alzahid2017

SPE-185832-MS
Alkaline Surfactant Polymer Flooding: What Happens at the Pore Scale?
Yara Alzahid, Peyman Mostaghimi, Majid Ebrahimi Warkiani, and Ryan T. Armstrong, The University of New
South Wales, Sydney, Australia; Vahid Joekar-Niasar and Nikolaos Karadimitriou, The University of Manchester,
Manchester, United Kingdom
Copyright 2017, Society of Petroleum Engineers
This paper was prepared for presentation at the SPE Europec featured at 79th EAGE Conference and Exhibition held in Paris, France, 12–15 June 2017.
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Abstract
Alkaline-surfactant-polymer (ASP) flooding is a Chemical Enhanced Oil Recovery (CEOR) method
whereby alkali, surfactant and polymer are injected as the same slug. It is one of the most promising worldwide focus of CEOR research and field trials, due to the unique synergy of the three chemical components.
Polymers increase the viscosity of injected water, which improves macroscopic sweep efficiency by
stabilizing the displacing front and counteracting heterogeneity effects. Surfactants, on the other hand,
decrease the interfacial tension between the injected water and crude oil, which improves microscopic
displacement efficiency by mobilizing trapped oil. Alkaline chemicals generate soap when reacting with
crude oil, which reduces surfactant adsorption to grain surfaces. To fully understand the flow mechanisms
in oil reservoirs and develop efficient recovery methods, it is essential to recognize the physics at the pore
scale since this is the length scale at which capillary-trapped oil is mobilized. We developed an experimental
micro-scale approach in which oil recovery is analyzed using microfluidics. The micromodels are fabricated
based on: (1) a pore network generated via a Delaunay triangulation with an average pore size of 60 µm,
(2) X-ray micro-Computed Tomography images of Bentheimer sandstone with resolution of 4.95 µm, and
(3) a fractured network with a porosity of 10%. The networks are etched into silicon wafers and used to
fabricate polydimethylsiloxane (PDMS) microfluidic devices. We compare not only oil recovery with ASP
flooding between different porous networks but we also study displacement mechanisms and pore scale
emulsion formation for ASP formulations that exhibit Winsor Type II+, II- and III behaviour. Our studies
elucidate micro-scale oil recovery mechanisms for different ASP flooding scenarios. Our results provide
direct visualization of the micro-scale phenomena occurring during ASP flooding and will guide further
studies to target effective ASP flooding scenarios.
Introduction
Chemical enhanced oil recovery (CEOR) uses three main chemical compounds to recover additional oil,
which are alkaline, surfactants and polymer. Alkaline and surfactant contribute to lowering the interfacial
tension (IFT) between water and oil, which helps mobilize trapped oil in the pores. Alkaline however also
generates soap in-situ when reacting with crude oil and helps in the reduction of surfactants adsorption
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on grain surfaces, both alkaline and surfactant aid in improving the microscopic displacement efficiency.
Polymers contribute to CEOR by stabilizing the displacing front by modulating the viscosity of injected
water, thereby improving the macroscopic sweep efficiency. Alkaline-surfactant-polymer (ASP) flooding
utilizes the aforementioned chemicals, and by combining the injected surfactant and in-situ surfactant
generation with controlled mobility of oil from the polymer, gives ASP flooding a potential advantage over
other CEOR methods. It is one of the most promising world-wide focus of CEOR research and field trial,
due to the synergy of these three chemical components (Olajire 2014). An average of 22% incremental oil
recovery (IOR) has been reported when evaluating 21 fields trails that implemented ASP flooding (Sheng
2013). ASP flooding aims to enhance both the microscopic displacing efficiency by increasing capillary
number (Nc) and decreasing IFT and the macroscopic displacing efficiency by improving the mobility
number (Nm).
Analyzing the displacement patterns in multiphase flow is crucial for understanding transport properties.
Lenormand (1990) reported that there are three main displacement patterns that are achieved in multiphase
flow: (1) viscous fingering, (2) capillary fingering and (3) stable displacement. These patterns are
characterized by capillary number (Nc) and mobility number (Nm). Nc shows the effect of viscous to
interfacial tension forces, which is represented as
(1)
where μ is dynamic viscosity (Pa·s), u is brine velocity (m/s), and σ is the interfacial tension (N/m) between
oil and brine.
