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Optimized solvent-aided steam-flooding strategy for recovery of thin heavy oil reservoirs
Fuel 112 (2013) 50–59
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Optimized solvent-aided steam-flooding strategy for recovery of thin heavy oil reservoirs David W. Zhao, Jacky Wang, Ian D. Gates ⇑ Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Canada
h i g h l i g h t s In Western Canada, 80% of heavy oil resources are in reservoirs <5 m thick. Current commercial thermal techniques (CSS/SAGD) cannot be used in thin reservoirs. Cold production has very low recovery factor, <10%, for thin reservoirs. Hybrid steam–solvent flooding processes have merit for thin heavy oil reservoirs. Enhanced solvent mixing occurs in thin heavy oil reservoirs with bottom water zone.
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Article history: Received 21 February 2013 Received in revised form 3 May 2013 Accepted 3 May 2013 Available online 25 May 2013 Keywords: Thin heavy oil reservoirs Steam-assisted recovery Solvent-assisted recovery Energy to oil ratio Optimization
a b s t r a c t Stochastic optimization based on a simulated annealing method was carried out to determine the optimum steam and steam–solvent flooding strategies in a thin (4 m) heavy oil reservoir both in the absence and presence of a bottom water zone. The steam injection pressure optimization case determined a technically feasible operating strategy. However, the cumulative energy to produced oil ratio (cEOR) realized from the optimized process is high. In comparison, the solvent-aided steam optimization case achieved an operating strategy with a much lower cEOR and cumulative water-to-oil ratio (cWOR) than those in the optimized injection pressure-only strategy. We observed that a solvent-rich channel forms at the top of the reservoir after solvent breakthrough occurs at the production well. The formation of the solvent-rich channel led to oil–solvent mixing at the periphery of the channel as well as heat transfer to oil beyond the channel, which in turn resulted in better recovery performance. In the presence of a bottom water zone, the optimized steam injection pressure optimization strategy was found to perform poorly. However, the optimized solvent-aided strategy achieved superior performance. With solvent injection, the presence of the bottom water zone enhanced mixing of solvent and oil yielding improved oil recovery performance. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction In Western Canada, about 80% of heavy oil resources are found in reservoirs less than 5 m thick [1]. Although currently commercial thermal-based techniques such as Steam-Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) are successful for recovering bitumen and heavy oil from thick pay zones (>15 m), their application in thin heavy oil (<6 m) reservoirs are generally not thought to be economically viable. This is due to the high steam-to-oil ratio (SOR) caused by significant heat losses to the overburden relative the amount of heat delivered to the oil which renders the processes uneconomic. Heavy oil cold production (CP) employs small energy input. However, the average recovery factor
⇑ Corresponding author. Tel.: +1 (403) 220 5752; fax: +1 (403) 284 4852. E-mail address: [email protected] (I.D. Gates). 0016-2361/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2013.05.025
is typically low, usually, between 3% and 8% of the Original Oil In Place (OOIP) [1]. By employing the so called Cold Heavy Oil Production with Sand (CHOPS) technique, the recovery factor can reach as high as 15% [10]. However, the formation of wormholes during CHOPS operation creates new challenges for applying follow-up processes to recover additional oil beyond CHOPS. In conventional oil reservoirs, water flooding and polymer flooding have been the most widely used techniques for secondary recovery after the end of primary production [9,2]. For heavy oil reservoirs, however, the performance of both waterflooding and polymer strategy are poorer and commercially successful cases are mostly found for reservoirs with dead oil viscosities less than a few thousand cP [9,2,4]. Existing thermal-based recovery process studies based on laboratory experiments, field trials, or reservoir simulation of thin heavy oil (<50,000 cP) reservoirs are rare. Stalder [11] reported reservoir simulation results on the application of Cross
D.W. Zhao et al. / Fuel 112 (2013) 50–59
