Microfluidic Analysis of Pore-Scale Petroleum Recovery in ......Microfluidic Analysis of Pore-Scale...
92
Microfluidic Analysis of Pore-Scale Petroleum Recovery in Steam Assisted Gravity Drainage Abdul Haseeb Syed A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Mechanical Engineering University of Toronto © Copyright 2015 by Abdul Haseeb Syed
Transcript of Microfluidic Analysis of Pore-Scale Petroleum Recovery in ......Microfluidic Analysis of Pore-Scale...
Microfluidic Analysis of Pore-Scale Petroleum
Recovery in Steam Assisted Gravity Drainage
Abdul Haseeb Syed
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science
Graduate Department of Mechanical Engineering University of Toronto
© Copyright 2015 by Abdul Haseeb Syed
ii
Abstract
Microfluidic Analysis of Pore-Scale Petroleum Recovery
in Steam Assisted Gravity Drainage
Abdul Haseeb Syed Master of Applied Science
Graduate Department of Mechanical Engineering University of Toronto
2015
Steam Assisted Gravity Drainage (SAGD) is an in-situ method of extracting bitumen and involves the
injection of steam into the oil sands reservoir through a horizontal well pair. The formation of a steam
chamber and heat transfer to the bitumen allows gravity drainage and production. There is presently a
lack of understanding concerning the specific mechanisms of bitumen mobilization and drainage at the
pore-scale within the SAGD process. This thesis focuses on developing and utilizing microfluidic
platforms to allow the experimental observation and characterization of the interface morphology at
the edge of the steam chamber.
iii
Acknowledgements
I would like to express my sincere gratitude to my supervisor, Professor David Sinton, who offered me
this wonderful opportunity to conduct my Master’s research in the very exciting and innovative field of
microfluidics. Professor Sinton’s insightful expertise, his support and advice helped me greatly to
achieve this work.
I would like to thank all the members of the team, in particular I would like to thank Nader Mosavat who
helped me during my experimental work and shared his valuable knowledge. My special thanks goes out
to Pushan Lele for his friendly collaboration and the many interesting conversations we had on a wide
range of topics, I also extend my gratitude to Seven Qi who spent many long hours helping me with the
very involved process of microchip fabrication. My thanks to Ryan Mendell who helped me understand
how to design reliable devices and demystify the fine details mechanical design. I would like to thank
Jason Riordon, Huawei Li, Bo Bao, Phong Nguyen, Tom Burdyny for providing their support whenever I
needed. I would like to thank Percy Graham, Matt Ooms and Scott Pierobon for their input.
I would like to thank my family immensely for giving me unlimited support!
iv
Table of contents
Abstract ................................................................................................................................ ii
Acknowledgements .............................................................................................................. iii
Table of Contents ..................................................................................................................iv
List of Figures ........................................................................................................................vi
1.0 Foreword ......................................................................................................................... 1
1.1 Motivation ................................................................................................................................. 1
1.2 Thesis Overview ........................................................................................................................ 2
2.0 Introduction to Microfluidics & Oil Sands ......................................................................... 4
2.1 The Potential of Microfluidics in Petroleum Research ............................................................. 4
2.2 Review of Oil Sands Resources & Extraction Technologies ...................................................... 5
2.2.1 Characteristics of Oil Sand Reservoirs ....................................................................... 6
2.2.2 Steam Assisted Gravity Drainage (SAGD) .................................................................. 8
2.2.3 Theoretical Model of SAGD and Research Topics ................................................... 10
2.2.4 Effect of IFT, Viscosity & pH on Petroleum Recovery .............................................. 14
2.3 Fluid Mechanics in Microfluidic Context ................................................................................ 15
2.4 Glass Micromodel Fabrication Method .................................................................................. 19
3.0 Micromodel Analysis of Pore-Scale SAGD Recovery ........................................................ 19
3.1 Experimental Apparatus & Platform Development ................................................................ 19
3.1.1 Experimental Apparatus ....................................................................................... 19
3.1.2 Micromodel Design ............................................................................................... 20
3.1.3 Manifold Design .................................................................................................... 22
3.2 Pore-Scale Analysis of Additive Effect on SAGD Interface ...................................................... 23
3.2.1 SAGD Micromodel Recovery ................................................................................. 23
3.2.2 Physical Morphology of Steam-Bitumen Interface ............................................... 27
v
3.2.3 Thermal Morphology of Steam-Bitumen Interface .............................................. 35
3.2.4 Pore-Scale Fluorescence Imaging of Steam-Bitumen Interface ........................... 40
3.2.5 Key Findings on the Nature of Pore-Scale Steam Front Dynamics ....................... 42
3.3 Bitumen Displacement from Gasification of Reservoir Liquid ................................................ 46
3.4 Emulsion Analysis & Segmentation Algorithm ....................................................................... 49
4.0 Conclusion & Future Directions ..................................................................................... 53
Appendix A: PEEK Manifold CAD Drawings .......................................................................... 54
Appendix B: Glass Fabrication Standard Operating Procedure ............................................. 56
Appendix C: Semi-Automated Emulsion Analysis Standard Operating Procedure ................. 65
Bibliography ....................................................................................................................... 80
vi
List of Figures
Figure 1 - Distribution of bitumen, water and sand grains within the oil sands deposits ............................ 6
Figure 2 – (A) Cross sectional view of Idealized SAGD steam chamber (B) Pore-scale SAGD recovery at
the interface (C) Side view of injection (red) and production (blue) horizontal wells in SAGD ................. 10
Figure 3 - Schematic representation of heat transfer & fluid flow at the steam chamber interface based
on the assumptions for the Butler equation (Author’s reproduction of figure from *22+) ........................ 11
Figure 4 - Glass chip fabrications steps with image of fabricated chip ...................................................... 18
Figure 5 – Schematic of experimental apparatus for SAGD micromodel ................................................... 19
Figure 6 – (A) Pore-scale SAGD recovery at the interface (B) Pore network pattern in micromodel ........ 20
Figure 7 – Unit cell of pore network pattern prior to etching (photomask, left) and after etching .......... 20
Figure 8 – Distribution of post and of pore throat within a single the unit cell of the pore network ........ 21
Figure 9 – Steel manifold is shown on the left and PEEK manifold on the right. ....................................... 22
Figure 10A – Time-lapse progression of Steam (0ppm additive) injection test ......................................... 24
Figure 10B – Time-lapse images of steam + 200pppm additive injection test ........................................... 24
Figure 10C - Time-lapse images of steam + 2000pppm additive injection test .......................................... 25
Figure 11 – Bitumen Recovery vs. Time ...................................................................................................... 26
Figure 12 - Final Recovery vs. additive concentration in ppm .................................................................... 26
Figure 13 – Steam chamber interface for steam (0 ppm), 200ppm, 2000ppm additive
(A – In-situ bitumen, B – Leading Edge, C – Lagging Edge, D – Steam Chamber) ....................................... 28
Figure 14 – Bitumen recovery through droplet generation at the interface (Time-lapse: 2 sec) .............. 30
Figure 15 – Finger Displacement of bitumen (Time-lapse: 1 min) ............................................................. 31
Figure 16 – Non-directional Gravity Drainage at the top of steam chamber (Time-lapse: 5min) .............. 31
Figure 17 – Emulsion generation under limited bitumen drainage rate at outlet port ............................. 32
vii
Figure 18A – Steam vapor bubble invasion & trapping into lagging edge ................................................. 33
Figure 18B – Maximum extent of steam invasion into lagging edge (steam test) .................................... 34
Figure 19A – Trapped Steam bubble condensation (Time-lapse: 2min) ................................................... 34
Figure 19B – Size of condensing trapped steam droplet vs. Time .............................................................. 35
Figure 20 - Time-lapse thermal images over the 160 minute duration of the tests .................................. 36
Figure 21 – Thermal & Physical Steam Chamber Comparison: Lagging edge acts as barrier to steam
chamber expansion .................................................................................................................................... 37
Figure 22 – Temperature profile at the interface ...................................................................................... 38
Figure 23 – Comparison of the rate of advance of leading & lagging edge of steam chamber ................ 39
Figure 24 – Fluorescent (bitumen = green) & Brightfield (bitumen = black) post-run microscope images .... 41
Figure 25 – Small water droplets are observed in the bitumen ahead of the leading edge ..................... 41
Figure 26 – Bitumen droplets drainage ahead of the interface ................................................................. 42
Figure 27 – Physical model allows evaluation of validity of interface assumptions .................................. 45
Figure 28 – Gasification of liquid volume over 45 minutes leads to 35% bitumen displacement ............. 47
Figure 29 – Pore-scale view of gas expansion (left) and liquid finger region (right) .................................. 47
Figure 30 – Fluorescence microscope images of bitumen displacement in gas & liquid region ................ 48
Figure 31 – (Top) Original image of emulsion (Bottom) Semi-automated analysis ................................... 50
Figure 32 – Semi-automated analysis particle size distribution (corresponding to Figure 31) .................. 51
Figure 33 – Automated image segmentation of the SAGD interface ......................................................... 52
1
1.0 Foreword
1.1 Motivation
Petroleum is a major component of the world’s energy supply. Petroleum is an energy dense, highly
portable fuel and is of particular importance for transportation. The ability to economically transport
people and freight over long distances is a key enabler of modern trade and economic activity. It was
estimated that in 2012, the combustion of petroleum fuels provided 92% of the energy used for
transportation in the United States [1]. Energy demand has grown 51.8% from 1985 to 2013 from 60,083
to 91,195 thousand barrels per day and is expected to climb further commensurate with an increase in
population and economic development in emerging economies [2, 3].
Recent data shows that conventional oil discoveries peaked in the 1960s with a steady decline in
subsequent decades, very few giant oil fields have been found since the early 1980s and 261 of the 507
known giant oil fields are already in their decline phase [4]. On the other hand, the volume of
conventional oil consumed has increased commensurate with industrialization and economic
development. The widening gap between the forecast demand and conventional productions means
that unconventional resources may have to be developed to meet demand. Some factors affecting the
future demand for petroleum includes the price of oil (with lower prices encouraging consumption) and
the development of alternatives to petroleum for transportation (with electric vehicles, ethanol-based
fuels lowering petroleum demand).
Due to the slowdown in new discoveries of large conventional oil fields and the continued increase in
petroleum demand, there has been increased interest in developing unconventional petroleum
resources. Greater development has taken place in terms of shale gas, heavy oil, carbonate reservoirs
and oil sands. However, extraction from the unconventional reservoirs presents a host of complex
technical challenges and significant investment has been made in research work to better understand
these new resources. Canada holds a large reserve of unconventional petroleum in the form of oil sands
with 169 billion barrels of proven reserves amounting to 98% of total Canadian reserves and giving the
country the 3rd largest petroleum reserves after Saudi Arabia and Venezuela. However, numerous
technical and environmental challenges must be overcome in order to develop the oil sands in a
sustainable manner [5].
2
Approximately 20% of the Canadian oil sands reserves can be extracted through mining as the deposits
are close to the surface but the remaining 80% require in-situ methods for recovery. Steam assisted
gravity drainage (SAGD) is an important extraction technique enabling in-situ recovery from oil sands
formations. SAGD essentially involves the injection of steam into the underground reservoir to heat the
bitumen and allow recovery through gravity drainage [6].
Research into the SAGD process has mostly occurred in the form of simulation studies or core flood
experiments. A significant limitation of core flood tests is the inability to visualize exactly what is
happening at the advancing steam interface; furthermore it is difficult to reproduce consistent
experimental conditions due to core changes through chemical deposition or permeability damage.
Simulation models require some level of simplification of the process since it is difficult to model multi-
phase flow while also accounting for heat transfer, phase change, steam condensation and a moving
steam front. Solving simulation models may take a long time and it is also be difficult to history match
simulation results with field data, specifically at the pore scale.
Microfluidic pore-scale models provide an important tool to study oil recovery beyond simulations and
core floods. The micromodel provides a platform for direct experimental observation and
characterization of the specific mechanisms associated with oil recovery from the reservoir at the pore-
scale. The objective of our work is to develop and use a microfluidic platform to investigate the
mechanisms of bitumen displacement and drainage at the pore-scale within the context of the SAGD
process. The effect of alkaline additive on the bitumen recovery was also investigated.
1.2 Thesis Overview
Chapter 1 begins by presenting the motivation for this work followed by an overview of the thesis.
Chapter 2 is an introduction to microfluidics and oil sands. A review of oil sand characteristics and the
steam assisted gravity drainage (SAGD) process is provided, along with a review of relevant microfluidic
concepts and glass micromodel fabrication methods.
Chapter 3 discusses the experimental apparatus as well as the characteristics of the pore network in the
micromodel. Section 3.2 presents the main study conducted on the pore-scale analysis of the SAGD
interface and the effect of alkaline additive at various concentrations. A second study of bitumen
3
displacement through porous media as a result of gas generation from a trapped liquid volume is
presented in section 3.3. Semi-automated methods for image and emulsion analysis developed during
the course of the research are discussed in section 3.4.
Chapter 4 explains future directions that could be pursued based on the high temperature-pressure
micromodel developed in this work.