Nm is defined as the ratio of invading fluid viscosity to the defending fluid viscosity. Viscous fingering or
unstable displacement occurs when the pressure drop is negligible in the invading fluid (large Nm). Capillary
fingering; however, occurs when the injection rate is very low and viscous forces are negligible in both
fluids. Lastly, stable displacement occurs at high flow rate when capillary forces are low and with negligible
pressure drop in the defending phase (small Nm). Figure 1 displays a phase diagram for the three unique
displacement regimes that can occur.
Figure 1—The phase diagram showing the three displacement patterns of non-reactive flow in porous media.
Microfluidics is defined as the study of fluids at a small scale, ranging from microliters to picolitres.
These studies can be of interest to multiple fields of study within science and engineering. Applications
include drug development, biomedical analysis, genetics, proteomics, and energy conversion (Kjeang et al.
2009; Lifton 2016; Whitesides 2006). An advantage of using microfluidics is that that it allows for small
sample volumes and reagents, providing low cost, fast high-resolution analysis and a smaller footprint in
comparison to other analytical devices (Whitesides 2006; Zhao & Middelberg 2011). Most of the current
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research in microfluidic systems has been carried out in polydimethylsiloxane, or PDMS, which is a soft
elastomer that is optically transparent. In petroleum engineering studies, what has been extensively studied
in the past is the use of glass-etched micromodels based on network patterns (Meybodi et al. 2011), and the
results of which are often correlated and included in pore-network models to compute multiphase transport
properties (Lenormand 1990; Culligan et al. 2006). Currently, 3D representations of rock microstructure
can be generated using micro-computed tomography (micro-CT), focused ion beam-scanning electron
microscopy (FIB-SEM) and/or nuclear magnetic resonance (NMR) imaging. These data can be used to
generate 2D relizations of the pore space that can be used for further microfluidic investigations. However,
important fluid-rock interactions, such as wetting characteristics, are difficult to recreate (Gunda et al.
2011). Therefore, there is a need for experimental studies at the micro-scale to validate and improve
these simulations to understand multi-phase flow and in particular surface properties. Gunda et al. (2011)
generated the so-called "Reservoir-On-a-Chip" (ROC) and investigated the displacement of non-wetting
fluid by wetting fluid using ROC. They generated the pore network via a Delaunay triangulation routine
in Matlab, which was then imported in AutoCAD, and lastly transferred to a glass substrate. They used
piranha solution and plasma reactive ion etching (ICPRIE) to ensure a water-wet environment. Buchgraber
et al. (2011) reported the use of a microfluidic device of a Berea sandstone pattern etched into silicon to
study polymer flood displacement efficiency of viscous oil in a water-wet environment. In addition, Nilsson
et al. (2013) developed a hydrophobic microfluidic sandstone device from micro-CT sandstone images to
investigate the effect of surfactant fluid rheology on oil recovery. Gerami et al. (2016) used PDMS chips of
a realistic coal cleat system to measure relative permeability by monitoring the liquid recovery at recorded
saturations after breakthrough. Pore network modelling was also conducted in this study to predict relative
permeability curves, which was in agreement with the experiments.
In this manuscript, we analyze the phase behaviour and IFT of three surfactants types with varying
salinities to select an optimum surfactant. We then compare oil recovery curves using surfactant floods
with different salinities in a fractured model and oil recovery curves of AS and ASP floods at optimum
salinity for a sandstone network. Lastly, we compare displacement mechanisms using two models: (1) porenetwork model generated from Delaunay tessellation and (2) a representation of sandstone rock from microCT images.
Materials and Methods
Chemicals
Three surfactants produced by Shell Chemicals, known as the ENORDET O series, were selected due to
their relevance for EOR field applications. Sulfonated hydrolyzed poly-acrylamide (HPAM) was chosen to
be the main polymer in use, as it is the most widely used polymer type in EOR field studies. Details of the
chemicals are shown in Table 1.
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Table 1—Details of the polymer and surfactants used in the study
Equilibrium Phase Behavior Tests
Two different types of phase behaviour tests were conducted; (1) surfactant—oil—water system and (2)
ASP—oil—water system. Aqueous solutions of surfactant or ASP solutions with cosolvent were prepared
as listed in Table 2. The salinity of the solutions was varied by adding sodium chloride (NaCl). For each
salinity, a 10 ml test tube was prepared that consisted of 5 ml of surfactant with cosolvent or ASP solution
with cosolvent and 5 ml of n-decane (mixing ratio of 1:1). Test tubes were shaken and then allowed to
separate at ambient and stagnant conditions. The solubilization was analyzed by visually measuring the
volumes of the formed phases, optimum salinity was then selected based on equal oil and water solubilisation
ratios for the emulsion phase.