Steam-Assisted Gravity Drainage (XSAGD) for a bitumen reservoir with a net pay of 10 m. The results demonstrated that there are economic advantages of XSAGD over SAGD. However, despite improved performance beyond that of SAGD, he stated that it is not enonomic to apply XSAGD to thinner reservoirs. Tavallali et al. [12] reported on the results of optimizing the SAGD well configuration in a 10 m thick Lloydminster-type heavy oil reservoir with a dead oil viscosity of 5000 cP. They suggested that placing the production well offset from the injection well is the optimum well configuration for depleting the reservoir in the shortest period of time. Solvent injection based recovery methods for bitumen and heavy oil have attracted increasing attention in recent years. Gates [5] proposed a solvent-aided thermal recovery process for 8 m thick oil sands reservoir. Gates reported that the solvent-aided process led to substantially lower steam usage and net injected energy (both steam and solvent lost to the reservoir) to oil ratio compared to that with traditional SAGD. Istchenko [7] examined a cyclic solvent process as a potential technique for post-CHOPS field operation. The results suggested that the overall recovery factor could be raised by about 50%. However, to the best of our knowledge, studies to understand and design solvent-aided thermal recovery processes for heavy oil reservoirs with thickness less than 5 m have not been done before. Here, to better understand and to derive viable thermal recovery process designs for extracting oil from thin (<5 m) heavy oil reservoirs, a reservoir simulation study has been done to investigate steam-based recovery processes with and without solvent. 2. Reservoir simulation model The reservoir model, solved in a commercial thermal reservoir simulator [3], is a two-dimensional model with two horizontal wells spaced 50 m apart as used in a previous study [14]. A detailed listing of the underlying equations, finite volume discretization, and solution algorithms is described in CMG [3]. The thickness of the heavy oil interval is equal to 4 m. The models were discretized into a regular Cartesian grid with dimensions 1 m in the cross-well direction, 1000 m in the downwell direction, and 0.4 m in the vertical direction resulting in 500 grid blocks in the model. The length
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of the perforated intervals of the horizontal wells in all models is equal to 1000 m. The reservoir simulation model and fluid properties are listed in Table 1. The spatial distributions of porosity (average equal to 0.32), horizontal permeability (average equal to 3650 mD), and oil/water saturations (average oil saturation equal to 0.65), displayed in Fig. 1, were derived from core data taken from one of Devon Canada’s heavy oil fields located in eastern Alberta. The vertical-tohorizontal permeability ratio is equal to 0.8. The initial reservoir pressure and temperature are equal to 2800 kPa and 20 °C, respectively. The solution gas-to-oil ratio at original reservoir conditions is equal to 6.17 m3/m3. All reservoir simulation models were run in parallel on a 12-core personal computer; a typical simulation took roughly 10 min to execute.
3. Optimization algorithm 3.1. The simulated annealing method In this work, a simulated annealing (SA) algorithm is used for operating strategy optimization following that of Gates [5]. As a random search technique, the simulated annealing method was first proposed by Metropolis et al. [8] for atomistic simulation and has been used for reservoir simulation optimization. The optimization algorithm is wrapped around and controls the thermal reservoir simulator. Parameters for reservoir simulation are generated by the SA algorithm and then used for assembling the simulation input file. Then a simulation run based on the newly generated input file is performed by the reservoir simulator. Once the simulation is complete, a computer code is called to process the reservoir simulation output data and evaluate the performance of the simulated strategy. The evaluation results are then sent back to the optimizer for generating new parameter sets and next iteration of the optimization algorithm starts. In the optimization procedure, the SA algorithm conducts random searches that attempt to lower the value of the cost function. The SA parameters used in the present work were the same as those used in our previous work (please see [6] for more detailed description of the algorithm and parameters).
Fig. 1. Reservoir properties of the reservoir model. The injection well is on the left side of the domain whereas the production well is on the right side of domain. The spacing between the injection and production wells is equal to 50 m.
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Table 1 Reservoir simulation model and fluid properties. Property
Value
Depth to reservoir top (m) Net pay (m) Porosity Oil saturation Solution gas-to-oil ratio (m3/m3) Horizontal rock permeability kh (mD) kv/kh Effective rock compressibility (1/kPa) Rock heat capacity (kJ/m °C) Rock thermal conductivity (kJ/m day °C) Reference pressure (kPa) Reference depth (m) Initial reservoir temperature Dead oil viscosity (cP) 20 °C 40 °C 80 °C 160 °C 250 °C Water thermal conductivity (kJ/m day °C) Gas thermal conductivity (kJ/m day °C) Oil thermal conductivity (kJ/m day °C) Effective molecular diffusion coefficient of oil (m2/day) Effective molecular diffusion coefficient of solvent (m2/day) Methane K-value correlation in oil and solvent (hexane)
334 4 0.32 ± 0.02 0.65 ± 0.09 6.17 3650 ± 347 0.8 14 10 6 2600 660 2800 334 20 15,212 1884 125.4 9.66 3.09 53.5 5 11.5 4.32 10 6 4.32 10 5 Oil
K-value = (Kv1/P) exp (kv4/(T + Kv5)) Kv1 (kPa) Kv4 (°C) Kv5 (°C) Liquid phase components viscosity (cP) versus temperature curves (methane viscosities are liquid equivalent viscosity) Mixing rule for oil phase viscosity: ln lmix = xoil ln loil + xmethane ln lmethane + xsolvent ln lsolvent where x is mole fraction.