4
2.0 Introduction to Microfluidics & Oil Sands
2.1 The Potential of Microfluidics in Petroleum Research
Microfluidics is a multi-disciplinary field of research that focuses on the manipulation and study of small
volumes of fluids, typically 10-9 to 10-18 liters, at small scales. A microfluidic device typically consists of a
portable chip that contains a network of channels, junctions and components with a geometry that
allows the processing of injected fluids. Microfluidic devices represent a miniaturization of fluid handling
systems that allow accurate fluid metering, transport, mixing, and separation. These devices have been
manufactured from a range of materials including glass, polymer (PDMS, PMMA, and Teflon), silicon,
paper and even carbonates. [7-10].
Inkjet printers in the 1950s marked an important milestone in the accurate handling of small volumes of
fluid; the precursor to modern microfluidics was the gas chromatograph technologies of the 1970’s
which allowed for the first time very high resolution and sensitivity measurements [11, 12]. Microfluidics
today is used as a platform for research in a wide variety of fields ranging from molecular biology to
solubility analysis to cooling of integrated circuits [13, 14]. Microfluidics research can either focus on
developing new capabilities in terms of small scale fluid handling or on studying the science of fluid
behavior at the small scale. Microfluidics devices provide an effective platform to investigate research
topics on the behavior of fluids at the micrometer or nanometer scale.
Microfluidics technology has the potential to play an important role in the petroleum sector in the
context of both laboratory research and field applications. Oil samples are a valuable resource and there
are economic and logistical benefits in reducing the sample fluid volume required in petroleum research
and testing. Microfluidics provides an ideal solution to allow the extraction of meaningful data from
small volumes of oil using an inexpensive and portable device.
For instance, Hossein et al developed a microfluidics device to perform CO2 diffusivity measurements in
bitumen and reduced the time required for these measurements from days to 10 minutes and the fluid
requirements from 0.5L to 1nL [14].
Some commercialization of lab-scale microfluidic equipment has already taken place. One example that
is relevant for the petroleum industry is the measurement of asphaltenes. Crude oil is a complex mixture
of a wide variety of hydrocarbon fractions and one of the heaviest and most aromatic fractions in
5
petroleum is the asphaltene fraction. The manual method for asphaltene measurement is prone to
human error since fluids are dispensed manually and asphaltene precipitation is observed by eye. A
compact and portable microfluidic device was developed by Fraunhofer for asphaltene analysis. The
device is able to dispense precise volumes of fluid and uses multi-wavelength imaging techniques to
identify asphaltene precipitation and is able to remove subjectivity from the process [53].
An important aspect of microfluidics is the ability to fabricate chips that incorporate a complex network
of pores from transparent materials while retaining the ability of the device to withstand high
temperatures and pressures. Due to the steady depletion of previously established oil fields, new
ventures in petroleum extraction are increasingly tapping into unconventional petroleum resources with
complex micro-scale geometries including shale gas, heavy oil, carbonate reservoirs and oil sands.
The precise mechanism of oil recovery at micrometer scales within porous media in unconventional
reservoirs is not well understood and microfluidics can be a useful tool in this context since it allows
direct visual observation of oil recovery throughout the model.
The value of microfluidic tools is that they provide a smaller, transportable, inexpensive method for
petroleum testing with much lower sample volume requirements. Microfluidics can especially useful as
a screening tool for more comprehensive tests, leading to significant reduction in the misallocation of
time and capital.
2.2 Review of Oil Sands Resources & Extraction Technologies
Canadian oil sands are a massive resource with an estimated 1.8 trillion barrels of bitumen in-place of
which 168.7 billion barrels are considered to be reserves that can be economically recovered using
current technology. Canada also initially had 18 billion barrels of conventional oil reserves of which 16.5
billion barrels has been produced and a further 1.5 billion remains to be produced [15, 16]. The oil sand
deposits are largely located in northern Alberta; the largest fields are Athabasca, Peace River and Cold
Lake.
6
2.2.1 Characteristics of Oil Sand Reservoirs
Petroleum deposits are formed by the decomposition of organic matter at a suitable depth and
temperature over geological time scales. The oil sands in Alberta are thought to have been formed by
the same geological processes that lead to the formation of the Canadian Rocky Mountains. The exact
process by which the oil sands were formed is a matter of debate, one possibility is that the rise of the
Pacific tectonic plate over the North American tectonic plate may have caused the deep burial of the
sedimentary rock layers of the Alberta plains and the subsequent conversion of kerogen into light oil
and natural gas took place due to the high temperatures. The light oil then migrated up through hydro-
dynamic transport and microbial action near the surface resulted in the degradation of the light oil into
bitumen [17, 18].
Oil sands deposits consist of highly viscous bitumen hydrocarbons trapped in the porous volume
between unconsolidated sand grains in underground formations. A schematic representation of oil
sands is provided in Figure 1, showing also the presence of small pockets of trapped water between
grains. Fine clusters of clays are also present but not shown in the figure. Various studies suggest the
existence of a thin connate water film, of approximately 10 nm, surrounding the sand grains [19, 23-25].
Figure 1 – Diagram of distribution of bitumen, water and sand grains within the oil sands deposits
The oil sand deposits are found at a depth of up to 700 m, with reservoir temperature between 8˚C and
20˚C and the initial reservoir pressure is on the order of 300 to 500 kPa [21, 27]. The bitumen-rich
region of the oil sands deposit that is viable for extraction is known as the pay zone and the pay
thickness is on the order of 10 to 40 m, with a minimum thickness of 10 m for a field to be considered
economically viable [26].
7
The quality of the oil sands ore can vary significantly between fields. Typical ore will consist of 85 wt%
sands grains and fine particulates, 12 wt% bitumen and 3 wt% water. The quartz grains are angular in
shape and range from 160 µm to 250 µm in diameter [21, 47]. There are also clusters of fine particles
including kaolinite, smectite and illite that are generally less than 2 µm in diameter. Lower grade oil sand
ore may contain more than 20 wt% of fine clays while higher grade ore will have less than 5 wt% [22].
Typical oil sands porosity varies between 30-35% and permeability is typically on the order of 1 to 6 D
[27, 21]. Most in-situ bitumen contains some amount of dissolved natural gas and there are also small
pockets of gasses within the reservoir [22]. The study of oil sand characteristics is complicated by the
difficulty in analyzing the ore underground and the tendency for changes to occur within the ore when it
is removed from the reservoir and disturbed by depressurization [20, 21].
Various studies have suggested the presence of water in the oil sands deposits, which may exist
between the sand grains, within clusters of fine clays or as a thin film of connate water over the surface
of the quartz grains. The connate water layer is estimated to be approximately 10nm thick but is
sufficient to ensure no direct contact between the bitumen and quartz. This layer of water, combined
with the hydrophilic nature of the quartz, plays an important role in enabling the easy recovery of
bitumen since the sand is not oil-wet. It should be noted that oil sands reservoirs exhibit significant
heterogeneity. Frequent variations in reservoir geometry and bitumen properties complicate field-scale
recovery.
The form of oil found in the oil sands is an extremely viscous hydrocarbon substance known as bitumen.
While properties vary between fields, typical bitumen will have an elemental composition of
approximately 83.2% Carbon, 10.4% hydrogen, 0.94% oxygen, 0.36% nitrogen and 4.8% Sulphur. The
viscosity of in-situ bitumen in the reservoir typically exceeds 1x106 cP and can in certain locations go
higher than 6x106 cP [28]. Bitumen density is on the order of 1.01 g/cm3 at room temperature and
pressure. The very high viscosity of the bitumen is one of the primary factors making bitumen recovery
and processing more difficult [21, 29].
API gravity is an important measure for the classification of oil. The API gravity, standardized by the
American Petroleum Institute, represents the relative density of a substance compared to the density of
water. API gravity has no units but is expressed in degrees. Oil with an API gravity of more than 10˚ will
float in water and less than 10˚ will sink in water. API gravity is used to categorize oil into light (>31.1˚),
medium (22.3 ˚ - 31.1 ˚), heavy (10 ˚- 22.3 ˚) and extra-heavy (<10 ˚). Bitumen from Canadian oil sands
8
formations has API gravity ranging from 6 to 12 and is considered a form of extra-heavy oil [30, 31]. The
equation for API gravity is as follows:
˚API = API gravity SG = specific gravity
The constituent components of bitumen are often separated, on the basis of chemistry, into saturates,
aromatics, resins and asphaltenes. Saturates include non-polar linear, branched and cyclic saturate
hydrocarbons, aromatics contain aromatic rings and are more polar, resins and asphaltenes are large
aromatic molecule. Asphaltenes precipitate out of the oil in excess pentane (C5 asphaltenes) or heptane
(C7 asphaltnenes).
The tendency of asphaltenes to precipitate on process equipment due to high temperatures or changes
in composition (such as addition of saturates) presents significant operational challenges in the
transport and processing of bitumen. Athabasca bitumen will typically consist of 14wt% saturates,
34wt% aromatics, 38wt% resins and 12wt% asphaltenes [30, 32].
2.2.2 Steam Assisted Gravity Drainage (SAGD)
Mining of the oil sands ore is viable only for fields located at a depth of less than 70m from the surface.
From the 169 billion barrels of bitumen in reserve, only 20% can be recovered through mining and the
remainder must be produced by in-situ methods [5, 33].
In-situ recovery of bitumen has been a practical engineering problem with substantial economic
implications. Conventional water flooding is not viable since bitumen has a viscosity in excess of
1,000,000 cP at reservoir conditions. Furthermore, even in heavy oil reservoirs, the injected water has a
tendency to exhibit significant fingering displacement, resulting in a large portion of residual oil
remaining in the reservoir [42].
A major development enabling the economical extraction of Alberta oil sands on a large scale was the
development of horizontal wells and steam injection. Roger M. Butler developed the process of steam
assisted gravity drainage (SAGD) in the 1970’s and participated in the pilot plant testing of SAGD in 1978
9
in the Cold Lake deposit. The SAGD process has since been applied on a commercial scale and in 2009
accounted for 242,000 barrels per day of production (18% of the total production) and is expected to
increase in the future as minable reserves are depleted [22, 34].
Steam assisted gravity drainage is a thermal extraction process that begins with the drilling of two
horizontal wells vertically aligned with each other inside the pay zone, steam is injected through the top
well leading to the formation of a steam chamber after an initial start-up period. The steam condenses
at the edge of the chamber resulting in the transfer of the latent heat of condensation to the bitumen
and the subsequent drainage of the lower viscosity heated bitumen due to gravity; Figure 2 illustrates
this process. As the as the bitumen is drained and produced, the empty pore space becomes filled with
steam. The steam chamber grows upwards and sideways over time. A large number of factors affect
SAGD and can broadly be separated into three categories: reservoir properties, bitumen characteristics
and steam parameters. Reservoir and bitumen properties are discussed in more detail in 2.2.1.
The high energy requirements associated with steam generation are a major cost of the SAGD process
and there is strong interest in reducing the energy intensity of SAGD. The steam-oil-ratio (SOR) is a key
measure of efficiency that compares the barrels of water (turned into steam) required for the recovery
of one barrel of bitumen. SOR can be between 4 to 5 during the initial start-up phase of the SAGD
process and can drop down to 2-3 a few years into production [35].
The SOR can be measured as either Cumulative steam-oil ratio (CSOR) averaging the volume of steam
required per barrel over the lifetime of the SAGD process or Instantaneous steam-oil ratio (ISOR) which
measures the number of barrels of water required at any given time to produce a barrel of oil. The SOR
will be high during the initial phases of the SAGD project due to the start-period that requires the
injection of steam to create the steam chamber prior to production [34].
10
Figure 2 – (A) Cross sectional view of Idealized SAGD steam chamber (B) Pore-scale SAGD recovery at the
interface (C) Side view of injection (red) and production (blue) horizontal wells in SAGD
2.2.3 Theoretical Model of SAGD and Research Topics
As stated earlier, Roger M. Butler developed the process of SAGD in the 1970’s and established the basic
model and theory. The Butler equation forms the basis for the theoretical understanding of the SAGD
process and will be discussed in this section. Many modifications of Butler’s equation exist but the focus
will be on the original form of the equation since it provides a good starting point for the exploration of
SAGD models. The full derivation of the Butler equation can be found in Thermal Recovery of Oil and
Bitumen, chapter 7, only a brief overview will be provided below [22].
11
Due to the hot steam rise, the chamber is expected to initially grow faster in the upward direction,
however the morphology of the typical reservoir as an elongated layer would cause the upwards growth
to be stopped by the overburden and the sideways growth of the chamber to become the dominant
aspect. Figure 3 shows a schematic view of a small section at the interface as defined in the context of
Butler’s equation, the steam chamber is on the left with the in-situ bitumen on the right.
Figure 3 - Schematic representation of heat transfer & fluid flow at the steam chamber interface based
on the assumptions for the Butler equation (Author’s reproduction of figure from *22+)
The steam is at temperature Ts and the reservoir initial temperature is Tr. Steam is condensing on the
interface which is inclined at angle θ. The interface temperature is equal to the steam temperature Ts
and heat flux due to conduction occurs into the bitumen. The interface is moving into the reservoir at a
velocity of U as a result of the difference in oil flow into section (from upstream drainage along the
interface) and the flow out of the section (due to bitumen drainage). The advance of the interface gives
rise to oil production of Q.