Table 2—Surfactant Solution formulation
IFT and Rheology Measurements
A spinning drop tensiometer (M6500, Grace Instrument, USA) was used for the IFT measurements of the
surfactant-oil-water system at room temperature. A rheometer (MCR 502, Anton Paar, Austria) was used
for viscosity measurements of the ASP and HPAM formulations at room temperature.
Pore Morphologies
A benchtop helical micro-CT scanner at the Australian National University was used to image a Bentheimer
sandstone sample (4.5 mm diameter and 10 mm long). The resolution of this image was 4.95 µm, with
an image size of 891 × 891 ×1440 voxels. The core was imaged at 65 amp and 100 meV. The second
network pattern was a pore network generated via a Delaunay triangulation routine in Matlab, as previously
described in Gunda et al. (2011), with an average pore size of 60µm. The third pattern was generated by
micro-CT images of fractures that were overlayed using an image quilting technique to make a fracture
network. Figure 2 shows the patterns used for this project.
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Figure 2—Patterns of different chips used. (a and b) shows the patterns used for IOR and microscale imaging of sandstone and fracture models, respectively. (c and d) show the models used
for displacement studies of sandstone and the Delaunay tessellation pore networks, respectively.
Microfluidic Chips Fabrication
The sandstone microfluidic chip was fabricated by first calibrating one slice of the 3D micro-CT image in
ImageJ (v1.48, National Institutes of Health, USA) for 4.95 μm per pixel image resolution, which was then
transferred to AutoCAD (Version 2017, Autodesk Inc.). The sandstone network pattern was then mirrored
twice and seven times to achieve the required dimensions for IOR experiments and displacement studies,
respectively. The patterns were then transferred into a chrome mask for silicon mould microfabrication
using standard lithography techniques described previously (Warkiani et al. 2011). The silicon wafers were
patterned and etched using deep reactive ion etching (DRIE) to define the channel features on the wafer to
achieve an etch depth of 50µm. It is then salinized with trichloro (1H, 1H, 2H, 2H-perfluorooctyl) silane
to render the surface hydrophobic for subsequent PDMS coating. PDMS chips were prepared by mixing
the PDMS monomer with the curing agent at a standard 1:10 ratio and degassing in a desiccator. PDMS
mixture is then poured onto the silicon mould and cured at 80 °C for 1–2 hours, which is then cut from
the mould; the inlet and outlet are created with a 1.5mm diameter puncher. Lastly, the PDMS chips and
PDMS coated glass slides are subjected to oxygen plasma treatment (Harrick Plasma Cleaner, NY, USA)
for 4 minutes and 15 seconds and then are immediately brought into contact with each other to create the
siloxane linkage, ensuring a water-wet environment.
Experimental System
New PDMS microfluidics chips were generated for each experimental study to ensure no contamination
occurred between sequential experiments. The chips were first injected with their respective water salinity,
meaning that the salinity matched the salinity at which the subsequent EOR treatment would be conducted.
Then decane (Sigma Aldrich, USA) is injected to achieve a 90-80% oil saturation. Surfactant at different
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salinity was mixed with 1.0 wt.% food coloring the color of which depends on the experiment conducted
and is explained in the results section (AmeriColor, Placentia, CA, USA) and was then injected at a capillary
number of 10−6. A high-precision infusion syringe pump (Fusion 100 Infusion, Chemyx, Australia) is used
to monitor and control the flow rate. Images are taken using a CCD digital camera (Zeiss, Axiocam 506
color) coupled with a stereo zoom microscope (Zeiss, Axio Zoom V16) with frame resolution of 2752 ×
2208 pixels. Figure 3 (a) shows the experimental setup used.
Figure 3—(a) Experimental set-up for IOR and visualization experiments and (b) displacement experiments set-up.
For the IOR tests, oil recovery is measured via image processing of the captured images. We used
ImageJ with the assumption of a 2-dimensional fluid system meaning that only a single phase can occupy a
given pixel. For displacements experiments, images were collected using a digital camera (GH4, LUMIX)
and these images were then analyzed using ImageJ. A high-precision infusion syringe pump (Fusion 100
Infusion, Chemyx, Australia) was also used to inject the different fluids. Figure 3 (b) shows the set up for
the displacement experiments.
