscosity

testing can be improved by submerging the bitumen supply line in the silicon bath for

improved temperature control. However, this recommendation would require a larger capacity

silicon bath to accommodate the supply lines and volume displacement cylinder.

3) Imbibition rates of three fracking fluids were visualized and quantified in a 175 nm nanofluidic

chip resembling an oil shale formation. Brine was determined as the best fracking fluid due to

its maximum infiltration rate during hydraulic fracturing and minimum imbibition rate after

drawdown; followed by slick water and DI water. The nanofluidic-based quantification

method showed an advancement in visualizing nano-scale fluid dynamics, well-suited for

shale oil reservoirs.

4) However, this application does not address the geological imbibition dynamics or

physiochemical fluid-rock interactions, such as clay hydration and osmosis. Research is

currently being pursued to fabricate microfluidic micromodels using geo-material for shale

rock experiments, which is the next evolutionary step in imbibition testing [44]. The presented

imbibition work can also be applied to shale gas reservoirs by controlling the nanopore

network geometries to smaller depths and tighter porosities.

32

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36

Appendix A

MATLAB Code for Quantifying Shale Oil Saturation

The following is a MATLAB code which quantifies the amount of remaining oil in a nanogrid by

comparing the number of fluorescing pixels to the total number of pixels in each image.

1. myDir = uigetdir; 2. myFiles = dir(fullfile(myDir, '*.jpg')); 3. for k = 1:length(myFiles) 4. baseFileName = myFiles(k).name; 5. fullFileName = fullfile(myDir, baseFileName); 6. I = imread(fullFileName); 7. I2 = rgb2gray(I); 8. I3 = imadjust(I2); 9. bw = im2bw(I3, 0.4); 10. totpx = numel(bw); 11. whtpx = bwarea(bw); 12. m(k,:) = [k totpx whtpx]; 13. figure('units','normalized','outerposition',[0 0 1 1]), 14. subplot(1,2,1); 15. imshow(I); 16. subplot(1,2,2); 17. imshow(bw); 18. imwrite(bw, [myDir, 'bw', num2str(k), '.jpg']); 19. end

Lines 1 and 2 access the file directory where the nanogrid experiments images are located and lists

the JPEG file contents in myFiles. The for loop iterates through each image in Line 3. The

number of iterations is determined by the length of myFiles, which represents the number of

JPEG files in the indexed array and therefore the number of loops required. Lines 4 and 5 retrieves

the image names, which can be helpful when troubleshooting. The respective image is read from

the file into MATLAB in Line 6. Image correction and analysis begins from this point.

Line 7 converts the original colour image to a 2D grayscale array containing values ranging from

0 to 255, representing the grayscale spectrum from black to white, respectively. The imadjust

function in Line 8 adjusts the image contrast and improves the accuracy of the binary conversion

in Line 9. The im2bw function converts the contrast adjusted grayscale image to a black and white

(therefore binary) image based on a threshold, in this case, 0.4. The output image is a 2D array

where the pixels with luminosity above than the threshold are set to 1 or white, and the those below

37

are set to 0 or black. The resulting image is shown in Fig. S-1, where crude oil is white and the

fracking fluid and nanogrid geometry is black. The number of pixels in the image (Line 10) and

number of white pixels (Line 11) are recorded in an array in Line 12 along with its iteration number

to identify the nanogrid. Lines 13 through 17 opens each image in myFiles with its corresponding

binary image for quality assurance purposes. Finally, Line 18 saves the binary image to the same

file directory as the originals.

The m array is copied over to Excel where the pixel counts of the respective nanogrids are

compared at all stages in the imbibition experiment. The infiltration (fracking) and imbibition

(drawdown) percentages are a quotient of the difference between the initial and remaining white

pixels during the experiment and the initial white pixels.