The full derivation of the equation can be found in [22]. The basic framework is that Darcy’s law is used
to obtain an equation describing the rate of drainage of oil, dQ, with respect to the element dξ. A
second relationship is established between the flow of oil and the interface velocity by considering the
material balance at the interface. The two equations together are used to eliminate the unknown terms
and yield the Butler equation for oil recovery for SAGD:
12
√
Q = oil production rate
φ=porosity
∆S0 = initial oil saturation - residual oil saturation
νs =oil kinematic viscosity
m = dimensionless factor between 3-5 (based on viscosity-temperature relationship of the oil)
It is interesting to note that all of the terms in the numerator of the Butler equation are equally
weighed, meaning that the porosity, permeability, gravitational force, thermal diffusivity and reservoir
height are all equally important to bitumen drainage according to this model.
The ∆S0 term must be computed in order to solve the equation above. Butler’s theory states that the
viscosity of the residual bitumen in the steam chamber is very low, leading to continued drainage and a
lower fraction of residual oil remaining in the reservoir. The initial oil saturation would be known (from
reservoir geological assessment) and the residual oil saturation could be found using:
(
) ⁄
Sor = average residual bitumen saturation after time t νs = oil kinematic viscosity
φ = porosity k = permeability
g = gravitational force Z = drainage height
b = exponent in Cardwell and Parson's relative permeability equation
h = Net pay thickness
α = reservoir thermal diffusivity
g = gravitational force
k = permeability
13
With b set to a typical value of 3.5 and Z set to equal h (the maximum possible value of Z):
(
)
The various theoretical models of SAGD (including the Butler equation) make assumptions regarding the
conditions at the interface of the steam chamber. The current understanding of the SAGD process is
limited and a number of concerns remain with the basic model. One aspect under scrutiny is the
assumption that conduction is the dominant means of heat transfer and that convection plays a minimal
role. Studies by Ito et al and Farouq-Ali et al [38, 39] suggest that convection from the flow of
condensates at the interface does in fact play a significant role in bitumen production and that assuming
heat transfer to occur exclusively through conduction is not viable. Studies by Edmunds et al and Irani et
al [36, 37, 40] indicate that the contribution of convection to bitumen recovery is minimal.
The mechanics of bitumen drainage from the top of the steam chamber are another point of debate.
The morphology of steam rise through the reservoir and the counter-current bitumen drainage is not
well understood [49, 48]. The role of condensate flow in the bitumen drainage is not clear and a
simulation study by Ito et al claims that the condensate flow adjacent to the bitumen is a major factor in
encouraging bitumen recovery [39]. Geomechanics of the steam chamber expansion is another
significant area of research, the high pressure and temperature of the steam chamber has an impact on
the rock remaining within it. Studies estimate that shear failures of sand grains due to stresses within
the steam chamber leads to increases in permeability and promotes continued chamber growth [41].
As discussed above, many of the uncertainties pertaining to the dynamics of bitumen recovery from
SAGD stem from the lack of understanding of the specific interface morphology between the advancing
steam front and the bitumen. Our micromodel work with SAGD would help to get a better
understanding about the specific dynamics of steam-condensate-bitumen interactions at the steam
chamber edge and would help to evaluate the validity of assumptions made in various models. The
experimental data from our physical model could eventually lead to improved simulation of the steam
chamber interface.
Micromodels have previously been applied in the context of petroleum recovery for carbonate
reservoirs [10], CO2 EOR [42], petroleum asphaltenes deposition [43] as well as SAGD [44, 45, 46].
14
However, the limitation of the previous work was that either the pore sizes were not representative (at
1mm+), the reservoir fluid was not representative of Alberta bitumen or that the steam parameters
were not representative (due to steam injection at 100˚C and atmospheric pressure). Our present work
ensures reservoir-relevant geometries, since our micromodel grain sizes of 260 µm to 380 µm
correspond with average grain sizes in the field of 160 µm to 250 µm [21, 47]. Our study included
significant platform development that allowed the injection of steam at higher temperatures of more
than 150˚C and pressures on the order of 340 kPa.
2.2.4 Effect of IFT, Viscosity and pH on Petroleum Recovery & Emulsion Formation
At the interface between two immiscible liquids, the imbalance in molecular interactions between
dissimilar fluids gives rise to an accumulation of free energy at the interface known as the interfacial
tension (IFT). The IFT is measured in units of dynes/cm or mN/m, where 1 dyne/cm = 1mN/m, and
represents the amount of work that needs to be done to expand the interface between the two phases
isothermally. Therefore, a larger value for IFT signifies that there is a stronger tendency for the interface
between the immiscible liquids to be minimized. IFT can be measured while the interface between the
phases exists in a transient state, termed as dynamic IFT, or while the interface exists in a steady
equilibrium. [54,55]
Since IFT is a measure of the work required to expand or disturb the interface, a lowering of IFT enables
easier displacement of the oil from the pore network as the interface is less resilient to perturbation.
Another important aspect is that for gravity drainage to occur, IFT must be lowered such that capillary
forces in the pores become less significant in relation to gravitational forces. Reducing IFT between the
bitumen and the condensate leads to improved gravity drainage. [54, 55]
A surfactant consists of a single molecule with a polar head and non-polar tail works to reduce the free
energy at the interface by aligning between the water and oil phases. The polar component is oriented
into the water while the non-polar component, often consisting of hydrocarbon chains, aligns with the
oil phase. The presence of a surfactant reduces the imbalance of forces felt at the interface and reduces
the interfacial tension. An important aspect of surfactant dynamics is the critical micelle concentration
(CMC) which indicates the concentration of surfactant that is required to saturate the interface and
above which significant micelle formation in the bulk phase. A low CMC denotes high efficiency of the
15
surfactant in saturating the interfaces. The reduction in surface tension is less pronounced with
increased surfactant concentration beyond CMC. [56-60]
Alkaline additives can be injected into the reservoir in order to react with the acidic components within
the oil and produce surfactants, lowering IFT. The acid content in petroleum is measured by the Total
Acid Number (TAN) which corresponds to the mass of potassium hydroxide (in miligrams) that is
required to neutralize the acid in one gram of oil and is expressed in units of mg/g. The TAN in
Athabasca bitumen typically ranges from 4 to 5.5 mg KOH/g. [61, 62]
A study has found that the surfactant-producing effect of alkaline flooding, within the context of
petroleum extraction, is generally observed at pH ranges above 9 and that a higher acid number may
not correlate directly with increased in-situ surfactant production. The precise nature of the acidic
components present within the oil determines the effectiveness of the produced surfactant in lowering
the interfacial energy. For instance, asphaltenes or resin molecules may have carboxylate functional
groups that cannot be extracted into the aqueous phase. Furthermore, short chain acids may not
produce effective surfactants because they are too hydrophilic since the short hydrocarbon chain
cannot effectively align with the non-polar phase. [63, 64] The naphthenic acids in bitumen are
predominantly cycloaliphatic carboxylic acids, comprising 10 to 16 carbons, and form an important
surfactant-generating component. [65]
2.3 Fluid Mechanics in Microfluidic Context
Reynolds Number
The Reynolds number is a ratio of the inertial forces acting on a fluid to the viscous forces. A fluid or
geometry that results in a larger Reynolds number is more likely to exhibit turbulent flow because the
effect of the inertial forces acting on the fluid will dominate. A Reynolds number less than 2300 signifies
laminar flow, and greater than 4000 will correspond to turbulent flow. Reynolds numbers between 2300
and 4000 result in a transient regime. In the context of microfluidics, the channel dimensions will
typically be on the order of tens to hundreds of microns and most fluid flow within microfluidic devices
will be laminar. The Reynolds number for flow in a channel is computed as:
16
Re = Reynolds number A = Cross sectional area
μ = dynamic viscosity ν = kinematic viscosity
ρ = density v = mean velocity
DH = hydraulic diameter Q = Volumetric flow rate
Capillary Number
The capillary number represents a ratio of viscous forces to surface tension on a gas-liquid interface. The
capillary number is important in understanding the behavior of gas-liquid interfaces in micro-channels.
For a flowing liquid, a capillary number much larger than 1 indicates that viscous forces are dominant.
However, if the capillary number is much lower than 1 then viscous forces are negligible compared to
the interfacial forces.
Ca = Capillary Number μ = dynamic viscosity
V = characteristic velocity γ = surface tension
Darcy’s Law
Darcy's law describes the flow of a fluid through porous media of known permeability. Darcy's law is
valid only when the flow is laminar, under steady-state flow conditions and the porous media is
saturated. Another assumption is that the fluid is homogenous, isotherm and incompressible. Kinetic
energy is neglected which is acceptable for low fluid velocities.
Q =Volumetric flow K = Permeability
17
A = Cross sectional area μ = dynamic viscosity
dP/dx = Pressure gradient
Bond Number
The bond number is a ratio of gravitational force to surface tension. A large value for the bond number
indicates that the role of the interfacial tension is minimal while a bond number of less than one
signifies that interfacial tension plays an important role in the system. Experimental studies have shown
that a large bond number has a positive effect on petroleum recovery through gravity drainage [50, 51].
g = gravitational acceleration ∆ρ = density difference between phases
L2 = characteristic length σ = interfacial tension
2.4 Glass Micromodel Fabrication Method
The main steps involved with the fabrication of the glass micromodel are detailed in Figure 4.
Fabrication of devices with a large area of micro-scale features is challenging and requires a very high
degree of care in the handling of the glass, even a small force is sufficient to damage the micron-scale
pores during the fabrication process. A comprehensive standard operating procedure was developed in
order to ensure maximum success rate during fabrication (see Appendix A). Pre-coated square Borofloat
33 glass slides of 10 cm x 10 cm x 2.25 mm in thickness were obtained from Telic.
Step 1 in Figure 4 shows the original glass is polished and pre-coated with a 300 nm thick chrome layer
and the photoresist AZ1500 is coated above it to a thickness of 5300 Angstrom. A photomask is also
printed with the desired pore pattern on a transparent polymer sheet. Step 2 involves the exposure of
the pattern whereby the portion of the photoresist not covered by the photomask is damaged. AZ400K
developer is used to remove the damaged photoresist (step 3) and the chip is baked on a hot plate at
130˚C for 30 minutes to harden the photoresist.
18
Step 4 shows that the glass is then immersed in de-chromer in order to remove the exposed chrome.
After this step, the glass itself will be unprotected in the pattern of the pore space while the photoresist
and chrome will cover and protect the area corresponding to the grains.
Step 5 involves the etching of the glass using hydrofluoric acid solution prepared from a 1:3:5 ratios of
49% HF: HNO3: H2O. As displayed in the figure, the etching occurs in an isotropic manner, meaning that
the exposed glass is etched both in depth and sideways. This will result in a shrinking of the grains in the
pore network and the mask must be designed to accommodate this effect.
The etched glass is then cleaned with piranha solution (1 H2O2: 2 H2SO4), which will remove the
photoresist. The exposed chrome is also removed with de-chromer (step 6) and piranha cleaning is
repeated. The etched glass is then taken to an optical profilometer in order to measure the exact
etching depth. The bottom left corner of Figure 4 shows an optical profilometer measurement. The
target etch depth is 50 µm for our micromodel this is achieved within +/-5%. The final step involves the
thermal fusion bonding of the cover glass on top of the pore network at 645°C for 10+ hours. An image
of a fabricated chip is shown at the bottom right.
Figure 4 - Glass chip fabrications steps with image of fabricated chip
19
3.0 Micromodel Analysis of Pore-Scale SAGD Recovery
3.1 Experimental Apparatus & Platform Development
3.1.1 Experimental Apparatus
A schematic of the experimental apparatus is shown in Figure 5. A Teledyne-ISCO pump is used to feed
the water into the steam generator at a controlled pressure or flow rate. The steam generator produces
steam at a constant set temperature which is then fed into the micromodel through 1/8 inch stainless
steel tubing. The steam flows from the injection port at the top of the model towards the outlet port at
the bottom. A bitumen trap is located between the outlet and the back-pressure regulator (BPR) in
order to prevent bitumen clogging of the small membrane. There is also a bypass line in order to allow
the establishment of a stable steam temperature and pressure prior the injection into the chip.
Figure 5 – Schematic of experimental apparatus for SAGD micromodel
20
3.1.2 Micromodel Design
A glass micromodel is used in this study due to the requirements for the model to withstand high
temperatures and pressures while also being optically transparent. The microfluidic pattern, shown in
Figure 6, on the right side, was used to approximate the sand grain and pore network from the oil sands
reservoir. As shown in Figure 7, the basic unit cell of the pattern consists of a double-hexagon
arrangement of circular posts where there is a significant variation in the sizes of the posts in order to
create a range of pore sizes.
Figure 6– (A) Pore-scale SAGD recovery at the interface (B) Pore network pattern in micromodel
Figure 7 – Unit cell of pore network pattern prior to etching (photomask, left) and after etching
(micromodel, right) Red text signifies post diameters and grey text for pore sizes (dimensions in µm)
21
Circular posts were chosen to allow the even filling of the bitumen. The pattern was obtained by tiling
the double-hexagon unit cell and changing the orientation of the internal posts at random, thus
preventing directional permeability bias while maintaining a homogenous pore network. The posts and
pore size distributions are shown in Figure 8. The posts range in size from 260 to 380 µm with an
average of 336 µm being comparable to the average grain sizes in the field of 160 µm to 250 µm [21,
47]. Pore sizes range from 112 µm to 170 µm with an average of 134 µm. Every point on the micromodel
is etched to a depth of 50 µm and this is the smallest dimension occurring within the model. Therefore,
in addition to the pore and post geometry, the etching depth of 50 µm plays an important role in
defining fluid dynamics and the physics within the micromodel.