Results and Discussion
Phase Behavior of Surfactant-Oil-Water Systems
To select a surfactant, first we measured the IFT between DI water with surfactant and decane (Table 3).
Table 3—IFT measurements of surfactant—oil—DI water
From table 3, 0.5% (w/v) of ENORDET J13131showed the lowest IFT and thus was chosen for phase
behavior tests with varying NaCl content. Figure 4 shows the phase behavior tests and Table 4 reports the
volume of emulsion formed at under-optimum, optimum and over-optimum salinities.
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Figure 4—Phase behaviour of 0.5% ENORDENT J13131 and 2% Butanol at varying NaCl content (3.6 - 4.5 % w/v). The
red boxes indicate the middle phase, decane is on top of the middle phase and brine is shown as the clear solution.
Table 4—Phase behaviour measurements
Solubility Measurements
Water and oil solubility ratios, γw and γo, which are defined as the amount of oil or water that is soluble in the
microemulsion phase, were determined by visually inspecting the phase behaviour tubes shown in Figure
4. Vw is the volume of emulsion below the middle line (at 5ml) and Vo is the volume of emulsion above
the middle line. Under optimum salinity is when Vw > Vo, which displays Winsor Type II- phase behaviour.
Conversely, over-optimum salinity occurs when Vw < Vo, which displays Winsor Type II+ behaviour. The
optimal salinity is when Vw is equal to Vo, which was found at 3.9% salinity. The solubility coefficients
are reported in Figure 5.
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Figure 5—Solubility ratios of surfactant in aqueous and oil phases. Water (γw) and oil (γo) solubility ratios as a
function of salinity (NaCl% w/v). Increasing NaCl content, the water solubility decreases, as opposed to oil solubility.
As discussed in Unsal et al. (2016), the equilibration time for the middle phase formation is important
to note for the different salinities. Similar to their findings, under and optimum middle phase formation
occurred at a much faster rate than the over optimum tubes. Within 5-7 hours, the emulsion at under-optimum
and optimum conditions appeared and reached equilibrium. However, the over-optimum salinity took 2 days
to show the emulsion phase and reach equilibrium. Consistent with the findings from Unsal et al. (2016),
salinity plays a pivotal role in the surfactant equilibration times.
Phase Behavior of ASP—Oil—Water Systems
ASP phase behavior tests showed no middle phase formation for the salinities tested (Figure 6). The ASP
formulation and composition is similar to that studied by Humphry et al. (2013) where they added 0.25%
Shell Chemicals ENORDET H771, 0.25% Shell Chemicals ENORDET O242, 1% sodium carbonate, 2%
2-butanol by weight and 1300 ppm Flopaam 3330S polymer (SNF-Floerger). Their salinity screening test
showed a middle phase across the different tubes, with an optimal salinity being in the range of 3.9% NaCl
- 4.0% NaCl. One factor that would explain the different results obtained by us is the different types of
surfactant used. In our study, the surfactant type was an alkyl ether sulfate, which is known to be tolerant
to divalent ions (Negin et al. 2016). However, in Humphry et al. (2013) a combination of an alkyl ether
sulfate (ENORDET H771) and an internal olefin sulfonate (ENORDET O242) were used to give a total of
0.5% (w/v) of surfactant. This combination has proven to be efficient for CEOR processes, hence, giving
the middle phase in their phase behavior. Whereas, our study used one type of surfactant, an alkyl ether
sulfate surfactant only. Another aspect that was investigated was the amount of alkaline concentration added
in the phase behavior tubes, as 1% (w/v) sodium carbonate was used in a different test (data not shown)
and it displayed results similar to Figure 6. In addition, alkaline (0.5% w/v) surfactant (0.5% w/v) phase
behavior tests were conducted to address the polymer effect on the phase behavior results, no middle phase
emulsion was formed and the alkaline remained in the aqueous phase, causing it to remain cloudy (data not
shown), which could suggest that the HPAM polymer remained in the aqueous phase, as it is water soluble,
and pushed the alkaline and surfactant to the oil phase, making it appear hazy, as seen in Figure 6.