504,547 879.84
Oil–water relative permeability curves
Solvent (hexane) 1,006,200 2697.55
265.99 T (°C)
loil
lmethane
224.37
lsolvent
5 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 180 200 220 240 260 280 300
1203489 57014.11 15212.75 4937.991 1884.388 822.580 401.772 215.666 125.376 77.989 51.395 35.585 25.707 19.266 14.904 11.853 9.657 6.811 5.128 4.065 3.355 2.859 2.501 2.234
115.042 98.059 72.880 55.644 43.496 34.707 28.201 23.285 19.500 16.538 14.185 12.290 10.745 9.471 8.410 7.519 6.762 5.559 4.656 3.961 3.416 2.980 2.625 2.400
0.360 0.342 0.309 0.281 0.257 0.237 0.219 0.203 0.190 0.178 0.167 0.156 0.149 0.141 0.134 0.128 0.122 0.112 0.104 0.097 0.090 0.085 0.080 0.078
Sw 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 0.5500 0.6000 0.6500 0.7000 0.7500 0.8000 0.8500 0.9000 0.9500 1.0000
krw 0.0000 0.0002 0.0016 0.0055 0.0130 0.0254 0.0440 0.0698 0.1040 0.1480 0.2040 0.2710 0.3520 0.4470 0.5590 0.6870 0.8340 1.0000
krow 0.9920 0.9790 0.9500 0.7200 0.6000 0.4700 0.3500 0.2400 0.1650 0.1100 0.0700 0.0400 0.0150 0.0000 0.0000 0.0000 0.0000 0.0000
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D.W. Zhao et al. / Fuel 112 (2013) 50–59 Table 1 (continued) Property
Value
Gas–liquid relative permeability curves
Sl 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 0.5500 0.6000 0.6500 0.7000 0.7500 0.8000 0.8500 0.9000 0.9500 1.0000
krg 1.0000 0.9500 0.8400 0.7200 0.6000 0.4700 0.3500 0.2400 0.1650 0.0930 0.0750 0.0450 0.0270 0.0200 0.0100 0.0050 0.0000 0.0000
krog 0.0000 0.0002 0.0016 0.0055 0.0130 0.0254 0.0440 0.0698 0.1040 0.1480 0.2040 0.2710 0.3520 0.4470 0.5590 0.6870 0.8340 0.9920
Table 2 List of optimization parameters. Parameter
Fig. 2. Cost function versus iteration number as the optimization proceeds for Case 4.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection
well well well well well well well well well well well well well well well well well well well well
pressure pressure pressure pressure pressure pressure pressure pressure pressure pressure solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction
Onset time (months)
Base value, Allowed range
0 4 9 15 21 27 35 43 51 63 0 4 9 15 21 27 35 43 51 63
3600 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2
1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa
3.2. Adjustable parameters and cost function For optimization, the adjustable parameters are the injection pressures and fraction of solvent in the injection stream over specified time intervals, summarized in Table 2. In the present study, the solvent used is hexane – it is a reasonable surrogate for diluent, a solvent based on gas condensates often used in Alberta for diluting heavy oil and bitumen to meet viscosity standards for pipelines. In total, 10 pressure parameters with base value of 3000 kPa and optimization range set equal to 1200–4200 kPa, and 10 solvent volume fraction parameters with base value equal to 0.0 m3/m3 and range 0.0–0.2 m3/m3 are used to optimize the process. The cost function against which the adjustable parameters are optimized is a function of the net present value (NPV) – this includes costs for lost solvent within the reservoir for the steam-solvent processes. For the simple economic model used here, the following economic factors are considered: initial capital investment (including well drilling and field equipment), operating costs, fixed costs, variable costs, water treatment costs, and operating revenue. The following assumptions formed the basis of our evaluation: well drilling cost and other initial investment $2,500,000
(for a single well), discount rate of 10%, variable cost to be 10% of the operating revenue, heavy oil price $80.00/bbl [13], natural gas price $4.4/GJ, thermal efficiency equal to 0.75, solvent price $95.00/bbl, and waste water treatment cost is $2.00/m3. Then the cost function (CF) is formally defined as: CF = (7 106 – NPV)/1 106. This indexes the value of the CF to range, in general, between 0 and 10 with lower values of the CF being more optimal. The optimum operating strategy is chosen as the case with the lowest CF among all of the cases executed. An example of the evolution of the cost function for Case 4 (described below) versus iteration number is displayed in Fig. 2. The results demonstrate that the SA algorithm is capable of stepping out of local minima to seek out the global minimum. 4. Details of investigated cases for optimization 4.1. Case 1. Steam injection pressure optimization In this case, only steam injection pressures are taken as target for optimization. Both the injection and production well is located 0.6 m above the reservoir bottom.