As described in the fabrication section 2.4, a photosmask is used to expose the pattern on the
photoresist and this pattern is later etched isotropically in hydrofluoric acid solution. The highest
printing resolution for the photomask allows a minimum feature size of 10 µm. The smallest pore on the
photosmask is 11.6 µm and isotropic etching to a depth of 50 µm causes the widening of the initial pore
by 100 microns laterally, the final dimension of this pore in the fabricated micromodel is 111.6 µm.
Figure 7 illustrates the pore expansion due to etching of the unit cell. Isotropic etching is the primary
limitation of glass micromodels, thus channels and pores cannot have a large aspect ratio. However,
glass was found to be the only suitable material for this study due to the need for an optically
transparent model that could tolerate high temperatures and pressures.
Figure 8 – Distribution of post sizes (expressed as diameter) and of pore throat sizes (rounded to the
closest µm) within a single the unit cell of the pore network
22
The micromodel has a porosity of 50% which is significantly higher than literature data of up to 35% in
oil sands reservoirs. Within the context of isotropic glass etching, a lower porosity could be achieved via
a large increase in the size of the posts; however the posts (simulating sand grains) would not be
comparable to oil sands reservoir geometry if their size was increased. Moreover, it is not practical to
reduce the porosity by reducing the etching depth since a very thin micromodel would not allow for
gravity drainage and not would be representative of the reservoir. The pore network design used for this
model was selected on the basis of minimizing porosity while maintaining reservoir-relevant grain and
pore sizes.
3.1.3 Manifold Design
A significant amount of platform development was required on this project. Most of the engineering
challenges arose from the need to handle steam while maintaining a constant temperature and pressure
as well as working with bitumen. A custom manifold was designed to allow for the exchange of fluids
with the microfluidic chip.
The first version of the manifold was fabricated from stainless steel and extensive insulation was used
during the test in order to minimize heat loss. However, significant heat loss to the steel occurred due to
close contact with the glass. A polymer-based manifold was developed to allow steam injection with
minimal heat loss. Polyether ether ketone (PEEK) was selected since it has good mechanical properties
and can withstand high temperatures. The PEEK manifold, combined with extensive insulation, enabled
the injection of steam into the chip with minimal heat loss.
Figure 9 – Steel manifold is shown on the left and PEEK manifold on the right.
23
Another challenge encountered was the need to fill bitumen into the micromodel. Bitumen is a very
thick fluid with a viscosity in excess of 1,000,000 cP at room temperature, and the micromodel has a
depth of 50 μm. The chip-filling with bitumen was achieved by immersing the glass chip in 80°C water
and injecting bitumen slowly at the elevated temperature. The front of the apparatus can be insulated
using a Calcium Fluoride (CaF2) window that allows transmission in both the IR and optical spectrum.
Also, the glass micromodel was initially fabricated from 1.1 mm thick glass, which was not able to
withstand the test pressures. Thicker 2.25 mm thick glass was subsequently utilized.
3.2 Pore-Scale Analysis of Additive Effect on SAGD Interface
The microfluidic platform shown in Figure 5 was used to investigate the effect of an alkaline steam
additive on SAGD interface morphology and recovery profile. The name of the additive cannot be
disclosed due to confidentiality. Three solutions of DI water and the alkaline additive are prepared with
different concentrations of 0 ppm (pure DI water), 200 ppm and 2000 ppm additive concentrations.
For each test, the solution was fed into the Teledyne-Isco pump and injected into the steam generator in
order to produce steam. The steam generator and the line heaters were set to the desired temperature
and the ISCO pump was operated on a constant pressure setting. The operator chooses to flow the
steam either through the chip or the bypass using a pair of 3-way valves. Initially, the steam is circulated
through the bypass line in order to establish stable temperature and pressure conditions for 20 minutes.
The pressure builds up behind the back-pressure regulator (BPR) membrane until it reaches the desired
pressure and then steam flow proceeds out of the BPR and into a steam collection vessel. The piston in
the pump compresses and expands in order to achieve the set constant pressure. Thermocouples are
located at various points on the stainless steel tubing and pressure gages are in place at the inlet and
outlet of the micromodel.
3.2.1 SAGD Micromodel Recovery
Three injection tests were done with steam, steam + 200 ppm additive and steam + 2000 ppm additive
at 155˚C and 344 kPa for duration of 160 minutes. The resulting recovery profile and evolution of the
steam chamber are shown in Figure 10A, 10B and 10C respectively. When steam is injected into the
24
micromodel, a steam chamber develops which is larger at the top of the model and narrower at the
base towards the outlet. The high temperature steam heats up the adjacent interface and a steady flow
of bitumen down to the outlet port is observed. Steam has the lowest recovery followed by the 200 ppm
then 2000 ppm additive test with the highest recovery.
Figure 10A – Time-lapse progression of Steam (0ppm additive) injection test
25
Figure 10B – Time-lapse images of steam + 200pppm additive injection test
Figure 10C - Time-lapse images of steam + 2000pppm additive injection test
The general shape of the chamber can be explained by considering that a specific volume of bitumen
which drains from the top of the micromodel will fill the lower section of the model on its way to the
26
outlet. Moreover, the outlet is a single point and this leads to an increase in the slope of the interface at
lower model height. The conditions within the micromodel broadly match with the conditions observed
at the edge of the steam chamber in the SAGD process.
The advancing steam forms a distinct front during the early phase of injection with steam behind and
bitumen ahead of the interface. However, the steady development of a ‘drainage layer’ with residual
bitumen at the interface occurs in all cases. The drainage layer appears to be most pronounced with the
steam injection case and is also larger in the 2000 ppm run that the 200ppm. The layer is thicker at the
top of the model than at the base for the additive tests but not for the steam test. The exact nature of
bitumen drainage and fluid flow within this layer is discussed in section 3.2.2.
Figure 11 shows the recovery curves plotted against time for each test. The recovery is calculated from
the runtime images of the chip since the produced volumes are too small to collect. The high resolution
Nikon camera is able to capture the pore-scale advance of the steam front and thus allows accurate
measurement of the recovery. Bitumen production is expressed as a percentage of the total pore
volume. The shape of the recovery curve is similar for all the runs with a high initial rate of production
followed by a steady decay. The steam injection temperature and pressure were maintained constant
throughout the test. The steam case has the lowest recovery with 25.6% final recovery while the 200
ppm additive shows a slight improvement at 33.2% of total pore volume produced at 160 minutes of
runtime. The 2000 ppm additive test shows the greatest improvement in total recovery at 68.8% of the
micromodel volume produced after 160 minutes of steam injection.
27
Figure 11 – Bitumen Recovery vs. Time
Figure 12 - Final Recovery vs. additive concentration in ppm
Figure 12 shows the final recovery for each test plotted against the additive concentration. The
relationship between bitumen recovery and the parts-per-million concentration of the additive appears
to be linear within the context of this micromodel, but more tests must be conducted to ascertain the
trend.
28
The rate of advance of the steam front in the last 2 hours of the test was computed from the chip
images. The front for the steam case progressed 1.667 mm in the last 114 minutes of the run,
corresponding to a rate of 2.104 cm/day. For the 200ppm additive test, the front advanced 6.553 mm in
the last 107 minutes and this corresponds to 8.819 cm/day. The 2000 ppm additive test exhibits a
15.2332 mm advance in the last 124 min, corresponding to 17.69 cm/day. The ‘front’ was measured all
the way to the intact bitumen region and encompasses the drainage layer of residual bitumen.
3.2.2 Physical Morphology of Steam-Bitumen Interface
One of the primary objectives of this study is to analyze the morphology of bitumen drainage and steam
front advance at the pore scale. This section discusses the main findings pertaining to bitumen mobility
at the interface.
Figure 13 shows a composite image of the interface amalgamated from the pore-scale video. The
Dinolite USB camera was used to monitor the steam-bitumen interface The USB camera is placed as
close as possible to the micromodel in order to capture a maximum of detail. To view a larger area, as is
the case in figure 13, it is necessary to sweep the camera over the area and then create a composite.
Due to the variable nature of the interface, both over the micromodel height and with respect to time,
any comparison in the interface morphology between different tests must occur at a fixed height and
run time. Figure 13 compares the drainage layer between the pure steam, 200 ppm and 2000 ppm
additive tests at approximately 100min of runtime at a height of approximately half of the micromodel.
The camera was maintained at an equal distance from the chip in all tests, therefore the scale shown at
the top applies to all image within figure 13.
29
Figure 13 – Steam chamber interface for steam (0 ppm), 200ppm, 2000ppm additive
(A – In-situ bitumen, B – Leading Edge, C – Lagging Edge, D – Steam Chamber)
30
All of the runs conducted show a relatively distinct 4 step model of steam front morphology:
o (A) In-Situ Bitumen: The bitumen adjacent to the interface is heated due to the latent
heat of condensing steam. There is temperature decay within the in-situ bitumen region
(and a corresponding viscosity increase away from the interface).
o (B) Leading Edge: Steam from the chamber reaches the reservoir bitumen and
condenses into water, forming a flowing ‘stream’ at the leading edge. Steam in vapor
form is not observed directly adjacent to the bitumen except at the very top of the
model. The flow of bitumen within the leading edge is limited at the top of the chamber
but a greater flow is observed with decreasing height. The condensate flows past the
heated bitumen at the leading edge and snaps off droplets from the bitumen, resulting
in a multitude of small fast-moving bitumen droplets. The leading edge seems to act as a
narrow ‘highway’ for rapid bitumen recovery. Water fingers originating from the leading
edge develop into the in-situ bitumen region.
o (C) Lagging Edge: The continuous losses of small bitumen droplets from the leading
edge tend to accumulate into larger droplets and eventually form a lagging edge of slow
draining bitumen behind the leading edge, over time. Bitumen droplets are forced out
of the high flow condensate stream due drainage through the porous media. The lagging
edge drains more slowly since it does not experience as much shear forces from the
condensate flow. The lagging edge boundary adjacent to the steam chamber is subject
to regular invasion by small steam bubbles, which subsequently become encapsulated in
bitumen and do not progress further. The lagging edge itself can be separated into a
rear section (in front of the steam chamber) where significant steam invasion is
observed and a front section (behind leading edge) with higher bitumen saturation and
no steam pockets.
o (D) Steam Chamber: The majority of the clear volume in the pores is occupied by steam
vapors. The steam chamber interfaces with the lagging edge and vapor steam droplets
sometimes enter into the lagging edge and become encapsulated with bitumen.
31
The lagging edge seems to be considerably wider in the pure steam injection case compared to the
additive runs; this can be attributed to the absence of alkaline additive in the condensed water since
there would be no additive-induced reduction in interfacial tension. This may be due to the significantly
greater bitumen recovery for 2000 ppm, since the lagging edge is created from the accumulation of
draining bitumen falling out of the condensate stream, it follows that the greater bitumen recovery from
the 2000 ppm run gives rise to a larger lagging edge when compared to the 200 ppm test, despite the
IFT-reducing effect of the additive.
Figure 14 – Bitumen recovery through droplet generation at the interface (Time-lapse: 2 sec)
The flow of condensed water at the leading edge of the steam chamber appears to exert shear forces on
the adjacent bitumen. Figure 14 shows droplets are created when the falling water forces the bitumen
against the posts until snap-off occurs and a bitumen drop is generated. The size and frequency of the
bitumen droplets is dependent on the bitumen viscosity, steam temperature and pressure in addition to
the morphology of the pore network. The droplet generation occurs at short time-scales (time-lapse of 2
seconds for figure 14). The relevance to field-scale SAGD projects is that the rate of bitumen production
from an area may be linked to the degree of condensed water flow within that area.
32
Figure 15 – Finger Displacement of bitumen (Time-lapse: 1 min)
A common mechanism of bitumen displacement at the interface is through the appearance and
expansion of water fingers ahead of the interface, demonstrated in Figure 15. Condensed water that
flows down along the leading edge of the interface may be funneled into fingers which then steadily
expand due to the high pressure in the steam chamber and due to continued water condensation and
flow at the interface. The finger displacement morphology is relatively narrow and directional and the
fingers always develop in the direction of gravity (downwards). The finger displacement occurs in the
mid-section of the interface and not as much at the top of the model since there is little condensed
water flow at the top.
Figure 16 – Non-directional Gravity Drainage at the top of steam chamber (Time-lapse: 5min)
Figure 16 shows the typical bitumen displacement at the very top of the steam chamber. The area
highlighted red shows that the heated bitumen seems to drain under the effect of gravity with no role
from the flow of condensed water (as opposed to recovery in the middle section of the interface where
33
condensed water has a greater impact). The gravity drainage at the top proceeds steadily in all
directions and differs from the finger displacement mechanism of the previous figure because there is
much less directionality.
Figure 17 – Emulsion generation under limited bitumen drainage rate at outlet port
Figure 17 shows the generation of emulsions at the outlet of the chip. This phenomenon was observed
during the steam run at the time of initial steam injection. The rate of bitumen drainage is limited by the
physical size of the outlet port (1mm diameter circle) as well as the high viscosity of the bitumen. The
water is squeezed through the bitumen and breaks up into numerous small droplets. The significance of
this finding is that the character of the emulsions produced from the SAGD process is not solely
determined within the reservoir. The pressure, temperature and geometry that the SAGD produced fluid
are exposed to during its movement to the surface play an important role in the final properties of the
emulsion.
