Heavy-Oil Recovery by Combined Hot Water and Alkali ... - OnePetro

J170161 DOI: 10.2118/170161-PA Date: 3-February-16

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Heavy-Oil Recovery by Combined Hot Water and Alkali/Cosolvent/Polymer Flooding M. Tagavifar, R. Fortenberry, E. de Rouffignac, K. Sepehrnoori, and G. A. Pope, the University of Texas at Austin

Summary A hybrid process is developed and optimized for heavy-oil recovery that combines moderate reservoir heating and chemical enhanced oil recovery in the form of alkali/cosolvent/polymer flood. The process is simulated by use of a model derived from existing laboratory and pilot data of a 5,000-cp heavy-oil field. It is found that hot waterflooding is efficient in heating the reservoir only when high early injectivity is achievable. This may not be the case if incipient fluid injectivity is low and/or long, continuous, horizontal shale baffles are present. To remedy the former, an electrical-preheating period is devised, whereas switching to a horizontal flood could overcome the latter. Once the reservoir temperature is raised sufficiently, a moderately unstable alkali/ cosolvent/polymer flood is capable of mobilizing and displacing oil. A best combined strategy for efficient reservoir heating, high oil recovery, and cost effectiveness is found to involve reducing the oil viscosity to values of approximately 300–500 cp and combining a degree of mobility control and low interfacial tension as recovery mechanisms. Introduction Heavy-oil-recovery methods range from cold production to complex thermal enhanced-oil-recovery (EOR) technologies. The most common thermal-recovery methods use steam to heat the oil in place (Shah et al. 2010; Al-Bahlani and Babadagli 2009; Ardali et al. 2012), but steam injection is not feasible or economic under certain common conditions. Hybrid processes, despite the possible greater complexity, may help overcome the technical challenges involved. In this paper, we focus on a new hybrid process that combines chemical EOR and moderate reservoir heating. Recent field and laboratory evaluation of nonthermal chemical EOR for heavy-oil recovery has shown promising results (Delamaide et al. 2013; Fabbri et al. 2014; Levitt et al. 2011; Kang et al. 2011; Fortenberry et al. 2013; Gao 2011; Kumar and Mohanty 2010). Yet there is much to gain if the chemical EOR is performed at an elevated temperature, which may be achieved by heating injection water. Besides lowering the oil viscosity with increasing temperature, it is known that chemicals perform better at elevated temperatures. These two features improve the hybridprocess economy and robustness through efficient (and perhaps less) use of chemicals at little incremental cost of heating injection water. Injection of hot water or even polymer solutions is practical for viscous-oil reservoirs (i.e., viscosity up to several hundred centipoise), which pilot trials have shown [an example of polymer flood can be found in Han et al. (2006)]. However, fluid injectivity may initially be low, if present, for heavy oils (i.e., viscosity up to several thousand cp). This demands a reservoir-preheating period by means that require no injection of a heatbearing fluid. Electrical heating has been used for such purposes in the past (Glandt 1991; Pizarro and Trevisan 1990;), and in hybrid processes more recently in which water (McGee and Vermeulen 2007), a gas (Zhong et al. 2011), or a solvent (Zhu and C 2016 Society of Petroleum Engineers Copyright V

This paper (SPE 170161) was accepted for presentation at the SPE Heavy Oil Conference— Canada, Calgary, 10–12 June 2014, and revised for publication. Original manuscript received for review 10 July 2014. Revised manuscript received for review 2 January 2015. Paper peer approved 25 February 2015.

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Zeng 2012) is injected during or after the electrical heating. To generalize the hybrid process proposed here, an electrical-preheating period is considered that may not be required in all cases. A simulation study was conducted to assess the performance of the proposed process in terms of reservoir-heating efficiency, fluid injectivity and oil recovery, and cost effectiveness. In the absence of a simulator capable of modeling both electrical heating and nonisothermal chemical EOR, CMG-STARS (CMG 2011) and UTCHEM (Bhuyan et al. 1990; Delshad et al. 1996; Mohammadi et al. 2009) were coupled to simulate the former and the latter, respectively, in a stepwise manner. The simulation model was built using the reservoir properties of the Bluesky formation in western Canada (Koci and Mohiddin 2007a, 2007b; Shell Canada Ltd. 2009) and related chemical EOR coreflooding experimental work (Fortenberry 2013). The in-situ oil viscosity is approximately 5,000 cp, which roughly corresponds to the upper feasibility limit of polymer flooding (Delamaide 2014). In addition, this high oil viscosity impedes achieving high-enough injection rates early during hot waterflooding for efficient reservoir heating; therefore, a preheating period was considered. It should be noted that hot waterflooding at low injection rates is typically inefficient in heating thin heavy-oil reservoirs because of early water breakthrough and heat losses. There are many questions about the details of the proposed process, including such issues as the efficiency and length of heating by electrical heating and hot waterflooding, the elevated temperature at which the chemical flood can be performed, and the details of chemical-flood design. To answer these questions and to find out whether an optimal strategy exists that is economically feasible, an optimization study was performed to identify the best combined-operation strategy. A detailed description of the proposed process is given in the Recovery Scheme and Wellbore/Electrode Configuration section. Thermal and electrical properties of the rock and fluids that determine the energy transport are given in the Rock and Fluid Properties section. The Simulation Model section presents the simulation model and provides an overview on how heterogeneities affect the process. The results of the optimization study are presented in the Process Optimization for Efficient Reservoir Heating and Oil Recovery section. Recovery Scheme and Wellbore/Electrode Configuration In the proposed process, chemical enhanced oil recovery (EOR) is preceded by hot waterflooding and a possible short electrical preheating that may be required in the case of low incipient fluid injectivity. The same set of wells is used for injecting both energy (by use of casing as electrode) and fluids, and the inclusion of a preheating step requires only minimal efforts. In electrical-heating processes, the intensity of heating is inherently greatest within the near-wellbore region, which allows for creating fluid injectivity. Unlike in the McGee and Vermeulen (1996) approach, no significant oil should be produced in this stage to prevent produced fluids from removing the generated heat from the reservoir (Rice et al. 1992). In addition, this approach increases the pressure and energy of the formation before production, which results in higher ultimate oil recovery (McGee and Vermeulen 2007). Because of the moderate efficiency of heating the reservoir electrically (Das February 2016 SPE Journal

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floods recovered almost all the oil from these cores. A set of these corefloods was used to develop and parameterize the simulation model of the chemical-flood studies.