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in this region). Both the injection and production well are located 1.4 m above the water zone. Only steam injection pressure is optimized (no solvent injection is used). 4.4. Case 4. Steam injection pressure and solvent fraction optimization in the presence of 2 m bottom water zone
Fig. 3. Optimized steam injection pressure strategy for the Case 1.
4.2. Case 2. Steam injection pressure and solvent fraction optimization In this case, the reservoir model and well configurations are identical to those in Case 1. The parameters for optimization are the steam injection pressure and solvent volume fraction in the injection steam. 4.3. Case 3. Steam injection pressure optimization in the presence of 2 m bottom water zone In the present case, the reservoir model is derived from Case 1 by adding a 2-m thick bottom water zone (water saturation is 1.0
In the present case, the reservoir model and well configuration are identical to that of Case 3. Both steam injection pressure and solvent volume fraction are optimized to achieve the best operating strategy. In all the above cases, the production wells are subject to two constraints: first, a bottom hole pressure of 500 kPa and a total maximum liquid production rate equal to 500 m3 per day. The total number of reservoir simulations executions that were conducted was equal to 800 in each optimization run – the optimal solution was the best one achieved among the 800 runs. The optimized strategies are further improved by adopting a two to three-month blowdown period at the point of time when maximum NPV is achieved. 5. Results and discussion 5.1. Case 1. Steam injection pressure optimization Fig. 3 shows the optimized steam injection pressure operating strategy. The optimized strategy yielded a maximum NPV after 73 months. In the best case, the pressure started at 3300 kPa and stepped up to 4200 kPa before it bottomed at 2275 kPa. Thereafter, the pressure increased to 4200 kPa and then dropped down to 3300 kPa at the end of the 73-month operating period. As shown
Table 3 Comparison of optimized operating strategies in all the four cases in terms of cumulative oil production, cumulative water produced to oil produced ratio (cWOR), cumulative energy injected to oil ratio (cEOR), operating time and net present value (NPV).
a
Case
Cumulative oil production (m3)
cWOR (m3/m3)
cEOR (GJ/m3)
Operating time (month)
NPVa ($million)
1 2 3 4
32,278 31,518 23,145 32,136
7.0 4.7 9.8 4.9
16.2 11.5 21.7 11.2
73 48 32 26
2.7 4.8 1.9 6.1
The blowdown performance is not considered in the NPV calculation which means the real NPV could be slightly higher than the presented values.
Fig. 4. Optimized steam injection pressure and solvent fraction for the Case 2.
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Fig. 5. Comparison of oil production rates and cumulative oil production of the optimized strategies of the Case 1 (pressure) and Case 2 (pressure + solvent).
equal to 7.0 m3/m3. The cumulative energy-to-produced oil ratio (cEOR) is found to be 16.2 GJ/m3, that is larger than the value of 10 GJ/m3 which is typical for SAGD. This strategy realized a NPV equal to $2.7 million, which is larger than the NPV of $1.2 million obtained from constant injection pressure equal to 3000 kPa.
5.2. Case 2. Steam injection pressure and solvent fraction optimization
Fig. 6. Comparison of cEORs of the optimized Case 1 (pressure) and Case 2 (pressure + solvent).
in Table 3, the optimized strategy achieved a cumulative oil production of 32,278 m3 (recovery factor equal to 77%) with a cWOR
Fig. 4 shows the optimized strategy employing both steam and solvent injection for this case. The optimized strategy realized a maximum NPV after 48 months. The results of this case are different from that of Case 1. Here, the pressure started with relatively low values ranging from 1500 to 2600 kPa over the first 21 months. It then increased to between 3800 and 4200 kPa over the next 14 months, which was followed by a substantial pressure reduction to 1200 kPa, which then remained for the last 13 months of its total 48-month operating time. The solvent injection strategy exhibited a cyclic pattern with relative higher value (0.14– 0.20 m3/m3) during the period of 10th to 27th month and a much lower value of 0.007 m3/m3 for its last 5 months. The optimized strategy of Case 2 realized a cumulative oil production equal to 31,518 m3 (recovery factor equal to 75%), which is similar to that in the Case 1. However, it depleted the reservoir in about 48 months, a 34% reduction in operating time. Fig. 5 shows
Fig. 7. Temperature (°C) profiles of optimized Case 2 (pressure + solvent).