Figure 18A documents the tendency for steam bubbles to enter into the lagging edge and become
encapsulated in bitumen. The figure shows still frames from pore-scale video taken at the steam
chamber and lagging edge interface (between layers C and D from figure 13). This kind of steam invasion
into the lagging edge was a common and frequent occurrence and two separate instances of steam
invasion and encapsulation are shown at the top and bottom frames.
As Figure 19A shows, trapped steam bubbles that are completely surrounded by bitumen shrink due to
heat loss over time leading to water condensation A specific instance of trapped steam condensation is
shown over a time lapse of 2 minutes. The formation and subsequent condensation of a second steam
bubble is also highlighted in yellow.
34
Within moments of the full condensation of the steam bubble in Figure 19, the bitumen droplet
(highlighted in red) was then able to drain downwards, this shows that steam invasion and
encapsulation by bitumen at the rear boundary of the lagging edge is detrimental to bitumen drainage
because it ‘inflates’ the apparent size of the bitumen droplets. Moreover, the dynamics of gravity
drainage for a bitumen drop that contains within it a large bubble of steam will differ markedly from the
drainage of bitumen-only droplets. It is expected that the steam buoyancy would act in opposition to
the force of gravity and slow down the drainage of steam-invaded bitumen.
Since isolated steam will condense into water due to heat loss over time, the persistent presence of
steam bubbles at the boundary between the steam chamber and lagging edge would require consistent
invasion of steam in order to balance the steady loss. As mentioned in the description of figure 18, it
was indeed observed that steam invasion into the lagging edge was a common and frequent occurrence.
Therefore, one of the important findings of this work is that the development of a lagging edge at the
interface, with a high level of residual bitumen, acts as a physical barrier to the advance of steam.
Figure 18A – Steam vapor bubble invasion & trapping into lagging edge,
(two distinct examples top & bottom row)
35
Figure18B - Maximum extent of steam invasion into lagging edge (steam test) amounts to ~3mm
The maximum extent of the steam vapor invasion into the rear boundary of the lagging edge is
highlighted in figure 18B. The steam injection test is shown since the large lagging edge allows clear
observation of this phenomenon. The degree of steam penetration into the lagging edge is dependent
on the rate of steam invasion and the thickness of the lagging edge.
Figure 19A – Trapped Steam bubble condensation (Time-lapse: 2min)
36
Figure19B - Size of condensing trapped steam droplet vs. Time
Figure 19B shows a plot of the rate of condensation of the steam bubble as observed in the earlier figure
19A. The condensation rate of multiple steam bubbles must be plotted against the distance of the
bubble from the steam interface and the thickness of the encapsulating bitumen in order to understand
the dynamics of steam condensation in the lagging edge.
3.2.3 Thermal Morphology of Steam-Bitumen Interface
The development of the thermal profile was captured using the RAZ IR NANO infrared camera. The
camera is able to measure temperatures up to 250°C. The camera was placed as close as possible to the
glass chip while still ensuring the visibility of the whole chip. The resolution of the thermal sensor is
160x120 pixels with a measured temperature associated to each pixel. In our experiments, one pixel was
found to correspond with 1.075 mm of distance across the chip.
Figure 20 shows a time-lapse of the steam chamber development as observed by the infrared camera
over the 160 minute duration of the tests. As indicated on the scale, the deep red areas are at 110°C or
more and the black areas have a temperature lower than 80°C. An important observation concerning
the thermal profile is that the thermal chamber always seems to have a relatively sharp and distinct
forward edge throughout the test (as opposed to the emergence of a thick drainage layer in the physical
chamber).
37
Figure 20 - Time-lapse thermal images over the 160 minute duration of the tests
Figure 21 shows a composite image with the thermal profile and physical profile of the steam chamber
superimposed. The thermal profile shows temperatures between 130°C (in red) and 75°C (in faded blue)
with a bright yellow band at temperatures from 100°C to 115°C.
In the highlighted area of detail of 4 cm in length, the temperature was plotted along the distance of the
central horizontal axis in order to allow comparison between the temperature profile and corresponding
changes in the physical chamber morphology. The temperature data point closest to 100°C is shown in
red.
The 10min and 85 min temperature profiles should not be directly compared to one another within
Figure 21 since the scales on the temperature axis are not matched. The 10 min and 85 min temperature
profiles can be compared with each other in Figure 22.
38
Figure 21 – Thermal & Physical Steam Chamber Comparison:
lagging edge acts as barrier to steam chamber expansion (temperature axes are not scale-matched)
39
Figure 22 – Temperature profile at the interface
Figure 21 shows that, at 10 minutes of runtime, the bitumen drainage interface coincides with the
condensation of the steam at the 98.5°C. During the early stages of the run, the physical interface
consists of a sharp front and the steam directly contacts the bitumen. In contrast, the 100.3°C point at
85 minutes corresponds with the rear boundary of the lagging edge.
Figure 22 shows that at 10 minutes, the temperature beings to decay closer to the interface but shows a
more significant drop across the interface; whereas at 85 minutes, the temperature drop begins slightly
further from the interface and the decay is more gradual. The early chamber at 10 minutes of runtime
appears to be closer to the established understanding of SAGD and to Butler’s theory (discussed in 2.2.3)
which assumes a sharp interface.
Therefore, two distinct thermal regimes are observed in the test. Upon the injection of the steam, a
chamber develops with ‘direct contact’ between the steam and the bitumen giving rise to a high rate of
recovery. However, with the continued drainage of bitumen and the development of a lagging edge, a
transition soon occurs to a second thermal regime that is defined/characterized by obstruction to steam
advance due to residual bitumen, a ‘thermal gap’ between the edge of the physical and thermal steam
chamber, leading to a tapering of the rate of bitumen recovery. It is important to note that the bitumen
recovery does not decay to zero in the late stages of the test. Recovery continues, albeit at a much
slower pace. The rate of interface advancement calculated over the last two hours of the test is equal to
40
2.104 cm/day (steam), 8.819 cm/day (200 ppm) and 17.69 cm/day (2000 ppm) and this corresponds
broadly to the rate of interface advance in field-scale SAGD projects of 2 to 27 cm/day [52].
For the 85 min profile, it is observed that there is a greater distance between the forward boundary of
the leading edge and the thermal chamber at the top of the model than at the bottom. As mentioned in
section 3.2.1, the large apparent thickness of the drainage region at the top of the micromodel is
explained by considering that a specific volume of bitumen which drains from the top will fill the lower
section of the model on its way to the outlet and thus any bitumen drained from the very top of the
model is not itself replenished by incoming flow from above.
Figure 23 further reinforces the argument that residual bitumen from the lagging edge prevents steam
chamber development as it shows minimal advance of the rear boundary of the lagging edge while the
leading edge has advanced to a greater extent.
Figure 23 – Comparison of the rate of advance of leading & lagging edge of steam chamber
41
3.2.4 Pore-Scale Fluorescence Imaging of Steam-Bitumen Interface
After the completion of each run, the micromodel was immediately taken to the fluorescence
microscope for imaging. The fluorescence microscopy is useful in undertanding micro-scale behaviors
such as wetting since it provide a higher magnification observation of the system. It should be noted
that stopping the steam flow to the model resulted in depressurization and condensation of the steam
chamber. The interface was disturbed as a result and the imaged sample may differ significantly from its
runtime conditions of high temperature and pressure.
Figure 24 shows a composite image of fluorescent (top) and brightfield (bottom) microscope images for
each run. The large images were stichted from smaller individual images. The bitumen appears green
under the fluorescent light and appears black in the brightfield images. Despite the disruption of the
interface (due to drawdown of the pressure), the four stages of interface morphology are still broadly
distinguishable (as indentified by the blue-box labels below each image). The main observations from
this figure is that the emulsions at the interface are mostly found between the steam chamber and
lagging edge boundary. Also, the leading edge has a higher bitumen saturation in the steam case
compared to the additives and in the 200 ppm case compared to the 2000ppm.
Figure 25 shows the morphology at the interface within the reservoir bitumen region just ahead of the
leading edge. While the reservoir bitumen region appears to be a continuous mass from macro-scale
optical observation, a magnified pore-scale view reveals that it comprises a collection of small bitumen
droplets (especially for the for the 2000 ppm case). Droplet accumulation at the interface is more
common at lower heights within the model and this indicates that the bitumen droplets are most likely
to have been generated upstream (as shown in figure 14) and became trapped within the porous
medium on their way to the outlet. The fact that adjacent drops of bitumen did not agglomerate shows
the effectiveness of the additive at reducing IFT. The size of these droplets steadily increases (due to
increased tendency towards agglomeration) with lower additive concentration.
Figure 26 also shows are in the reservoir bitumen ahead of the steam chamber for the steam case. Very
small droplets of water (highlighted in yellow in the figure) are seen to invade a few millimeters into the
bitumen. These droplets suggest that the bitumen in that region is heated and sufficiently mobile to
slowly drain under gravity as well as in response to the pressure gradient in the chamber within the
model (a similar pressure gradient should also exist in the field-scale SAGD chamber).
42
Figure 24 – Fluorescent (bitumen = green) & Brightfield (bitumen = black) post-run microscope images
Figure 25 – Small water droplets are observed in the bitumen ahead of the leading edge
43
Figure 26 – Bitumen droplets drainage ahead of the interface
3.2.5 Key Findings on the Nature of Pore-Scale Steam Front Dynamics
The experimental results of the steam additive tests are described in detail in the previous subsections
of 3.2. The key findings of this micromodel study on the dynamics of the SAGD chamber interface are
outlined below.
New insights physical & thermal pore-scale morphology of SAGD Interface:
The four-stage morphology at the steam chamber edge is identified and described. The
interface can be segmented into:
o (A) reservoir bitumen ahead of the front
o (B) leading edge
o (C) lagging edge of the condensed water front defined by low drainage rates and high
bitumen saturation
o (D) steam chamber behind the lagging edge is mostly filled with steam
The leading edge forms when steam at the interface condenses into water and is defined by
high drainage rates and low bitumen saturation.
44
o High flow rates at the leading edge are only apparent from 2 cm below the top of the
model onwards, since there is less volume of condensed water flow at the top of the
micromodel
o Bitumen displacement at the top of the leading edge proceeds through slow, non-
directional gravity drainage
o Bitumen displacement closer to the center of the leading edge is more heavily affected
by condensate flow. Two distinct mechanisms are observed at the boundary between
the leading edge and the reservoir bitumen:
Droplet generation (when condensed water pushed bitumen against the posts)
Water finger initiation and growth (when the draining condensate is funneled by
the porous media into a finger and leads to finger growth)
The lagging edge develops, over time, behind the leading edge and is defined by low drainage
rates and high bitumen saturation.
o The lagging edge forms as a result of bitumen droplets being forced out of the high flow
condensate stream due to the pore network
o Bitumen within this layer drains slowly due to the absence of the condensate flow
o Vapor bubbles from the expanding steam chamber invade into the back of the lagging
edge and become trapped due to encapsulation by bitumen.
Trapped steam bubbles tend to condense over time and the persistent
existence of trapped steam at the back of the lagging edge is due to continued
steam invasion
Trapped steam contributes towards the slow drainage due to steam buoyancy
There exists two distinct thermal regimes of steam chamber temperature during the test:
o Direct-contact regime: sharper temperature drop over interface
o Thermal gap regime: due to lagging edge hindrance/obstruction of steam flow
Effect of alkaline additive on recovery dynamics
The inclusion of alkaline additives into the steam improves bitumen recovery from 25.6%
(steam-only) to 33.2% (200 ppm aqueous solution) and 68.8% (2000 ppm aqueous solution).
45
Recovery is defined as percentage of total volume of the micromodel produced (with 100%
initial bitumen saturation)
Interface velocity over the last two hours of the tests is found to be equivalent to 2.104 cm/day
(steam), 8.819 cm/day (200 ppm) and 17.69 cm/day (2000 ppm) and this corresponds broadly to
the rate of interface advance in field-scale SAGD projects of 2 to 27 cm/day [52]
The specific effects of the alkaline additive are observed as follows:
o Lower interfacial tension (IFT) on bitumen surfaces due to the surfactant effect of the
additive is observable in terms of:
Increase oil-in-water emulsions at the reservoir bitumen interface
Bitumen saturation in the leading edge is reduced from the steam case to the
200 ppm case and further reduced with 2000 ppm additive. The width of the
leading edge also generally increase with additive content
200 ppm additive test a thinner drainage layer than the steam test
The reduction in the lagging edge thickness that stems from the additive-induced lower IFT is
crucial in helping steam chamber growth because the lagging edge was found to impede steam
advancement.
As mentioned in section 2.2.3 existing SAGD models make assumptions regarding the nature of the
steam chamber interface and the pore-scale observations from the micromodel tests can be used to
evaluate the validity these assumptions. The original derivation of the Butler equation for bitumen
production from SAGD made the assumption that the interface consists of a single boundary with
the steam chamber on one side and the in-situ bitumen on the other. The micromodel tests
revealed that the interface actually consists of two distinct layers with a lagging edge and a leading
edge. A new model can be developed based the new interface morphology that accounts for the
distinct physics of each layer, as shown in figure 27.