Cross section Vrms∠0

Stage:

Vrms∠0

Horizontal Well

Fig. 1—Reservoir model and wellbore/electrode configuration. The wellbore arrangement allows for the use of three-phase electrical power during preheating (if necessary) and efficient flooding during fluid injection. The voltage/phase of each electrode is indicated by Vrms \h , where Vrms is root-mean-square voltage.

2008) and no oil production, the time length of this stage would be short (often less than 3 months). In addition, the electricaloperating conditions should be chosen to avoid near-wellbore water vaporization (McGee and Vermeulen 2007). An electrode configuration that would facilitate more-uniform heating and hence less risk of near-wellbore water vaporization is presented in Fig. 1. Oliveira et al. (2009) showed that this electrode configuration combined with standard three-phase electric power is very efficient compared with other configurations. Another advantage of this repeated-triad configuration is efficient oil displacement in the chemical EOR phase once fluid injection is started. After electrical heating has created sufficient fluid injectivity, high-rate/high-pressure hot-water injection accelerates the increase in temperature of the reservoir and oil production simultaneously starts at the producers. The water injection extracts energy from the hot sand near the wells and transports it deep into the reservoir and also displaces oil toward the producers. The injection rate must be high to quickly increase the reservoir temperature, a principle demonstrated in our own optimization studies as well as those performed by Zhao and Gates (2013) for a similar case. Simultaneous production avoids excessive pressures and helps with the project economics. Achieving relatively low bottomhole pressures in production wells has also been shown to improve oil recovery (Rangel-German et al. 2004). Viscosity reduction, oil expansion, and unstable oil displacement by water are the recovery mechanisms, with potential recovery factors in the range of 5–25% of original oil in place. Because most of the oil recovery comes before water breakthrough—evident from flattened oil recovery curves upon water breakthrough, as shown in Luo and Torabi (2013) and Levitt et al. (2011)—further waterflooding produces very little oil. Thus, the duration of this stage is kept at less than 2 pore volumes to avoid hot water circulating through the reservoir without producing much oil. At the end of hot waterflooding, the oil viscosities are low enough for a chemical flood to be performed where oil can efficiently be mobilized and displaced at low pressure gradients. The chemical flood can be tailored to either combine mobility control and low interfacial tension (IFT) as the recovery mechanisms or promote only one of the two. We investigated alkali/cosolvent/ polymer (ACP) flooding, which combines both recovery mechanisms without the need for injecting a surfactant. Polymer provides mobility control, and alkali reacts with acids in the crude oil to form soap, which reduces the IFT. Cosolvent is used to optimize the phase behavior and prevent the formation of highly viscous emulsions. It should be noted that for some heavy oils, a perfect mobility control may not be achievable or even necessary, as experimental work has suggested (Levitt et al. 2011; Skauge et al. 2012), and a degree of mobility control would be practical. Fortenberry (2013) evaluated the ACP-flooding process in a series of core floods at moderately elevated temperatures by use of heavy oils in the range of 5,000–300,000 cp at 22 C. The ACP

Rock and Fluid Properties The success or failure of any thermal-recovery process is governed by energy transport, because oil viscosity must be reduced for any fluid flow to occur. In this regard, not only average reservoir temperature but also temperature distribution is important. Therefore, special consideration was paid to thermal and electrical properties used in the simulation model. Thermal Properties. Energy transports through both fluids and solids. Therefore, for a fluid-saturated porous medium, the heat capacity and thermal conductivity are averaged over the solid and fluids. Heat Capacity. The average heat capacity, Cv , is averaged as Cv ¼ ð1 /ÞCvr þ /

nl X

Sl Cvl ; . . . . . . . . . . . . . . . . . ð1Þ

l¼1

where / is the porosity and Cv is the volumetric heat capacity in Btu/ft3- F, subscripts r and l refer to rock- and fluid-phase number; S is saturation; and nl is the number of phases. Heat capacities of water, oil, and reservoir rock are 62.4, 30.2, and 35.3, respectively. Base- and caprock heat capacity are both 31:8. Heat capacity of the casing is 50:0, with a temperature-dependency coefficient of 0.0235 (1/ F) (Dobrosavljevic´ and Maglic´ 1992; Davis 1994). Heat Conductivity. Unlike heat capacity, the averaging of thermal conductivity in a porous medium is not straightforward. Three common approaches are volumetric averaging, kb ¼ ð1 /Þkr þ /

nl X

Sl kl ; . . . . . . . . . . . . . . . . . . . ð2Þ

l¼1

geometric mean, kb ¼ k/f kðr1/Þ ; . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ð3Þ and the empirical correlation of Anand et al. (1973), b kr kr kb ¼ kf ; ; b ¼ 0:28 0:757log/ 0:057log kf kf ð4Þ X where k is conductivity and kf ¼ S k (assuming no gas is l l l present). Using thermal conductivities of krquartz ¼ 108:0 (Horai 1971), kwater ¼ 8:9, and koil ¼ 1:8 in Btu/ft-D- F, with / ¼ 0:25 and Sw ¼ 0:25, Eqs. 2 through 4 result in significantly different kb values of 82, 46, and 33, respectively. To resolve the issue, comparisons were made against field measurements of approximately 34 Btu/ft-D- F (Seto and Bharatha 1991; Bachu 1993) for sandstones with similar porosities and water saturation, which agrees with the calculations from the correlation of Anand et al. (1973). Therefore, this correlation was used. The shale (base/caprock) thermal conductivity is taken to be approximately 16 Btu/ft-D- F (Bachu 1993). Electrical Conductivity. Bulk-electrical conductivity, Cb , can be obtained from resistivity logs or from Archie’s law in the case of clean sands by 1 aRw Rb ¼ m n ; . . . . . . . . . . . . . . . . . . . . . . . . . . ð5Þ Cb / Sw where Rb and Rw are the formation (bulk) and brine resistivity, respectively. For the Bluesky formation, typical standard logs are shown in Fig. 2. As indicated by the deep-resistivity log, the bulk