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Fig. 8. Mole fraction of solvent in both vapor and oil phases of the optimized Case 2.
the oil production rates of the optimized strategies of the Cases 1 and 2. Although the optimized strategy of the Case 2 underperformed that of the Case 1 in terms of oil production rate over the first 2 years of operation, it achieved much higher oil rates over the next 2 years. It also achieved relatively low WOR (4.7 m3/m3). As shown in Fig. 6, the cEOR of the Case 2 optimized strategy is high over the first 2 years. This is due to low oil production rate during the same period while solvent injection was employed (solvent injected but not produced is considered to be energy injection cost). With high oil rates in the next 2 year, the cEOR quickly decreased and ended up with an overall value of 11.5 GJ/m3, which is better than that of Case 1 and comparable with typical SAGD performance. Therefore it realized a higher NPV of $4.8 million (versus $2.7 million for Case 1). Fig. 7 shows the temperature profiles within the reservoir after 22, 28, and 36 months or operation, respectively. The results reveal that heat transfer from injection well is relative slow during the first 2 years but quickly picked up its pace in the later stage. This is in line with the initial low steam injection rates. The improved injection in the later stage is due to breakthrough of solvent in the production after 2 years. As shown in Fig. 8, a thin layer of solvent advances at the top of reservoir and eventually forms a solvent channel. This formation of solvent channel promotes heat transfer in the direction of production well and dilutes the heavy oil in the upper part of the reservoir along its path. The increased
reservoir temperature and oil dilution results in a substantial decrease of oil viscosity, as exhibited in Fig. 9. The oil viscosity reduction gave rise to faster oil flow to production well under the flood pressure gradient. In the optimized case, the solvent recovery factor is equal 98%, that is, 98% of the injected solvent is produced. 5.3. Case 3. Steam injection pressure optimization in the presence of 2 m bottom water zone The optimized operating strategy achieved maximum NPV after 32 months of operation. The optimized steam injection pressure for this case is displayed in Fig. 10. The injection pressure started at about 2700 kPa and then increased in a stepwise manner to 3900 kPa and remained in the range from 3700 to 3900 kPa for 16 months. At the last one third of the total 32-month operating time (approximately 11 months), the injection pressure was equal to about 1200 kPa. Due to the existence of the 2 m bottom water zone, water cut was observed to be between 95.6% and 99.7% during the 1 year. Injected steam quickly lost its heat to the bottom water zone. As the operation continued, the warmed-up bottom water zone heated the oil zone and oil rate exceeded 10 m3/day after 1 year. Although the optimized strategy realized cumulative oil production equal to 23,145 m3 (recovery factor equal to 55%), it was found to have high cWOR (9.8 m3/m3) and cEOR (21.7 m3/m3) and therefore a lower
Fig. 9. Viscosity (cP) distribution of the optimized Case 2 (the grid blocks shown in white represent region with zero oil saturation).
D.W. Zhao et al. / Fuel 112 (2013) 50–59
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NPV than that achieved in Case 1. This suggested that the presence of bottom water makes it more difficult to produce oil in a steam injection operating strategy based on pressure-only control. 5.4. Case 4. Steam injection pressure and solvent fraction optimization in the presence of 2 m bottom water zone
Fig. 10. Optimized operating strategy of Case 3.
Fig. 11 shows the optimized steam injection pressure and solvent volume fraction in this case. The optimized operating strategy achieved a maximum NPV after 26 months of operation. The resulting optimized strategy had a relatively high injection pressure during the majority of the operating process with lower injection pressure towards the end of the production operation. A similar trend was also found for solvent injection except that its initial high value period is shorter than that of the high pressure interval. As shown in Fig. 12, the optimized case yielded a cumulative oil production volume equal to 32,137 m3 (recovery factor equal to 77%), a 39% increase compared to that of the optimized Case 3. With introduction of solvent, the oil production rate exceeded
Fig. 11. Optimized operating strategy of Case 4.