46
Figure 27 – Physical model allows evaluation of validity of interface assumptions
For instance, the new model can evaluate the behavior of the leading edge by considering:
Leading edge thickness (based on steam flow pattern, condensation rate, model height, steam
temperature & pressure, pressure drop from inlet to outlet)
Size and frequency of droplet generation (based on shear effect of condensate, pore network
geometry, viscosity ratio of heated bitumen & condensate, IFT)
Entrapment of draining bitumen droplets within the pore network (based on droplet generation
dynamics, pore network geometry)
Effect of convective heat transfer from drainage of heated bitumen
Effect of convective heat transfer from lateral flow of condensate (based on an adapted version
of the Lauwerier Equation, which is used to understand heat transfer from the flow of a heated
fluid laterally alongside a porous media)
47
The new model could account for the negative effect of trapped steam on bitumen drainage in the
lagging edge by considering:
Lagging edge thickness (based on bitumen droplet generation rate, pore geometry, rate of
accumulation of bitumen droplets behind leading edge, gravity drainage)
Frequency of steam bubble invasion (based on chamber steam flow velocity field)
Extent of steam invasion into lagging edge (based on invasion & condensation rate)
Effects of trapped steam vapor on bitumen gravity drainage (based on density differences, IFT)
3.3 Bitumen Displacement from Gasification of Reservoir Liquid
The start-up phase of the SAGD process involves injecting steam into the reservoir to establish initial
communication between the injection and production wells. A large amount of steam is injected into
the reservoir with no production during the start-up phase and there is an interest in reducing the time
and energy requirements for this step. Within the context of reducing SAGD start-up time, a test was
conducted to investigate the morphology of bitumen displacement resulting from the gasification of a
liquid pocket in a bitumen-filled micromodel. An additive was also added to the water in this case to
produce a 10wt% solution of the additive in water. The exact nature of the additive for this section
cannot be disclosed and it is not related to the alkaline steam additive employed in section 3.2.
Figure 28 shows the initial condition of the micromodel, which is 85% saturated with bitumen and the
remaining 15% of the volume being occupied by the aqueous additive solution. The liquid exists as a
single large pocket and is not dispersed in the porous media. The two ports adjacent to the liquid
chamber are closed to prevent the leakage of generated gas while the ports on the bitumen side are left
open in order to allow bitumen displacement. The model is heated on a hot plate to a temperature of
120°C and gasification of the liquid pocket is allowed to occur.
The overall result of the experiment was that the gas was continually generated from the liquid pocket
for a span of 45 minutes and was able to displace bitumen and reduce the bitumen saturation of the
pore volume from 85% to 50%, as shown in Figure 28.
48
Figure 28 – Gasification of liquid volume over a span of 45 minutes leads to 35% bitumen displacement
Figure 29 – Pore-scale view of gas expansion (left) and liquid finger region (right)
Gasification of the aqueous solution leads to a pressure gradient that drives flow towards the open ports
on the opposite side. During the initial stages of the test, evenly spaced fingers of gas emerged from the
interface with the bitumen. However, within 12 minutes from the start of the test, the expansion of the
gas fingers stopped and instead a multitude of liquid fingers emerged and expanded towards the outlet
ports. Some of these liquid fingers initiated from the edge of the gas fingers.
49
As is shown in Figure 29, gas fingers are more effective at displacing the bitumen than the liquid fingers.
The amount of residual oil within the gas-cleared area is much lower than the residual oil left being in
the liquid-cleared area. It is also interesting to note that liquid droplets exist at the interface between
the gas and the bitumen, indicating there is no direct contact between the bitumen and the gas.
Figure 30 shows the pore-scale fluorescence images obtained after the test. The high recovery area
stemming from the expansion of the gas fingers is shown at the top, a mixed-wet condition is observed
and a large distribution of bitumen emulsion sizes is shown. The lower recovery area from the liquid
fingers is at the bottom of the figure, an oil-wet condition is observed along with a continuous oil phase.
The main finding of this experiment is that there is an initial period of rapid gas expansion at the
interface that later gives way to liquid finger expansion. Gas expansion is desirable since the gas-cleared
regions have low residual oil while the liquid-cleared regions leave behind a larger amount of residual
bitumen. Further tests are needed with different concentrations to determine the effect of the additive
on the gas generation and bitumen displacement dynamics.
Figure 30 – Fluorescence microscope images of bitumen displacement in gas & liquid region
50
3.4 Emulsion Analysis & Semi-Automated Video Segmentation
Automated image analysis is a useful tool that can be used to extract various types of numerical data
from different sets of images. Theories previously examined in a qualitative manner through the use of
images can now be studied quantitatively using charts, graphs and tables. A large amount of the data
produced from the pore-scale experimental apparatus is in the form of images and video. When
extracting information from images, it is desirable to minimize human error and bias. For instance, the
manual counting and measurement of an emulsion may yield different data based on the definition or
biases of the individual who counts the emulsion; alternatively, the same individual manually counting
an emulsions multiple times may find slightly different distributions each time due to human error. In
order to ensure the accuracy and reliability of the data, an emulsion analysis procedure was developed.
A brief review of these efforts will be presented in this section.
A semi-automated method was developed to obtain the particle size distribution from an emulsion
image. Our method is tailored towards emulsions with circular droplets of two different
substances/colors but can also be applied to multi-component emulsions with more than two different
types of particles provided they each have a unique coloration that allows differentiation.
The overall process of obtaining the particle size distribution involves 4 steps:
(I) Improve image quality & contrast to allow easier analysis
(II) Threshold image to separate particles/features from the background and to create a
particle mask
(III) Separate the particle mask into 2 categories for the different components of the
emulsion (a separate mask for the light and dark particles)
(IV) Process the image masks to generate a particle size distribution bar chart for the light
and dark particles
While image analysis is simple in principle, numerous image segmentation errors and artifacts must be
corrected in practice to obtain reliable data. The tool that is used to conduct image analysis in this
procedure is the analysis software Fiji (a variant of ImageJ) which is available for download for a variety
of operating environments.
51
Figure 31 – (Top) Original image of emulsion (Bottom) Semi-automated analysis identifies emulsions
52
Figure 31 shows test fluid obtained from a SAGD facility, the exact nature of the fluid cannot be
disclosed due to confidentiality. The original image at the top displays a multi-component emulsion with
large yellow droplets and smaller brown droplets. An emulsion of this complexity would be difficult to
manually characterize to a satisfactory level of accuracy. The semi-automated emulsion analysis
procedure was applied to obtain the annotated image at the bottom of the figure, with a separate color
scheme for the yellow and brown emulsions. Figure 32 shows the plot of the emulsion size distribution
extracted from the image. A full explanation of the procedure is provided in Appendix B.
Figure 32 – Semi-automated analysis is used to accurately and quickly obtain particle size distribution.
The light/dark blue distribution is the particle sizes for the corresponding emulsions from figure 31
A semi-automated analysis method was also developed for segmentation of the interface from pore
scale videos. Figure 33 shows a still image from a video being processed with this method. The basic
process begins with extracting an image sequence from the video file and combining the images into a
single TIFF image stack.
Each image in the stack is then converted to a binary black and white version with care being taken that
the binarized image is representative of the bitumen location in the original. The binary images are then
collapsed into a single gray-scale image using the Z-project function which assigns a gray-scale value
53
from 0 to 255 to each pixel based the average for that pixel throughout the stacked images. The
resulting gray-scale image is then threshold to identify the stable and mobile regions of bitumen.
Figure 33 – Automated image segmentation of the SAGD interface
54
4.0 Conclusion & Future Directions
The high temperature-pressure pore-scale microfluidics apparatus developed in this work was used to
investigate the pore-scale physical and thermal morphology of the steam chamber advance in the SAGD
process. It was identified that four distinct regions exist at the interface: in-situ bitumen, leading edge of
condensate flow with low bitumen saturations, a lagging edge of high bitumen saturation and low
drainage rate and the steam chamber. Novel dynamics concerning the advancement of the steam front
were documented, including the tendency of the lagging edge to act as a barrier to steam advance. The
significant impact of alkaline additive on recovery was found and the importance of the additive-induced
decrease in IFT was observed in terms of reducing the thickness of the lagging edge.
A wide range of future directions are possible with respect to the micromodel. One path is to develop a
detailed simulation model of the micromodel and to conduct experimental tests in tandem with the
simulation to inform the theoretical framework of the SAGD process. For instance, separate models
could be created, each based on a different set of assumptions and the experimental data from the set-
up would help us evaluate the validity of the assumptions. Moreover, numerous questions, such as the
role of shear forces in the leading edge, can best be answered by comparing simulation and
experimental data.
Alternatively, another direction to pursue would be to introduce heterogeneity in the model. The oil
sands reservoirs are known to be highly heterogeneous and the effect of shale barriers or fractured
reservoirs on bitumen recovery in the context of SAGD are yet to be understood. Another approach
would be to inject other substances, such as solvents for VAPEX or solvent-assisted SAGD. Injecting light
hydrocarbons into the micromodel can be achieved with minimal changes in the experimental set-up.
55
Appendix A
PEEK Manifold CAD Drawings
56
The drawings for the top clamp (above) and base clamp (below) of the PEEK manifold are shown. The
glass chip fits between the clamps with the inlet/outlet holes aligned with the ports. All dimensions are
in millimeters.
57
Appendix B
Glass Fabrication Standard Operating Procedure
58
The following standard operating procedure was developed for glass etching:
Book all required equipment on the cleanroom system, enable upon entry into cleanroom
Check photomask under microscope (sharp focus = side touching glass).
The side on which the pattern is printed must be touching the glass to ensure proper exposure.
Exposure
Conduct 50s cycles 3 times with the empty exposure box (to 'warm up' the light)
Place glass into exposure box with photoresist facing up, align photomask
Close cover, create vacuum to ensure optimum photomask contact
Close box, set time (45s typical, varies with different photoresist), expose
Mix developer
Find vessel (that can accommodate the chip) and clean with DI water
Mix: 1 AZ400K : 6 DI water (20ml : 120ml)
Stir well
Place exposed glass into developer
Agitate for 1min30s
Rinse in DI water until developer removed
Examine under microscope
Repeat in 30s increments until proper developing
Exposure Box
59
Wash glass in DI water, air dry (to remove developer)
Examine under microscope to ensure correct pattern exposure & development
Hard Bake
Turn on hot plate and set to 130°C
Place developed glass on hot plate at 130°C for 30min, this will harden photoresist
After the hard bake step, all cleanroom steps take place in etching room (white-lit)
Prepare Dechromer
Work in the wetbench that you booked (left or right side)
Find glass vessel and clean with DI water
Fill with dechromer enough to submerge chip (approx. 4mm high)
Optical Microscope
60
Place glass into dechromer (chrome exposed due to UV exposure is removed)
Wash with DI water until dechromer removed
Prepare HF solution
(Note: HF is a highly dangerous substance; you MUST be trained by the lab
manager to safely handle HF or Piranha prior to glass etching)
Find and clean thick-walled polymer vessel with DI water (No glass vessel)
Mix: 1 HF : 3 HNO3 : 5 DI water
Always Add Acid to Water *
(For 1400ml HF: 156ml HF, 467ml HNO3, 778ml H20)
Sufficient HF solution is needed to submerge a vertically standing chip completely
Clean with DI water the tools used to prepare HF
Etching:
A wafer-holder is typically used to handle glass slides (large ones in particular)
Place glass in HF vessel for glass etching, time in HF determines etch depth
BoroFloat33 glass etches at approximately 1μm/min
Remove glass from HF and dip gently into water every 15 minutes
Wetbench in Cleanroom
61
When etching period is complete, rinse with DI water, inspect under microscope
Use Teflon utensils, do not use metal
Etching Depth Measurement:
Optical profilometer is in CMS (MC405), equipment must be booked prior to use
Use optical profilometer to obtain the actual etching depth for each chip
Drill holes in cover glass
Wrap glass to be drilled in Aluminum, take to MC413
Drill holes in glass (align holes correctly using hole alignment mask)
While drilling, use DI water on the glass as coolant to prevent drill-bit damage
Wipe the drill to prevent rusting due to water
Return to cleanroom with drilled glass
If needed, use dicer in Pratt cleanroom to cut glass to required dimensions
Prepare Pirhana solution
(Note: Piranha is a highly dangerous substance; you MUST be trained by the
lab manager to safely handle HF or Piranha prior to glass etching)
Find glass vessel and wash with DI water
Mix: 1 H2O2 : 3 H2SO4 (or 1:2)
Always add Peroxide to Sulfuric Acid
(For 1400ml Piranha: 467ml H2O2, 933ml H2SO4)
Wait 1 min for reaction to begin
Clean with DI water tools used to prepare Pirhana
Drill Press
62
Final Dechroming
Place etched glass in Piranha (photoresist will be removed) until glass is metallic
Rinse in DI water
Place etched glass into dechromer until complete removal of remaining chrome
Rinse in DI water
Place etched glass into Piranha (removes all dirt and residual dechromer)
Rinse in DI water
Once the glass has been cleaned in Piranha, caution must be exercised to prevent any deposition of
dirt/dust which may cause issues with glass thermal bonding. From now on:
Wear face mask, do not breathe on the glass chip
Do not set down the glass chip on the table or on a wipe. Only place it on Aluminum foil.