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1AA112408519W500 (MD) MD

0.00

GB

150.00

467

–0.15 –0.15

NCU DPSS

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Rsh~5 (Ω·m) 530

BLSK 540

Rock framework Shales

Rb~90 (Ω·m)

550

Sands Carbonate

560

GTHG SUBK

570

570

Fig. 2—Typical standard logs for the Bluesky formation (Shell Canada Ltd. 2009).

formation resistivity varies across the reservoir column with an average value of approximately 90 Xm, corresponding to Sw ave ¼ 0:25; obtained from Eq. 5 by use of a ¼ 1, m ¼ 1:8, n ¼ 2:15, and Rw ¼ 0:32 Xm for the Bluesky formation (Shell Canada Ltd. 2009). The shale (base/caprock) resistivity is often distinctively low (5 Xm here), becase of clay cation-exchange capacity and bound water. To account for temperature effect on bulk conductivity, we used the brine-conductivity variation with temperature (Sorensen and Glass 1987) as Cw@Tref ½1 þ 0:026ðT Tref Þ; if ðT 100 CÞ ; Cw ¼ Cw@Tref ½2:98 þ 0:002ðT 100Þ; if ðT > 100 CÞ ð6Þ where Tref is often 25 C and Cw@Tref is brine conductivity at Tref . Eqs. 5 and 6 give the bulk conductivity. A similar temperature dependence was used for the shale conductivity. Temperaturedependent casing conductivities in the range of ð0:1 0:2Þ 107 S/m (Dobrosavljevic´ and Maglic´ 1992; Davis 1994) were used in the simulation. The casing conductivity determines the voltage drop along the wellbore. Oil Viscosity. The oil viscosity in the simulation model is approximately 5,000 cp at reservoir temperature. Fig. 3 shows the oil-viscosity variations with temperature (Fortenberry 2013). Solution gas has shown to improve the heavy-oil recovery upon

Oil Viscosity (cp)

104

103

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100 50

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Temperature (°F) Fig. 3—Measured oil viscosity vs. temperature. The data are adapted from Fortenberry (2013). 76

heating (Rangel-German et al. 2004) and is qualitatively accounted for by considering an oil-thermal-expansion coefficient of approximately 9 10–4 1/ F. Simulation Model Realistic simulation is a necessary tool for the proper evaluation of the proposed hybrid process. To make the simulation study as realistic as possible, we incorporated relevant experimental data and pilot observations in developing the simulation model. Furthermore, the electrical-heating part of simulations was performed by use of CMG-STARS (CMG 2011), and the chemical EOR simulations were performed by use of the geochemical module of UTCHEM (Bhuyan et al. 1990; Delshad et al. 1996; Mohammadi et al. 2009), which is specifically designed for pH-sensitive processes such as alkali/cosolvent/polymer (ACP) and alkaline flooding. The simulations were verified by the overall energy balance in the CMG-STARS simulation and by coreflood results for the chemical-flooding part. The reservoir model containing approximately 5.4 million bbl of oil and 11 wells (five injectors and six producers) is shown in Fig. 1. The length of the wells was chosen to be 3,000 ft, similar to a pilot polymer flood performed in the Bluesky formation (Murphy Oil Company Ltd. 2014). The pilot’s operating injection rate of polymer solution (25–40 cp at 10 sec–1) at approximately 0.14 bbl/day-ft was used as a guideline to determine the polymer-solution injectivity in our simulations. The pilot polymer flood was operated with the unaltered oil viscosity of approximately 5,000 cp, which is intended to be reduced to values of approximately 300 cp at the start of chemical flooding in our approach; this reduction in oil viscosity can be equated to an increased polymer injectivity of approximately 2.3 bbl/day-ft. Therefore, we set the polymer-solution-injection rate to approximately 1.2 bbl/day-ft (half of the calculated value) in the simulations because more-viscous polymer solutions (approximately 100 cp at 5 sec–1) were considered here. Assuming approximately 33% injectivity loss with polymer compared with water (Delamaide 2014), the maximum hot-water-injection rate was set to 4 bbl/day-ft with a maximum injection pressure of 1,600 psi, which is less than the fracturing pressure of approximately 1,800 psi (Koci and Mohiddin 2007a). The injectors (five wells) and producers (six wells) operate at constant injection rate (with monitoring injection pressure) and constant bottomhole pressure (250 psi), respectively. The horizontal well spacing between two adjacent wells is approximately 51 m, comparable with the pilot-well spacing of 70 m (Murphy Oil Company Ltd. 2014). Originally, 167 5 33 gridblocks were used in the simulations, which was subsequently reduced to 167 1 33 to reduce the simulation time and numerical difficulties. Base and caprocks are included in February 2016 SPE Journal



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McGee and Vermeulen 2007). Another critical aspect of electrical preheating is imposing appropriate electrical-operating conditions, which will be discussed later in this paper.

Table 1—Coreflood specifications.

the model as the presence of shale modifies the electrical-current flow and constrains the flow of heat. Heat losses to base/caprocks were obtained by solving the energy balance over the entire model domain and by considering an analytical heat-loss term for infinite base/caprocks.