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Figure 6—ASP-oil-water Phase Behaviour in different NaCl % (w/v)
Viscosity Measurements of ASP Formulations
To understand the displacement mechanisms, it is essential to identify the rheology of the ASP system. We
measured the viscosities of ASP formulations (0.5% NaCO3, 0.5% ENORDET J13131 and 0.1% HPAM w/
v) at different salinities (3.6%-4.5% NaCl w/v) using the shear rheometer under shear rates ranging between
0.1 and 100 s–1 at room temperature. Figure 7 (a) shows the polymer viscosity measured for the optimum
salinity (3.9% NaCl w/v) at different shear rates. It exhibited a shear-thinning behavior, with a viscosity of
22.4 mPa.s at a shear of 0.1 s−1 and decreasing to 10.1 at a shear rate of 1000 s−1. A shear rate of 7 s−1 is most
commonly used to represent the fluids that are injected into a reservoir (El-hoshoudy et al. 2016). Figure
7 (b) shows the viscosity at room temperature at a shear rate of 6.81 s−1 for the different salinities of the
ASP formulation. As can be seen, there are some fluctuations of viscosity with response to salinity ranging
from 7.8-5 mPa.s; however, the effect appears minimal. From previous studies, HPAM can degrade under
the influence of NaCl, CaCl2 and MgCl2, therefore the viscosity of the polymer is effected (Gao, 2014),
which is what we observed in Figure 7 b.
Figure 7—(a) The viscosity of (0.5% NaCO3, 0.5% ENORDET J13131 and 0.1% HPAM w/v) at 3.9 NaCl% (w/
v) in response to shear rate. (b) Viscosities of ASP formulations (0.5% NaCO3, 0.5% ENORDET J13131
and 0.1% HPAM w/v) at different salinities (3.6%-4.5% NaCl w/v) at a representative shear rate of 6.91 s−1.
Flooding at Fractured Network
Surfactant floods of under-optimum, optimum and over optimum salinity had an average initial oil saturation
of 90% and a total oil recovery of 82.3%, 82.5% and 63.5%, respectively. The results in Figure 8 are reported
in terms of incremental oil recovery (IOR), which is the amount of oil recovered after oil production from
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waterflooding reaches a plateau. The surfactant flood at under-optimum salinity achieved the highest IOR of
25%, followed by an IOR of 14% for over-optimum salinity and lastly an IOR of 12% for optimum salinity
(Figure 8). From these recovery studies, we can conclude that salinity has an effect on the efficacy of the
surfactant to produce more oil. Alagic et al. (2011) showed that combining low salinity brine (composed of
0.5% NaCl) with surfactant flooding resulted in high oil recovery in aged sandstone cores, compared to a
higher salinity of surfactant flooding. The authors claim that by using surfactant flooding at low salinity, it
would avoid re-trapping of oil at lower capillary pressure when compared to the classical oil mobilisation
as an effect of high capillary number. This could be an explanation to the results we obtained.
Figure 8—IOR vs. PV of surfactant flooding at different salinities in fractured network
Flooding in Sandstone Network
Incremental oil recovery at optimum salinity for surfactant and ASP flooding is reported in Figure 9. The
initial oil saturation of both experiments was 90% and the porosity of the sandstone chip was 25%. Surfactant
flooding at optimum salinity yielded an IOR of 20%, which is higher than the IOR for the optimum salinity
of the fracture network. This can be attributed to the higher porosity found in sandstone when compared
to the fractured network. The ASP flood started with an Alkaline Surfactant flood, giving an IOR of 18%,
and then the sandstone network was injected with an ASP slug, resulting in an IOR of 31%. The results
demonstrate that a low concentration of 0.1% (w/v) of polymer can increase the IOR by 72.2% in comparison
to the AS slug.
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Figure 9—IOR vs. PV of Surfactant flooding and ASP flooding at optimum salinity in Sandstone pattern
Micro-Scale Images of Surfactant Flooding, AS and ASP Flooding
High-resolution images were collected to visualize the interaction of surfactant solution with oil and
interstitial water at different salinities. Brine at under-optimum, optimum, and over-optimum salinities
(colored red) was first injected into the fractured network followed by oil saturation (transparent in color).
Then surfactant at a corresponding salinity (colored green) was injected. Figure 10 (a-d) displays images
at under-optimum conditions. For this case, very little interaction and mixing occurred between surfactant
and oil, which can be correlated to the phase behavior tests, as most of the emulsion was formed in the
aqueous phase. We also see very little mixing between the surfactant solution (green) and interstitial water
(red), which would have resulted in a shadowy color. However, at optimum salinity (Figure 10 e-h), mixing
between oil-brine-surfactant occurred at multiple sites in the fractured network giving the dark green color.