Dispose of Dechromer, HF and Piranha (in the appropriate waste bottles)
Piranha must be de-gassed overnight before disposal
Sonicate to clean glass (on the day of bonding)
Set up sonication station (Get 2 beakers that can fit glass, place in sonicator)
Fill beakers and sonicator with DI water
Sonicate glass for 10 min, replace water in beakers
Rinse glass under running water for 1 minute
Repeat three times
Hold cover and base glass under running water for 15 minutes
Water-bond or Dry-bond glass
Ensure the correct orientation of glass pieces (etched side inwards)
Dry glass completely then bring both sides together (Dry bond, recommended)
Bring both sides together under running water (Water bond)
Inspect glass under microscope:
If any particulate contaminants are observed: Carefully separate the two pieces of glass and repeat the water/dry bonding step
63
If there are no particulate contaminants between the glass sides: Wrap chip in aluminum foil, proceed to next step for thermal fusion bonding, Be sure to turn off microscope lamp to prevent burn-out
Glass thermal fusion bonding
Carefully transport glass to the furnace lab
Wipe clean the inside of the furnace from dust, use IPA and DI water
Wipe clean all weights to be used & alumina plates with IPA and DI water
Place the alumina, glass and weights within the furnace (as shown in the figure)
Set furnace stages of 400°C/3hr, 645°C/6hr, 50°C/5minutes at ramp rates 2°C/hr.
Bond overnight, check glass on the next day to ensure proper bonding
If incomplete bonding occurred, bond again at 400°C/2hr, 645°C/2hr, 50°C/5minutes
*SAFETY NOTE: Always Add Acid to Water
If water was added to acid in a beaker, then the initial point where the water first enters the acid would
form a highly concentrated mixture of acid and large amounts of heat may be produced, leading to
hazard of splashing or explosion. If acid is added to the water in a beaker then the initial point where the
acid first enters the water would form a very dilute mixture of acid and the risk is minimized. Pour the
acid slowly into the water.
SAFETY NOTE: Training is required before you can work with hydrofluoric acid (HF) or piranha. Please
consult the lab manager.
500g Weight
3 x 100g Weights Alumina
SlideGlass ChipAlumina Slide
Weight stack arrangement for Furnace bonding
64
TIMING FOR GLASS CHIP FABRICATION
The glass chips are usually fabricated in batches because making single chips is not practical. The
batches are usually for 3 chips at a time (the hot plate in the pratt cleanrrom accommodates 3 chips for
10x10cm chips). Often, 2 people work together to fabricate glass chips (it is recommended to work in
pairs when dealing with HF, piranha).
Below are the typical times and costs associated with glass chip fabrication.
Day 1:
Steps: Exposure, developing, hard baking, dechroming, hole-drilling on cover glass
Day 2:
Steps: Prepare HF solution, etch glass, optical profilometer measurement, dispose HF
Day 3:
Steps: Prepare piranha, piranha cleaning of glass, sonication, dry-bonding, furnace bonding cycles
Day 4,5,6:
Steps: Typically 3-4 furnace bonding cycles are required for chip bonding. The bonding takes 12+ hours
and the cycle runs overnight. The bonding time (at 650C) or the weights can be increased to minimize
bonding cycles but the risk of channel collapse in the glass is also increase in this case. Personnel time
requirements are minimal during the bonding phase (takes 30 minutes to set-up each furnace cycle).
Furnace bonding is typically done a single chip at a time, but multiple chips can be stacked to reduce the
time requirements.
65
COSTS FOR GLASS CHIP FABRICATION
This section assumes 3 chips are made in one batch. Equipment costs are listed and multiplied by typical
time requirements. Personnel costs are not directly listed but a template is provided. This section
assumes fabrication proceeds without significant mishap or delays.
Material costs:
Glass slides (Pre-coated, pre-cut. 2.25mm thick, BF33) $1200/20 slides $60
Photomask (one-time cost, printed by CAD/Art Services) $150
Equipment costs:
(Rates shown are for U of T users for the TNFC Pratt cleanroom except where indicated)
UV exposure box 27 $/hr 3hr (Day 1) $81
Hot plate 27 $/hr 2hr (Day 1) $54
Wetbench 42 $/hr 5.6.6hr (Day 1/2/3) $714
Optical Profilometer 30 $/hr (CMS) 2hr (Day 2) $60
Drill press free (lab equipment)
Furnace free (lab equipment)
Personnel costs:
2 x $/hr x 8 hr/day x 3 days = ___
Total costs = $1119 + Personnel costs
66
Appendix C
Semi-Automated Emulsion Analysis Standard Operating Procedure
67
Introduction & Overall Method
Automated image analysis is a useful tool that can be used to extract various types numerical data from
different sets of images. Theories previously examined in a qualitative manner through the use of
images can now be studied quantitatively using charts, graphs and tables. One metric of particular
interest is particle size distribution (PSD) which is useful in a variety of circumstances, including the
analysis of petroleum emulsion. Particle size distribution is difficult to obtain manually, especially in the
presence of a large number of particles, emulsion components and sizes.
An automated method was developed to obtain the particle size distribution from a given image. This
method is tailored towards emulsions with circular droplets of two different substances/colors but can
also be applied to multi-component emulsions with more than two different types of particles provided
they each have a unique coloration that allows differentiation. The image analyzed in this procedure is
shown below, the emulsion consists of 3 components (yellow background, yellow droplets, brown
droplets).
68
The overall process of obtaining the particle size distribution involves 4 steps:
(I) Improve image quality & contrast to allow easier analysis
(II) Threshold image to separate particles/features from the background and to
create a particle mask
(III) Separate the particle mask into 2 categories for the different components of
the emulsion (a separate mask for the light and dark particles)
(IV) Process the image masks to generate a PSD bar chart for the light and
dark particles
While image analysis is simple in principle, numerous image segmentation errors and artifacts must be
corrected in practice to obtain reliable data. The tool that is used to conduct image analysis in this
procedure is the analysis software Fiji (a variant of ImageJ) which is available for download for a variety
of operating environments at: http://fiji.sc/Downloads.
Image Analysis Procedure
The procedure for obtaining the particle size distribution is described below. For clarity and readability,
each overall task has been listed as a separate section with its own subsections and numbering of steps.
1.0 Obtain a mask of all particles
(1) Download and install the Fiji software. Open the program.
(2) Open the image that you will analyze into the program. You can either go to File>Open or click and
drag the image into the Fiji task bar.
69
1.1 Enhance Local Contrast to allow better contrast between features and background:
(1) Go to Process> Enhance Local Contrast (CLAHE); while optimal parameter may vary for different
images, the parameters shown here work well for this image.
(2) Another way to improve contrast of the image is to go to Process>Enhance Contrast
70
1.2 Threshold enhanced image to get the precursor to the particle mask:
Go to Image>Adjust>Color Threshold, a menu will pop up allowing manual control. Depending on the
image, it may be impossible to obtain all particles with a single threshold setting because settings that
capture the small droplets may leave out the larger ones and vice-versa. The solution is to obtain
multiple masks at various threshold levels then combine all the images. In this case, 10 distinct masks
were obtained using both of the enhanced images from 1.1. Each image is saved in TIFF format (as other
formats may lead to data/quality loss). Two examples are shown below:
71
1.3 Analyze particles to obtain a particle masks from each of the precursor images:
Go to Analyze>Analyze Particles, use the settings provided below for all precursor images. The size is
expressed in pixels but can be changed to microns if the scale was previously set. The upper limit on the
particle size will eliminate the large black background spots from the precursor image but will keep the
particles in place. Some of the images obtained from this step are shown below:
72
1.4 Add together all previously generated masks to obtain the mask for all particles
The numerous masks obtained in the previous step must be combined into a single mask. There are
various means to sum all of the images. Each of the pixels in the image have a value associated with it,
when the image is 8-bit black and white, the values range from 0 to 255, where typically white
corresponds to a pixel value of 255 and black to 0 (although this can be inversed through Edit>Invert or
Image>Color>Edit LUT).
One way to sum two images is to:
(1) Convert images to 8-bit grayscale (white = 255, black = 0) (Image>Type 8-bit);
manipulate polarity so that we get the particles in white and black background
(2) Add one image to the other (Process>Image Calculator) such that a pixel that is part
of the background (black with value =0) will not change anything in the combined image but a pixel
that is part of the particles (white with value = 255) will be added into the combined image
(3) Repeat the image addition with all of the masks previously obtained to get the mask of
all particles
The final step is to 'clean up' the mask by applying the de-speckle function 5 times consecutively
(Process>Noise>Despeckle) and analyzing particles to exclude those fragments smaller than 40 pixels in
terms of area. It should be kept in mind that the specific numbers that work best may vary for other
images.
The final particle mask acquired for this image is shown below:
73
1.5 Overlay 'all particles mask' onto the original image to verify if mask is adequate
Before proceeding further, the mask obtained in the previous section must be placed as an overlay on
top of the original image to ensure that it is adequate. If it is found that too many spots are missing from
the mask, then the steps of section 1.0 would be repeated to add more particles to the mask.
The overlay is applied by opening the original image and the mask (ensure the particles are white (255)
and background is black (0) in the mask), selecting the original image and going to Image>Overlay>Add
Image (select to add the mask, make sure to check 'zero is transparent'). The overlay image shown
below demonstrates that the mask produced in this example is adequate for particle size distribution
analysis.
74
2.0 Separate Light vs. Dark Particles
2.1 Create separate masks for light and dark particles
Enhance contrast of the original image (Process>Enhance Contrast) by 80% (not equalized) to obtain the
image below:
Use the image calculator (Process>Image Calculator) to calculate:
[Enhanced(80%, not equalized)] XOR [All Particles mask (ensure particles are white)]
75
In the above image, we can see that the coloring of the brown particles is light blue while the coloring of
the yellow particles is dark blue. This occurs because the XOR operation between image 1 and 2 is
applied to every pixel. The 80% contrasted image has pixels of red color in all the spots where brown
droplets are present and yellow pixels in all the sports where yellow droplets are present.
2.2 Artifact removal from the light particle mask
In the previous image, it may be noticed that a frequent pattern of error occurs of a ring of light blue
color surrounding the large dark blue particles, examples of this type of artifact are shown below.
The light blue color should only appear where the brown droplets occur and the presence of these ring
artifacts would give rise to a significant error during the size distribution analysis. These rings occur due
to the fact that in the original image, brown rings are present around the large yellow droplets.
In order to correct this artifact, it is necessary to first extract the light particles mask and dark particles
mask from the XOR image:
(1) Go to Image>Adjust>Color Threshold, adjust the threshold to obtain only the light blue and dark
blue particles respectively. It can be seen that the ring artifact is present in the light blue mask that
represents the yellow droplets from the original image.
(2) Dilate the mask for the yellow droplets three time consecutively. (Process>Binary>Dilate)
(3) Convert the masks for both yellow and brown droplets to 8-bit black and white.
(4) Use the image calculator (Process>Image Calculator) to subtract as follows:
[Original brown particle mask] - [Dilated yellow particle mask]
(5) De-speckle the resulting image 3 times to obtain the final mask for the brown particles
(Process>Noise>Despeckle).
76
Following the above steps, the separate particle masks are obtained for the yellow and brown droplets
as shown below (may need Image>Color>Edit LUT to define colors).
.
77
2.3 Overlay light & dark particle masks onto original to verify if mask is adequate
The next step is to overlay the light and dark particles masks onto the original image in order to verify
that the image segmentation is adequate. Use Image>Overlay>Add Image. The final result is shown
below, it is observed that the masks are adequate.
2.4 Create data tables to provide the numbers for the particle size distribution charts
(1) Go to Analyze>Set Measurements, check area &
centroid (x,y position) & perimeter.
(2) Open the mask image for the light particles, go to
Analyze>Analyze Particles, enter the desired values for
particle sizes & circularity, choose to show outlines,
labeled outlines as well as a table of results will appear.
78
3.0 Process Final Data File
(1) Transfer the data from the Fiji measurements window to an Excel file.
(2) If an image scale was not previously set in Fiji, all values will be in units of pixels and it will be
required to convert the units to microns on excel.
(3) Use the measured area of the particle to calculate the diameter of the particle. The diameter is used
as the measure of 'particle size'.
(4) Prepare the histogram and charts from the data as shown below:
Particle Size Light Particle Frequency Dark Particle Frequency
0 ˃ x ≥ 1.5 12 35
1.5 ˃ x ≥ 3 28 82
3 ˃ x ≥ 4.5 47 314
4.5 ˃ x ≥ 6 35 205
6 ˃ x ≥ 7.5 22 130
7.5 ˃ x ≥ 9 16 72
9 ˃ x ≥ 10.5 12 52
10.5 ˃ x ≥ 12 11 24
12 ˃ x ≥ 13.5 9 40
13.5 ˃ x ≥ 15 4 20
15 ˃ x ≥ 16.5 7 16
16.5 ˃ x ≥ 18 7 10
18 ˃ x ≥ 19.5 12 16
19.5 ˃ x ≥ 21 5 12
21 ˃ x ≥ 22.5 2 6
22.5 ˃ x ≥ 24 5 4
24 ˃ x ≥ 25.5 1 3
25.5 ˃ x ≥ 27 8 1
27 ˃ x ≥ 28.5 2 1
28.5 ˃ x ≥ 30 1 1
30 ˃ x ≥ 31.5 1 0
31.5 ˃ x ≥ 33 2 2
33 ˃ x ≥ 34.5 0 0
34.5 ˃ x ≥ 36 0 0
36 ˃ x ≥ 37.5 0 0
37.5 ˃ x ≥ 39 1 0
39 ˃ x ≥ 40.5 0 0
40.5 ˃ x ≥ 42 1 0
42 ˃ x ≥ 43.5 1 0
43.5 ˃ x ≥ 45 0 0
More 0 1
79
80
Concluding Remarks
This procedure demonstrates a versatile method for the extraction of numerical data from images.