Effect of Heterogeneities. Heterogeneities in terms of fluid/rock distribution and properties complicate the implementation of any recovery technology. In typical heavy-oil and oil-sand reservoirs, oil viscosity not only varies across the field but also within the reservoir column. For the Bluesky formation, the compositional gradient in the reservoir column and biodegradation causes a viscosity increase with depth, which is most significant when there is a bottomwater zone (Larter et al. 2008). In this case, the oil viscosity of approximately 5,000 cp sharply increases near the bottom of the reservoir to approximately 2 105 cp at 22 C (Larter

104

104

103

103 η (cp)

η (cp)

Electrical Preheating. Electromagnetic heating can be divided into low-frequency resistive heating and high-frequency dielectric heating (Sahni et al. 2000; Chhetri and Islam 2008). In low-frequency resistive heating (Pizarro and Trevisan 1990; Sierra et al. 2001; Hiebert et al. 1983), the flow of an alternating current through the reservoir brine dissipates (ohmic) heat and raises the reservoir temperature, whereas in high-frequency heating, the adaptive alignment of dipoles to the alternating electric field dissipates heat (Sahni et al. 2000; Mutyala et al. 2010). We investigated the use of low-frequency electrical-resistive heating, where horizontal wells serve as both electrodes and injectors/producers (McGee and Vermeulen 1996). In the simulation model, electrical power was applied at the gridblock faces that contain the horizontal electrodes (i.e., wellbore casing). As a result of this treatment, the simulation of electrical heating can be sensitive to gridblock sizes. For example, as noted by Das (2008), the amount of ohmicheat dissipation can decrease by increasing gridblock sizes. The key to avoiding possible unphysical results is to ensure that the energy balance is respected as the process unfolds. This was verified in the present simulation study by appropriate choice of grid sizes. Besides electrode configuration (repeated triad here), electrode spacing (approximately 51 m here) and length also require careful engineering design for efficient reservoir preheating and must be tailored to the specific application (McGee et al. 1999;

Chemical Flooding. To develop the second part of the simulation model, we used the experimental data of Fortenberry (2013) for a series of heavy-oil corefloods operated at different temperatures. The performance of a heavy-oil coreflood, among other things, depends on the mobility control and the experimental temperature. The role of temperature is significant because it changes the oil viscosity and modifies the phase behavior and interactions with chemicals. Therefore, the corefloods were chosen to be at different temperatures so the simulation modeling would not merely be a mobility-control exercise. The corefloods were performed by use of a heavy oil (12 API) from Alberta, Canada, in Bentheimer cores with brine permeabilities of approximately 2.5 darcies and porosities of approximately 0.23. The cores were first waterflooded to a remaining oil saturation of approximately 0:5 and then flooded with chemicals in tertiary-recovery mode. Table 1 summarizes the corefloods’ specifications. ACP-1 and ACP-2 are alkali/cosolvent/ polymer (ACP) floods, whereas ALK is an alkaline flood with no mobility control and no cosolvent. Experiment ACP-1 was performed at 50 C, at which the oil viscosity is approximately 1,000 cp. The coreflood was designed to have a mobility ratio of unity. Experiment ACP-2 was similar to ACP-1, but it was performed at a lower temperature where the oil viscosity is approximately 5,000 cp. The third coreflood, ALK, was at an elevated temperature and hence lower oil viscosity (lo ¼ 220 cp), but with no mobility control and a mobility ratio of 20. Fig. 4 shows the measured and computed viscosities of slug and polymer solutions, respectively, containing 4,100 and 3,700 ppm of 3630S hydrolyzed polyacrylamide polymer. Fig. 5 summarizes the simulation results. Good mobility control and efficient chemicals resulted in a stable displacement and high tertiary oil recovery for ACP-1 coreflood. Unlike in ACP-1, the displacement is not stable in ACP-2 and ALK corefloods, which resulted in slower oil-recovery rates. The pressure drop and pH of the ACP-1 coreflood are shown in Fig. 6 as examples. Table 2 compares the experimental data and simulation results for the three corefloods. More simulation details can be found in Tagavifar (2014).

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100 ACP-1 (MR = 1) ACP-2 (MR = 5) ALK (MR = 20) UTCHEM

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Fig. 5—Comparison of experimental (points) vs. simulation (lines) results for (a) oil recovery and (b) oil cut. The experimental results are adapted from Fortenberry (2013). ACP-1 and ACP-2 are ACP flooding and ALK is an alkaline flooding. MR 5 mobility ratio.

14 Δp pH UTCHEM

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Pore Volume Fig. 6—Simulated pressure drop and pH for ACP-1 vs. the experimental results of Fortenberry (2013).

et al. 2008). However, this viscosity difference with depth decreases to values of approximately 500–1,000 cp with a moderate increase of temperature to 52 C (Koci and Mohiddin 2007b). A viscosity difference of this order is not expected to greatly alter the performance of the proposed hybrid process with mobility control. Another common type of heterogeneity is the variation of fluid saturations in the reservoir column and presence of bottomwater zone. Experimental work of Oskouei et al. (2012) showed that increasing the initial water saturation lowers the recovery and energy efficiency of steam-assisted gravity drainage, and several researchers have shown the adverse effects of bottomwater (Sugianto and Butler 1990; Masih et al. 2012; Chang et al. 1992). To investigate how the water/oil distribution alters the electricalheating performance, the deep-resistivity-log measurements (Fig. 2) along with Eq. 5 were used to compute the initial water saturation across the reservoir column. The water saturation increases with depth, as is typical, and averages to approximately 0.25. Three scenarios were defined in which

• Water saturation is fixed and equal to 0.25. • Log-derived water saturation with an average of 0.25 is used, and electrical heating is performed in constant-power mode. • Log-derived water saturation with an average of 0.25 is used, and electrical heating is performed in constant-current mode. Fig. 7 shows the simulated x–z vertical cross-sectional temperature distributions for the three scenarios after a fixed amount of energy is injected. For the uniform Sw case (Fig. 7a), the symmetry in heating pattern is evident where temperature forms peaks around the electrodes (11 electrodes correspond to 11 peaks) and smear out toward the middle of the reservoir. Nonuniform-water saturation inherently changes the temperature distribution (Fig. 7b) because of uneven-power distribution among the electrodes, which is common with typical constant-voltage implementation of electrical-resistive heating. A remedy for imbalanced heating with nonuniform fluid distribution can be found in the efficient electricalresistive-heating design proposed by McGee and Vermeulen (2007) and McGee (2008), in which uniform electrical-power distribution between the electrodes is achieved through voltage phase and magnitude regulations. In other words, imposing proper electrical-operating conditions (EOCs) could resolve the imbalanced heating by controlling the amount of energy that is emitted from each electrode/wellbore. Unlike the power EOC, which provides little control over the heating process and normally results in imbalanced heating, the current EOC promotes a uniform power distribution among electrodes. Once the current EOC was applied, the heating pattern (Fig. 7c) became similar to the uniform-Sw case. For the severe case of bottomwater presence, however, changing EOCs will not resolve the uneven heating and the bottom-row electrodes/wellbores should be elevated in the reservoir as much as possible away from the bottomwater. Fig. 8 shows the effect of EOCs on uniform heating for the cases shown in Fig. 7 and when the bottomwater is present. The standard deviation of temperature profile, as a measure of uniform heating, was calculated by " #1=2 N 1 X 2 ðxi x Þ ; . . . . . . . . . . . . . . . . . . ð7Þ s¼ N 1 i1