At over-optimum salinity (Figure 10 i-l), multiple sites in the fracture network captured oil travelling freely
with the surfactant without mixing; hence, very little emulsion was formed. This occurred due to the higher
salinity, with a high oil solubility ratio.
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Figure 10—Surfactant (0.5% ENORDET J13131 w/v) oil (decane) interactions at different salinities in fractured network.
(a-d) under-optimum with 3.6% NaCl (w/v), (e-h) optimum with 3.9% NaCl (w/v) and (i-l) over-optimum with 4.5%
NaCl (w/v). Decane is transparent, green is the surfactant solution, and red is brine at the corresponding salinities.
We also investigated micro-scale images of the AS and ASP formulations at 3.9% NaCl (w/v) in the
sandstone network. The AS solution consisted of 0.5% ENORDET J13131 surfactant (w/v), 0.5% NaCO3
(w/v), 3.9% NaCl (w/v) and green dye (1% w/v). Figure 11 (a) shows an overview of the initial stages
of the AS flooding. Some channels in the chip (shown on the left of Figure 11 a) were difficult to access
with AS flooding and thus some oil was trapped in these channels. Figure 11 (b-c) shows the mixing of
AS with decane (shown in middle section of Figure 11 b-d). Figure 11 (a-d) images are similar to images
obtained in previous sections in terms of how the surfactant is mixing with decane. After the AS flood, an
ASP slug was injected as shown in light red in Figure 11 (e-h). The ASP slug consisted of 0.5% NaCO3
(w/v), 0.5% ENORDET J13131 (w/v), 0.1% HPAM (w/v) and 1% red dye (w/v). It can be concluded from
the images obtained that ASP successfully swept most of the trapped oil, due to altering the mobility ratio,
hence improved sweep efficiency. Figure 11 (e-h) show the ASP sweeping the AS slug (green) and some oil
trapped in the pores with the AS slug. Figure 11 (h) shows the final stage of ASP injection, and it can be seen
that all of the liquid present in the chip is the ASP with some interstitial water present in the narrow channel
at the top and bottom of the image and one section in the middle of the chip showed the AS slug left behind.
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Figure 11—AS and ASP flooding in sandstone patterned PDMS chip at 3.9% NaCl content (w/v). (a-d)
Alkaline (0.5% NaCO3 w/v), surfactant (0.5% ENORDET J13131 w/v) flooding. (e-h) Alkaline (0.5% NaCO3
w/v) surfactant (0.5% ENORDET J13131 w/v) and polymer (0.1% HPAM w/v) flooding. Dark red is connate
water (4.5% NaCl w/v), decane is transparent, green is the AS solution, and light red is the ASP solution.
Surfactant, AS and ASP Displacement Studies
Here we analyzed the displacement patterns that occur with surfactant, AS and ASP flooding at a constant
capillary number (Nc) of 10−6, which is considered typical for flow through reservoir pores. We investigated
two unique pore network patterns. For both of these models, the PDMS chips were saturated with decane
oil and then subjected to their respective flood type at ambient conditions. A red dye was used with the
injected chemical to improve its contrast with decane, which was transparent.
First, we show the displacement of a Delaunay triangulation pattern when injected with surfactant
solution using 0.5% of ENORDET J13131 (w/v) in 3.9% NaCl% (w/v) (Figure 12 a and b). Figure 12 (a)
shows the initial stage of injection from the inlet, and a initial stable displacement is observed. However,
when the displacement reaches towards the end (Figure 12 b), some instabilities in the flow can be observed
on the right side near the outlet port, and we also observed significant oil left behind, as the intensity of the
dye was very light, which implies that the surfactant flooding would not be able to sweep all of the oil in
the pattern, as confirmed previously from IOR studies of different pore network models. When ASP was
injected (0.5% NaCO3 w/v, 0.5% ENORDET J13131 w/v and 0.1% HPAM w/v) at 3.9% NaCl% (w/v) into
an oil saturated chip of the same pattern, a more stable flow with better sweep was noticed at the first stage
of the injection (Figure 12c). Towards the end of the displacement study (Figure 12d), the intensity of the
dye was realized, indicating a good sweep efficiency of the ASP formulation when compared to surfactant
flooding in Figure 12 a and b.