Explanation and guidance is provided to allow the plotting of the particle size distribution histogram
from the original image. While a three-phase emulsion (yellow background, yellow droplets, brown
droplets) was analyzed, it is possible to readily apply the method for more complex multi-component
emulsions.
Some of the plug-ins and functions built into Fiji are a result of academic research in image analysis and
proper referencing must be provided in published works using those functions. Tutorials on the various
Fiji plug-ins along with information on the authors of those plug-ins are provided at
http://fiji.sc/Category:Tutorials. A detailed guide about the basics of Fiji can be found at
http://rsbweb.nih.gov/ij/docs/guide/146.html.
As indicated throughout the text, the specific numbers that appear in this procedure are optimal for the
sample image used and different numbers may be needed for other images. Fiji has a built-in macro tool
(Plugins>Macros) that allows the creation of custom macros. Ultimately, the ability to automate the
image analysis depends on the similarity between the set of images analyzed. If all of the images that
are analyzed are sufficiently similar to one another in their general form and content, it would be
possible to run the exact same process to obtain reliable particle size distributions for every image.
81
Bibliography
[1] Stacy Davis, Susan Diegel, Robert Boundy. Transportation Energy. Data Book. Oakridge National
Laboratories. Technical Report. 2013
*2+Energy Information Administration (EIA)“International Energy Statistics.” Internet:
http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=5&pid=5&aid=2&cid=ww,&syid=1980&eyid=
2014&unit=TBPD, [Aug. 05, 2015].
[3] Organization of Petroleum Exporting Countries (OPEC). World Oil Outlook. 2012
*4+ Nick A. Owen , Oliver R. Inderwildi, David A. King. “The status of conventional world oil reserves—
Hype or cause for concern?” Energy Policy, vol. 38, pp 4743–4749, Feb. 2010.
[5] Natural Resources Canada. Oil Sands A strategic resource for Canada, North America and the global
market. Technical Report. Feb 2013.
[6] Pembina Institute. “Mining vs. In-situ: What is the highest environmental impact oil?” Technical
Report. Available : https://www.pembina.org/reports/mining-vs-in-situ.pdf
[7] Martinez, Andres W., et al. "Diagnostics for the developing world: microfluidic paper-based analytical
devices." Analytical chemistry 82.1 (2009): 3-10.
[8] Tsao, Chia-Wen, and Don L. DeVoe. "Bonding of thermoplastic polymer microfluidics." Microfluidics
and Nanofluidics 6.1 (2009): 1-16.
[9] Kim, Pilnam, et al. "Soft lithography for microfluidics: a review." (2008).
[10] Song, Wen, et al. "Chip-off-the-old-rock: the study of reservoir-relevant geological processes with
real-rock micromodels." Lab on a Chip 14.22 (2014): 4382-4390.
[11] Whitesides, George M. "The origins and the future of microfluidics." Nature442.7101 (2006): 368
[12] Vesna Slapar. “MICROFLUIDICS”. Technical Report. January 2008. Available: http://mafija.fmf.uni-
lj.si/seminar/files/2007_2008/Microfluidics-final.pdf
[13] Hidrovo, Carlos H., and Kenneth E. Goodson. "Active Microfluidic Cooling of Integrated
Circuits." Electrical, Optical and Thermal Interconnections for 3D Integrated Systems (2008): 293-330.
82
[14] Rapid microfluidics-based measurement of CO2 diffusivity in bitumen, H Fadaei, B Scarff, D Sinton,
Energy & Fuels 25 (10), 4829-4835
[15] Energy Alberta (Alberta Government). “Alberta ’s Energy Industry . An Overview” June 2012.
[16] Energy Information Administration (EIA), US Gov. “International energy data and analysis” Internet:
http://www.eia.gov/beta/international/ [August 5 2015]
[17] Dusseault, M. B. "Comparing Venezuelan and Canadian heavy oil and tar sands." Canadian
International Petroleum Conference. Petroleum Society of Canada, 2001.
*18+ Fran Hein, Ph.D., P. Geol. Chief Geologist, ERCB “Geology of the Oil Sands”
[19] Dusseault, M.B. and Morgenstern, N.R. 1978. Shear Strength of Athabasca Oil Sands. Canadian
Geotechnical Journal, 15: 216-238.
[20] Berkowitz, Norbert, and James G. Speight. "The oil sands of Alberta." Fuel54.3 (1975): 138-149.
[21] Doan D.H., Delage P., Nauroy J.F., Tang A.M., and Youssef S. 2012. Microstructural characterization
of a Canadian oil sand. Canadian Geotechnical Journal, 49 (10), 1212-1220
[22]Butler, R.M. 1997. Thermal Recovery of oil and bitumen. GravDrain Inc., Alberta, Canada.
[23] Takamura, K., 1982. Microscopic structure of Athabasca oil sand. The Canadian Journal of Chemical
Engineering, 60: 538–545.
[24]Shuhua, G. and Jialin, Q. 1997. Micro-structure model of some Chinese oil sand. Petroleum Science
and Technology, 15(9): 857-872
[25]Czarnecki, J., Radoe, B., Schramm, L.L. and Slavchev, R. 2005. On the nature of Athabasca Oil Sands.
Advances in Colloid and Interface. Science, 114–115: 53–60
*26+ Richard Chan. “Application of Field Performance Data in Developing Simple Analytical Models to
Predict the Performance of Steam Assisted Gravity Drainage.” MS.c. thesis, University of Calgary,
Canada, 2013.
*27+Alberta OILSANDS Inc. “Corporate Presentation”. February 2012. Available:
http://www.aboilsands.ca/_pdfs/Presentations/Corporate_Presentation-02-07-2012.pdf
83
[28] Gates, I. D., and N. Chakrabarty. "Design of the steam and solvent injection strategy in expanding
solvent steam-assisted gravity drainage." Journal of Canadian Petroleum Technology 47.09 (2008).
[29] Souraki, Yaser, et al. "Experimental analyses of Athabasca bitumen properties and field scale
numerical simulation study of effective parameters on SAGD performance." Energy and Environment
Research 2.1 (2012): p140.
*30+ Canadian Heavy Oil Association (CHOA). “Heavy Oil” .Technical Presentation. 2013. Available:
http://www.albertacanada.com/mexico/documents/P2.HeavyOil.pdf
[31] Guan, Jianguo. "Physical Properties of Athabasca Bitumen and Liquid Solvent Mixtures." Thesis.
(2013).
[32]M Ashrafi. Experimental Investigation of Temperature Dependency of Relative Permeability Data in
Heavy Oil Systems with Applications to Thermal Recovery. Thesis. 2013
[33]McLennan, Jason A., and Clayton V. Deutsch. "SAGD reservoir characterization using geostatistics:
Application to the Athabasca oil sands, Alberta, Canada." Press, Canadian Heavy Oil Association
Handbook (2004).
*34+ IHS Cambridge Energy Research Associates. “Oil Sands Technology Past, Present, and Future”.
Technical Report. Jan 2011
*35+ Alberta Department of Energy ‘Oil Sands: Production Technical Report. Profile.April 2012.
[36] Edmunds, N. (1999). On the Difficult Birth of SAGD. Journal of Canadian Petroleum Technology.
PETSOC 99-01-DA.
[37] Irani, Mazda, and Ian D. Gates. "Understanding the convection heat-transfer mechanism in steam-
assisted-gravity-drainage process." SPE Journal 18.06 (2013): 1-202
[38] Farouq-Ali, S.M. (1997). Is There Life After SAGD? Journal of Canadian Petroleum Technology.
PETSOC 97-06-DAS.
[39] Ito, Y. and Suzuki, S. (1996). Numerical Simulation of the SAGD Process in the Hangingstone Oil
Sands Reservoir, paper No.96-57, presented at the 47th Annual Technical Meeting of Petroleum Society
in Calgary, Alberta, Canada, June 10 - 12.
84
[40]Irani, Mazda, and Sahar Ghannadi. "Understanding the heat-transfer mechanism in the steam-
assisted gravity-drainage (SAGD) process and comparing the conduction and convection flux in bitumen
reservoirs." SPE Journal 18.01 (2013): 134-145.
[41]Geomechanical Effects on the SAGD Process Patrick M. Collins, PS-CIM/CHOA/SPE, Petroleum
Geomechanics Inc. SPE/PS-CIM/CHOA 97905, PS2005-97905
[42] Nguyen, Phong, Hossein Fadaei, and David Sinton. "Pore-scale assessment of nanoparticle-stabilized
CO2 foam for enhanced oil recovery." Energy & Fuels28.10 (2014): 6221-6227.
[43] Hu, Chuntian, James E. Morris, and Ryan L. Hartman. "Microfluidic investigation of the deposition of
asphaltenes in porous media." Lab on a Chip14.12 (2014).
[44]Mohammadzadeh, Omidreza, and Ioannis Chatzis. "Pore-level investigation of heavy oil recovery
using steam assisted gravity drainage (SAGD)."International Petroleum Technology Conference.
International Petroleum Technology Conference, 2009.
[45] de Haas, Thomas W., et al. "Steam-on-a-chip for oil recovery: the role of alkaline additives in steam
assisted gravity drainage." Lab on a Chip 13.19 (2013): 3832-3839.
[46]Sasaki, K., et al. "Experimental Modelling of the SAGD Process 3/4 Enhancing SAGD Performance
with Periodic Stimulation of the Horizontal Producer." SPE Annual Technical Conference and Exhibition.
Society of Petroleum Engineers, 1999.
[47] GD Mossop. Geology of the Athabasca oil sands. Science, 207(4427):145{152, 1980.
[48] Al Bahlani, As Muatasim Mohammad, and Tayfun Babadagli. "A critical review of the status of
SAGD: where are we and what is next?." SPE Western Regional and Pacific Section AAPG Joint Meeting.
Society of Petroleum Engineers, 2008.
[49] Al-Bahlani, Al-Muatasim, and Tayfun Babadagli. "SAGD laboratory experimental and numerical
simulation studies: A review of current status and future issues." Journal of Petroleum Science and
Engineering 68.3 (2009): 135-150.
[50] Rostami, Behzad, et al. "Identification of fluid dynamics in forced gravity drainage using
dimensionless groups." Transport in Porous Media 83.3 (2010): 725-740.
85
[51]Rostami, B., et al. "Gas-oil relative permeability and residual oil saturation as related to
displacement instability and dimensionless numbers." Oil & Gas Science and Technology–Revue de
l’Institut Français du Pétrole 65.2 (2010): 299-313.
[52] Yongnuan Liu Conoco Philips.Predicting steam assisted gravity drainage steam chamber front
velocity and location Patent. US 20130277049 A1
[53]Fraunhofer. Microfluidic-based system for the determination of asphaltenes in crude. Available:
https://www.imm.fraunhofer.de/content/dam/imm/de/documents/pdfs/PD_Asphaltene.pdf
*54+Kurss. “Interfacial Tension’. Available: http://www.kruss.de/services/education-
theory/glossary/interfacial-tension/
[55] Biolin Scientific. “Putting Tensiometry to Work” available:
http://www.biolinscientific.com/attension/applications/?card=AA12
[56] Schramm, Laurier L. Surfactants: fundamentals and applications in the petroleum industry.
Cambridge University Press, 2000.
*57+ Dow Chemical. “Surfactant Basics – Definition of HLP, and How it Applies to Emsulsions”. Available:
http://dowac.custhelp.com/app/answers/detail/a_id/3277/related/1/session/L2F2LzEvdGltZS8xNDM5O
TY2NjI0L3NpZC9zNjNGQ2h1bQ%3D%3D
*58+ Dow Chemical. “Surfactant Basics – Definition of critical micelle concentration (CMC)”. Available:
http://dowac.custhelp.com/app/answers/detail/a_id/3280
[59] Mukerjee, Pasupati, and Karol J. Mysels. Critical micelle concentrations of aqueous surfactant
systems. No. NSRDS-NBS-36. National Standard reference data system, 1971.
[60] "Determination of critical micelle concentration of some surfactants by three techniques." Journal
of Chemical Education 74, no. 10 (1997): 1227.
[61] Gray, Murray R. Upgrading Oilsands Bitumen and Heavy Oil. University of Alberta, 2015.
[62] Papavinasam, Sankara, Parviz Rahimi, Sandy Williamson. "Corrosion conditions and the Path of
Bitumen from Well to Wheel." Presented at the Symposium on Crude Oil Corrosivity. 2012.
[63] James J. Sheng. “Investigation of alkaline-crude oil interaction”. Petroleum. 1.1 (2015): 31-39
[64] Kehinde, Olatunji Adeniyi. "The effect of pH on interfacial properties of heavy oil/brine and
adsorption." (2012).
[65] Rogers, Vincent V., et al. "Acute and subchronic mammalian toxicity of naphthenic acids from oil
sands tailings." Toxicological Sciences 66.2 (2002): 347-355.