Table 2—Comparison of coreflood performances and simulation results (numbers in parentheses). 78


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350 260 170 80 10 20 z (ft)

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Fig. 7—The x–z temperature profiles at the end of electrical heating for (a) uniform water saturation, (b) variable resistivity-logderived water saturation with Sw_ave50.25 and constant power EOC, and (c) variable resistivity-log-derived water saturation with Sw_ave50.25 and constant current EOC. Initial reservoir temperature is 80 º F, and same amount of energy is injected for all the cases. Depth increases with z (ft).

Standard Deviation (°F)

where x is the temperature with a mean value of x. As seen in Fig. 8, uneven-heating pattern with variable-water saturation can be resolved by controlling the EOC. With the presence of bottomwater, however, the disturbance cannot be resolved and reservoir heating is uneven regardless of EOC. These results also show that for a homogeneous reservoir with uniform-fluid distributions, the choice of EOC is irrelevant. Nonetheless, for field practices, imposing and maintaining an appropriate EOC is crucial and challenging. An ongoing pilot trial combining electrical-resistive heating and water injection (E-T Energy Ltd. 2013) is a step forward in this direction. It is worth mentioning that the salinity of reservoir brine is assumed to be uniform here, but its variation may

60

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e bl ia

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r Va

r Va

Sw

Fig. 8—Effect of initial water saturation and EOCs on the uniformity of the heating pattern. Variable-water saturation disturbs the balance of heating pattern, which can be resolved by controlling EOCs. With the presence of bottomwater, however, the disturbance cannot be resolved.

affect the developing temperature profile with electrical-resistive heating (Bogdanov et al. 2010). The third type of common heterogeneity is the presence of horizontal shale baffles in the reservoir. For steam-injection processes, they hinder the steam rise in the formation and reduce the recovery efficiency (Shin and Choe 2009; Yang and Butler 1992). For electrical heating, however, the presence of shale baffles encourages uniform heating, as shown by Glandt and Chia-Fu (1992), and does not affect the recovery greatly. Unlike electrical heating, hot-waterflooding performance is very sensitive to horizontal-shale-baffle presence, which can be simulated in a simplified approach by use of an effective vertical-to-horizontal permeability ratio. To demonstrate this sensitivity, a two-layer model of the Bluesky formation was considered with a logderived fluid distribution and a bottomwater zone. The permeability of the upper half was set at 400 md and the bottom half as 1,000 md. A series of simulations was performed in which electrical heating for 360 days was followed by hot waterflooding. Injection-water temperature was 212 F. As seen in Fig. 9a, the default well configuration is to have injectors near the bottom of the reservoir and producers near the top, which results in a vertical-upward displacement. Fig. 9b shows the average reservoir temperature vs. time for this well configuration at different vertical-to-horizontal permeability ratios, kv =kh . The upper and lower limits of kv =kh correspond, respectively, to the core-derived and history-matched values for the Bluesky formation (Koci and Mohiddin 2007a). Although the electrical heating appeared to be less dependent on kv =kh , the waterflood performed poorly in raising reservoir temperature for very low kv =kh values becausethe injectivity is severely decreased. In fact, for kv =kh ¼ 0:01, injectivity is essentially zero and the reservoir cools down during waterflooding because of heat losses to the overburdened and underburdened shales. The limited injectivity occurs because of reaching the maximum injection pressure, which is 200 psi less than the fracturing pressure of approximately 1,800 psi. In the case of severe blockage of vertical-fluid flow by shale baffles (similar to kv =kh 0:01), repositioning of injectors/producers


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210 kv /kh decreases

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(a) Positioning injectors at the bottom and producers at the top of formation results in a vertical flood during waterflooding. The arrows schematically show the direction and magnitude of flow. Waterflood becomes inefficient in the case of very low effective vertical permeability (right graph).

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Horizontal Flood

Injector

(c) Repositioning injectors and producers as above results in a predominantly horizontal flow which recovers the performance of the waterflooding at very low effective vertical permeabilities (right graph). The arrows schematically show the direction and magnitude of flow.

Tave (°F)

180 Vertical Flood

150

110 kv /kh = 0.01

80

0

200

(d)

400

600

800

Time (days)

Fig. 9—Effect of vertical-to-horizontal permeability ratio on the heating performance where electrical heating for 360 days is followed by a hot waterflood (T 5 212 º F). Although the electrical-heating performance is not greatly altered by the permeability heterogeneities, that of waterflooding is strongly affected (a, b). Switching to a horizontal-flow pattern recovers the efficacy of the process, as for example shown for the kv =kh 50.01 case (c, d).

is required to achieve a horizontal flood. Fig. 9c shows such a reposition, which both generates a predominantly horizontal-fluid flow and preserves the triad symmetry required for the electrical heating. Fig. 9d shows the average reservoir temperature associated with the new well configuration for the case with kv =kh ¼ 0:01. For a horizontal flow, maximum vertical-well spacing is not essential for oil recovery as in the vertical flood, and therefore the wells were moved toward the middle of the formation with the new well configuration. This resulted in a slightly better performance of electrical heating in comparison with the Fig. 9a well configuration. More importantly, the horizontal flood resulted in a significant improvement of the performance of the waterflood. These results suggest that the wellbore/ electrode pattern used here provides robustness that is essential for field applications. Process Optimization for Efficient Reservoir Heating and Oil Recovery Hot waterflooding with/without electrical preheating and chemical enhanced oil recovery have been separately investigated in the past. However, to identify the best combined-operation strategy and to find out whether this strategy is economically feasible, an optimization study was performed. The goal of the optimization was to provide guidelines for practical design of the process, rather than maximizing its economic profit. Therefore, a simplified economic model was used here, neglecting costs of surface facilities and the royalties and taxes that may vary from place to place and time to time. The optimization was performed to maximize the net present value (NPV) (the objective function) by adjusting design parameters (Gill et al. 1981) that dictate the process performance. Once identified, proper constraints/dependency 80