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Figure 12—Displacement of decane oil (transparent) in a Delaunay tessellation pore network by (a-b) surfactant flooding
of 0.5% ENORDET J13131 (w/v) at a salinity of 3.9% NaCl (w/v), and (b-c) Alkaline (0.5% NaCO3 w/v) surfactant (0.5%
ENORDET J13131 w/v) and polymer (0.1% HPAM w/v) flooding at 3.9% NaCl (w/v). Injection of surfactant and ASP floods were
coloured with red dye and shown here in black, decane oil is transparent. Both a and c show the initial stage of surfactant
flooding and ASP flooding, respectively. The final stages of each flood type after breakthrough are shown in b and d.
Lastly, we investigated the displacement of AS and ASP in the sandstone chip. Each flood was colored
in red (shown in the results as black) and performed in a new PDMS chip that was saturated with decane oil
(transparent). During the AS flood, the start of the injection (Figure 13a) displayed a very unstable front,
and the fluid moved through fracture lines that were generated due to the mirroring of the sandstone image
to achieve the chip dimensions of 58×46 mm2. At the end of the AS displacement (Figure 13 b), it can be
seen that very little oil was recovered and a slightly unstable flood front was visible, due to the low intensity
of the dye captured in the images. When ASP was injected (Figure 13 c), the flood front apeared more stable
than the previous AS flood, and towards the end of the flooding (Figure 13 b), the dye was intense in the
image, suggesting a stable front with better sweep efficiency, as exhibited from the ASP displacement in
the previous section.
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Figure 13—Displacement of decane oil (transparent) in a representative sandstone pore network by (ab) Alkaline surfactant flooding of 0.5% NaCO3 (w/v) and 0.5% ENORDET J13131 (w/v) at a salinity of 3.9%
NaCl (w/v), and (c-d) Alkaline (0.5% NaCO3 w/v) surfactant (0.5% ENORDET J13131 w/v) and polymer (0.1%
HPAM w/v) flooding at 3.9% NaCl (w/v). Injection of AS and ASP floods were colored with red dye and shown
here in black, decane oil is transparent. Both a and c show the initial stage of surfactant flooding and ASP
flooding, respectively. The final stages of each flood type after breakthrough are displayed in b and d.
Conclusion
We demonstrated the phase behavior of a selected surfactant exhibiting Winsor type II+, II- and III behavior.
We characterized the solubility ratios and determined the optimum salinity being 3.9% NaCl (w/v). We
measured viscosities of ASP formulations with varying NaCl content, which resulted in a shear thinning
behavior of the solutions and a viscosity ranging from 8-5 mPa.s at a shear rate of 6.81 s−1. We then
conducted microfluidic flow experiments to measure IOR for different flooding scenarios and studied the
micro-scale mixing of the phases and components. We analyzed IORs in two different PDMS models, a
fractured network and a sandstone network. The fracture models analyzed IORs of surfactant floods at
varying NaCl content (3.6%, 3.9% and 4.5% w/v), with the highest IOR achieved by the under-optimum
salinity (3.6% NaCl w/v), proving that salinity has an effect on the efficacy of the surfactant to produce
more oil. With the sandstone model, we tested the IOR achieved by surfactant flooding and AS followed
by ASP flood at optimum salinity, and as expected, the ASP flood achieved the highest IOR. ASP resulted
in an IOR increase of 72.2% when compared to AS flooding at optimum salinity. These IOR studies were
16
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then visualized using the same two models (fracture and sandstone) at the micro-scale. The images obtained
complemented the IOR results, as we were able to visualize the interaction of the surfactant with oil at
different salinities and the interaction of AS and ASP with different network patterns saturated with decane
oil. Lastly, we demonstrated the displacement mechanisms of surfactant flooding, AS and ASP using the
Delaunay tessellation pattern and a sandstone pattern. From these displacement studies, we found that
displacement efficiency is dependent on, as expected, the viscosity of the displacing fluid. Also, the porescale morphology of the porous networks tested provided significantly different displacement patterns for
the exact same capillary and mobility numbers.
Acknowledgment
The authors would like to thank EXPEC ARC department at Saudi Aramco, specifically the Chemical
EOR group for allowing us to use their spinning drop and viscometer machines and obtain results that
were essential to this study. In addition, the authors would like to acknowledge the Microfluidics lab at the
Mechanical and Manufacturing engineering school at UNSW for their support with the PDMS visualization
studies.
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