should be imposed on these design parameters. For the problem at hand, the design parameters were selected to be method of heating, duration of the heating, alkali/cosolvent/polymer (ACP) slug size and composition, and polymer-drive size and polymer concentration. Table 3 summarizes the design parameters and relevant constraints. The NPV is calculated as NPV ¼

n X

Ci

i¼1

ð1 þ r Þi

C0 ; . . . . . . . . . . . . . . . . . . . . . ð8Þ

where Ci is cash flow at the time of i, r is the discount rate, and C0 is the capital cost. The cash flow is obtained by Operational þ Electrical power : C ¼ Revenue þ Heating water þ Chemicals |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} Costs

ð9Þ An annual discount rate of 10% and oil price of USD 75/bbl was assumed. The unit prices used for cost calculations are shown in Table 4. To obtain operational expenses, it was assumed that cost of handling injected/produced water is USD 0.3/bbl and injection-water temperature is 212 F throughout the process. A final remark is that although the duration of electrical preheating could be up to 6 months, as in Table 3, preheating will be stopped early in case of connate-water vaporization. After setting the objective function and design parameters, a pattern-search algorithm (The Mathworks Inc. 2010) was used to search for the design parameters that would yield the maximum efficiency. The search algorithm uses a set of vectors ðvk Þ ( i.e., February 2016 SPE Journal



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Table 3—Design parameters for optimization.

Table 4—Unit prices for cost calculations.

pattern) to determine which set of points (i.e., mesh) to search at each iteration. Once mesh points are set, the objective function is evaluated over the mesh to try to find a point that yields a lower objective-function value than the incumbent (Audet and Dennis 2004). After finding the local minimum, the search continues to find a possible candidate over the entire pattern that gives a lower

Field/coreflood data

value for the objective function. Once a candidate is found, the search process is reiterated. The overall optimization involves a sequence invoking CMG-STARS (CMG 2011) and UTCHEM, as shown in Fig. 10. Because the optimization process requires robust and representative simulations, the adequacy of the simulation model was established by simulating a series of ACP floods by use of the well patterns and reservoir model shown in Fig. 1. The ACP floods were conducted after the average reservoir temperature was raised to approximately 150 F, corresponding to an average oil viscosity of approximately 280 cp, by electrical preheating and hot waterflooding. The only difference of the ACP floods is the polymer concentration in the slug/polymer drive, which resulted in different degrees of mobility control. Fig. 11 shows the performance of these ACP floods. An equivalent endpoint-mobility ratio was calculated for each flood by use of the average value of oil viscosity at the start of chemical flood. The oil viscosity is,

Initialize model and guess x0

Execute CMG-STARS

Electrical-heating waterflooding

Read p, T, St and initialize grid

Execute UTCHEM

Iterate x and update model

Chemical flood

Read performance and calculate NPV Pattern Search

No

Is x optimum?

Yes

Report x and NPV

Fig. 10—Optimization work flow in which the pattern-search algorithm invokes CMG-STARS and UTCHEM. February 2016 SPE Journal ID: jaganm Time: 21:19 I Path: S:/J###/Vol00000/150028/Comp/APPFile/SA-J###150028

81


~0.1

Oil Recovery (%)

80 60

0 ~1 MR eq

40

0 ~100 MR eq

20 0 0

0.25

0.5

0.75

1

1.25

1.5

Pore Volume Fig. 11—Field-scale simulation of chemical-flood performance after reservoir temperature is raised to 150 º F corresponding to average lo of approximately 280 cp. Equivalent-endpointmobility ratios shown in Fig. 11 are obtained by varying the amount of polymer used.

however, nonuniform, ranging from 50 cp in the vicinity of wellbore to 5,000 cp at the reservoir edges. The simulated oil recoveries, as expected, are lower than the coreflood values shown in Fig. 5. For example, for the case of alkaline flooding (corresponding to a mobility ratio MR0eq 100 in Fig. 11), the oil recovery is only approximately 20% in the simulated field case compared with approximately 46% in the coreflood. Optimization Results. The design-parameter set is organized and normalized as follows (refer to Table 3 for parameter notations and bounds l and u): xl ; ul ð10Þ

x ¼ fts ; ca ; cc ; cp ; tpd ; cpd ; q; te ; Qw ; twf g; xN ¼

x108

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where xN represents the normalized set. The optimization iteration summary is shown in Fig. 12 in terms of objective function (i.e., NPV) and the normalized-optimum-design-parameter set. The optimum NPV is approximately USD 238 million, which corresponds to a cost of approximately USD 13/bbl of produced oil. The oil recoveries computed during the optimization and the cost breakdown vs. the number of simulations (invoked by the optimization algorithm) are shown in Figs. 13a and 13b, respectively. The cost does not include the capital costs (Eq. 9), which were not considered in the optimization because the number of wells was fixed. If changed, the drilling/completion costs should be a part of the optimization. Reliable data were not available, but by use of a rough estimate of USD 2.5 million per well (Burrowes et al. 2011), the total cost increases to approximately USD 23/bbl of produced oil. Analyzing the cost data reveals that well expenses dominate the spending. Because of smaller interwell spacing compared with processes such as steam-assisted gravity drainage, the well costs are greater and so is the recovery. However, the number of wells is an optimization problem itself and will be investigated in a future study. Analysis of the optimum design set (Fig. 12) shows the results are in line with the recovery scheme presented previously. Short electrical heating and high-rate waterflooding are evident from the optimum parameter set. The optimum slug and polymer drive sizes were approximately 0.25 pore volumes (PV) and approximately 1 PV, respectively. A convenient verification of the optimum solution could be obtained by analyzing the chemical-slug composition. Intuitively, one knows that the optimum recovery should be achieved when the chemical flood is performed at the optimum salinity, because too-low alkali concentration fails to reduce interfacial tension and too-high alkali concentration increases the slug costs. The optimum case has an alkali concentration of 14,000 ppm, which is in the experimental range of optimum salinity at the slug temperature and cosolvent concentration of 1 wt%, from Fortenberry et al. (2013). The polymer concentrations in the slug and the polymer drive are approximately 3,000 ppm and approximately 3,500 ppm, respectively. Even though polymer injectivity has not been an issue in heavy-oil (viscosities of less than 5,000 cp) pilot trials (Delamaide 2014),

100 0 MR eq

Stage:

Best Function Value: –238080936

–NPV

0

–1

–2

–3 0

10

20

30

60

40 50 Iteration

70

80

90

Current Best Point Current Best Point

1 0.8 0.6 0.4 0.2 0 1

2

3

4

5

6

7

8

9

10

Number of Variables (10) Fig. 12—Optimization-iteration summary in terms of best before-tax discounted NPV (top) and the optimum normalized-designparameter set (bottom). The optimization took 87 iterations. 82



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35

90

Electrical power Heating water Operational Chemicals Total

30 25

70

Cost ($/bbl)

Oil Recovery (%)

80

60 50 40

20 15

30

10

20 5

10 0 200

0

400 600 800 Time (days)

(a)

1000

0 0 (b)

1200

100

200

300

400

500

600

700

Simulation-Run Number

Fig. 13—(a) Oil recoveries computed during the optimization process. The optimum solution is shown in black. (b) Cost breakdown vs. the number of simulations invoked by the optimization algorithm. The optimum solution corresponds to the last simulation.

115

50

101

30

94

20 10 200

400

600

800

1000

z (ft)

z (ft)

40

0.9

50

108

40

0.675

30

0.45

20

87 80

0.225

10 200

400

x (ft) 212

40

179

30

146

20

113

10

800

1000 0.9

50 z (ft)

z (ft)

50

600 x (ft)

40

0.675

30

0.45

20

0.225

10 200

400

600

800

1000

80

x (ft)

200

400

600 x (ft)

800

1000

Fig. 14—Temperature profile at the end of electrical heating (left) and waterflooding (right) for the optimum solution.

Fig. 15—Oil saturation at the end of waterflood (left) and chemical flood (right) for optimum solution.

geomechanical considerations may restrict the concentration of injecting polymer to avoid excessive pressures during the flood (Khodaverdian et al. 2010). Excessive pressures were not observed with the elevated reservoir temperature and short well spacing considered here, but the need for geomechanical considerations remains valid. The optimum reservoir temperature (i.e., average reservoir temperature at the start of chemical flooding) is approximately 142 F. The temperature and oil-saturation profiles during the optimum design are shown in Figs. 14 and 15, respectively.

economic calculations (excluding cost of surface facilities and taxes) showed that heating costs are nominal in the best combined design, chemical costs are comparable with a typical polymer flood, and well expenses dominate the spending. Clearly, the cost breakdown may vary in different cases, but any implementation of the proposed hybrid process should be performed after considering the limitations and potentials of each step of the recovery process (electrical heating, hot waterflooding, and chemical EOR) and the use of an efficient well pattern that provides the flexibility to deal with reservoir heterogeneities and allows for vertical or horizontal floods as needed.

Conclusions A best-combined design for reservoir heating and oil recovery was developed combining hot waterflooding and alkali/cosolvent/ polymer (ACP) flood for a 5,000-cp-heavy-oil field. The design requires only moderate reservoir heating for a short time to lower the oil viscosity such that a moderately unstable polymer flood, hence ACP flood, is applicable. In terms of heating, hot waterflooding proved to be efficient only when high injection rates can be attained early during the flood. A short preheating period by electrical means was devised for cases with low incipient fluid injectivity, but achieving a balanced heating pattern with electrical-resistive heating may not be technically feasible in some cases. In terms of oil recovery, the potential recovery of hot waterflooding and chemical enhanced oil recovery (EOR) are 10 and 20–50% of original oil in place, respectively. Preliminary

Nomenclature ca ¼ alkali concentration, ppm cc ¼ cosolvent concentration, wt% cp ¼ polymer concentration, ppm cpd ¼ polymer drive concentration, ppm C{b,w} ¼ electrical conductivity, S/m Ci ¼ cash flow, USD Cv ¼ volumetric heat capacity, Btu/ft3- F kv/kh ¼ vertical-to-horizontal permeability ratio, fraction q ¼ electrical power, kW Qw ¼ water-injection rate, bbl/day-ft r ¼ discount rate, % R ¼ electrical resistivity, Xm


83


S ¼ fluid saturation, fraction te ¼ length of electrical heating, month tpd ¼ polymer drive size, PV ts ¼ slug size, PV twf ¼ length of waterflooding, month T ¼ temperature, F Vrms ¼ root-mean-square voltage, V c_ ¼ shear rate, s1 g ¼ viscosity, cp h ¼ voltage phase, degree k ¼ thermal conductivity, Btu/ft-day- F l ¼ Newtonian viscosity, cp / ¼ porosity, fraction

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Seto, A. C. and Bharatha, S. 1991. Thermal Conductivity Estimation From Temperature Logs. Presented at SPE International Thermal Operations Symposium, Bakersfield, California, 7–8 February. SPE-21542-MS. http://dx.doi.org/10.2118/21542-MS. Shah, A., Fishwick, R., Wood, J., et al. 2010. A Review of Novel Techniques for Heavy Oil and Bitumen Extraction and Upgrading. Energy Environ. Sci. 3 (6): 700–714. http://dx.doi.org/10.1039/B918960B. Shell Canada, Ltd. 2009. Application for Approval of the Carmon Creek Project. Volume I: Project Description, November 2009. Calgary, Alberta, Canada: Shell Canada, Ltd. Shin, H. and Choe, J. 2009. Shale Barrier Effects on the SAGD Performance. Presented at SPE/EAGE Reservoir Characterization & Simulation Conference, Abu Dhabi, UAE, 19–21 October. SPE-125211-MS. http://dx.doi.org/10.2118/125211-MS. Sierra, R., Tripathy, B., Bridges, J. E., et al. 2001. Promising Progress in Field Application of Reservoir Electrical Heating Methods. Presented at SPE International Thermal Operations and Heavy Oil Symposium, Porlamar, Margarita Island, Venezuela, 12–14 March. SPE-69709-MS. http://dx.doi.org/10.2118/69709-MS. Skauge, A., Ormehaug, P. A. and Gurhoh, T. 2012. 2-D Visualisation of Unstable Waterflood and Polymer Flood for Displacement of Heavy Oil. Presented at SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, 14–18 April. SPE-154292-MS. http://dx.doi.org/10.2118/ 154292-MS. Sorensen, J. A. and Glass, G. E. 1987. Ion and Temperature Dependence of Electrical Conductance for Natural Waters. Anal. Chem. 59 (13): 1594–1597. http://dx.doi.org/10.1021/ac00140a003. Sugianto, S. and Butler, R. 1990. The Production Of Conventional Heavy Oil Reservoirs With Bottom Water Using Steam-Assisted Gravity Drainage. J Can Pet Technol 29 (2): 78–86. PETSOC-90-02-03. http:// dx.doi.org/10.2118/90-02-03. Tagavifar, M. 2014. Enhanced Heavy Oil Recovery by Hybrid ThermalChemical Processes. PhD dissertation, University of Texas at Austin, Austin, Texas (May 2014). The Mathworks, Inc. 2010. Genetic Algorithm and Pattern SearchToolbox, User’s Manual, Version 2. Natick, Massachusetts: The Mathworks, Inc. Yang, G. and Butler, R. M. 1992. Effects Of Reservoir Heterogeneities On Heavy Oil Recovery By Steam-Assisted Gravity Drainage. J Can Pet Technol 31 (8): 37–43. PETSOC-92-08-03. http://dx.doi.org/10.2118/ 92-08-03. Zhao, D., and Gates, I. 2013. Stochastic Optimization of Hot Water Flooding Strategy in Thin Heavy Oil Reservoirs. Presented at SPE Heavy Oil Conference Canada, Calgary, Alberta, Canada, 11–13 June. SPE165541-MS. http://dx.doi.org/10.2118/165541-MS. Zhong, L., Yu, D., Yang, H., et al. 2011. Feasibility Study on Producing Heavy Oil by Gas and Electrical Heating Assisted Gravity Drainage. Presented at Offshore Technology Conference, Houston, Texas, 2–5 May. OTC-21649-MS. http://dx.doi.org/10.4043/21649-MS. Zhu, Z. and Zeng, F. 2012. Evaluation of the Hybrid Process of Electrical Resistive Heating and Solvent Injection Through Numerical Simulations. Presented at SPE Heavy Oil Conference Canada, Calgary, Alberta, Canada, 12–14 June. SPE-157037-MS. http://dx.doi.org/ 10.2118/157037-MS. Mohsen Tagavifar is a post-doctoral fellow at the Center for Petroleum and Geosystems Engineering at the University of Texas at Austin. His research interests include enhanced oil recovery (EOR), reservoir simulation, soft-matter physics, and rheology of complex fluids. Tagavifar holds a PhD degree in petroleum engineering from the University of Texas at Austin. Robert Fortenberry is currently a reservoir engineer at Ultimate EOR Services. His areas of interest include heavy-oil EOR, reservoir simulation of chemical-recovery processes, and reservoir geology. Fortenberry is an SPE member. He holds a bachelor’s degree in chemical engineering from Montana State University, Bozeman, and a master’s degree from the University of Texas in Austin in petroleum engineering with a focus on chemical-EOR-technology development. Eric de Rouffignac has been with Shell since 1981, with expertise in reservoir engineering and thermal physics. His core work has been the generation, testing, and piloting of novel subsurface EOR technologies and their integration into ongoing


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operations in the US, Canada, Africa, and the Middle East. De Rouffignac has been a project leader in those areas for Shell Oil Company in the US. He is one of the principal inventors and developers of the in-situ upgrading process and the in-situ conversion process pioneered by Shell in the thermal arena. In 2002, de Rouffignac transferred to the Difficult Hydrocarbon Team in Rijswijk, The Netherlands, to lead a group working on novel processes for heavy-oil production. He was a research adviser and subject-matter expert for thermal recovery for Shell until his retirement in 2010. De Rouffignac is the author of more than 100 patents and more than 20 publications. He now resides in Austin, Texas, and continues to work as an oil and energy consultant. De Rouffignac lectures on EOR internationally, teaches thermal recovery to graduate students at the University of Texas at Austin, and is a research consultant at the same institution. He holds a PhD degree in physics (solid state) from the University of Texas at Austin. Kamy Sepehrnoori is a professor in the Department of Petroleum and Geosystems Engineering at the University of Texas at Austin, where he holds the W.A. (Monty) Moncrief Centennial Chair in Petroleum Engineering and is the director of the Reservoir Simulation Joint Industry Project in the Center for Petroleum and Geosystems Engineering. His research interests and teaching subjects include computational methods, reservoir

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simulation, parallel computing, EOR modeling, naturally fractured reservoirs, and unconventional resources. Sepehrnoori holds bachelor’s, master’s, and PhD degrees from the University of Texas at Austin. Gary A. Pope is the Director of the Center for Petroleum and Geosystems Engineering at the University of Texas at Austin, where he has taught since 1977. He holds the Texaco Centennial Chair in Petroleum Engineering. Previously, Pope worked in production research at Shell Development Company for 5 years. Pope’s teaching and research are in the areas of EOR; reservoir engineering; natural-gas engineering; reservoir simulation; characterization of reservoirs and aquifers with tracers, surfactants, and water-soluble polymers; phase behavior and fluid properties; and groundwater modeling and remediation. He has authored or coauthored more than 210 technical papers on these topics, and has supervised the research of more than 115 graduate students at the University of Texas at Austin. Pope was elected to the National Academy of Engineering in 1999 for his contributions to understanding multiphase flow and transport in porous media and applications of these principles to improved oil recovery and aquifer remediation. Pope holds a PhD degree from Rice University and a bachelor’s degree from Oklahoma State University, both in chemical engineering.