Fig. 12. Oil production rate and cumulative oil production of optimized Case 3 (pressure) and Case 4 (pressure + solvent).
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D.W. Zhao et al. / Fuel 112 (2013) 50–59
Fig. 13. Distribution of mole fraction of solvent in vapor and oil phases of optimized strategy of Case 4.
60 m3/day after 6 months of operation. Both the cWOR (4.9 m3/m3) and cEOR (11.2 GJ/m3) are comparable to those in the optimized results of the Case 2. In addition, due to the short operation time (26 months), it achieves the largest NPV in all the four investigated cases (see Table 3). The mechanistic factors accounting for the superior performance of the present case is as follows. First, the fluid injectivity is larger than that in the Cases 1 and 2 due to the existence of bottom water zone. The injected steam loses its enthalpy to the bottom water zone but this is recovered later as the heated water zone heats the oil zone. Second, the injected solvent travels
through the reservoir in the bottom water zone towards the production well. As shown in Fig. 13, a solvent-rich zone is found in the bottom water region. Due to its smaller density, solvent rises in the water zone and mixes with the oil. The ternary phase distribution shown in Fig. 14 suggests that the solvent front proceeds faster than the propagation of the steam chamber. The combined effects of heating the oil zone and solvent mixing both decrease the oil viscosity (as shown in Fig. 15). The decreased oil viscosity realizes relatively high oil flow and as a consequence of short breakthrough time, a shorter operating period. In addition, with a blowdown strategy, about 99% of solvent can be recovered.
Fig. 14. Ternary phase distributions of the optimized Case 4.
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Fig. 15. Viscosity (cP) distribution of the optimized Case 4 (the grid blocks shown in white represent region with zero oil saturation).
6. Conclusions Acknowledgements In the present work, stochastic optimization was conducted to determine optimum steam and steam–solvent flooding strategies in thin heavy oil reservoir in cases with and without a bottom water zone. The key observations are as follows. 1. The steam injection pressure optimization procedure realized a feasible operating strategy. However, the achieved cumulative energy injected to produced oil ratio (cEOR) is much higher than that obtained for SAGD in thicker oil sands reservoirs. 2. The steam–solvent optimization procedure achieved operating strategy with lower cEOR and WOR than those in the optimized injection pressure-only strategy. The results revealed that a solvent channel forms at the top of the reservoir after the breakthrough of solvent in the production well. The formation of the solvent channel promotes oil–solvent mixing as well as heat transfer to the oil zone which in turn results in better reservoir performance. 3. The presence of a bottom water zone was found to adversely affect the performance of the strategy achieved in steam injection pressure-based optimization case. High water-cut due to water coning leads to relatively high water-to-oil ratio and cEOR. Although the heated bottom water zone could mobilize oil located above the bottom water in a relatively short period of time, the overall economics is the worst of all the four investigated optimized cases. 4. With solvent introduction in the injected fluid, the presence of bottom water was shown to be a positive factor in promoting oil recovery. The bottom water enhanced mixing of solvent and oil due to the formation of a solvent-rich zone in the bottom water region. The optimized strategy in this case achieved the best economic performance. It is well known that the existence of bottom water worsens cold production and CHOPS performance; however, based on the results obtained in this study, we propose that solvent-aided steam-flooding should be considered as a potential operating strategy for thin (<5 m) heavy oil reservoirs, especially those with bottom water intervals. 5. The net present value calculations suggest, under present economic and reservoir model assumptions, that solvent co-injection with steam achieves improved economic performance compared to that of steam-alone operations.
Acknowledgement is extended to the Petroleum Technology Research Centre (PTRC) for their financial support and the University of Calgary for providing financial and logistical support as well as Computer Modelling Group for the use of its thermal reservoir simulator, STARS™. References [1] Adams DM. Experiences with waterflooding Lloydminster heavy-oil reservoirs. J Can Pet Technol 1982;34:1643–50. [2] Asghari K, Nakutnyy P. Experimental results of polymer flooding of heavy oil reservoirs. In: Paper 2008-189 presented at the Canadian international petroleum conference/SPE gas technology symposium 2008 joint conference (the petroleum society’s 59th annual technical meeting). Calgary, Alberta, Canada, 17–19 June; 2008. [3] Computer Modelling Group (CMG) Ltd. STARS™ Users Manual, Version 2011.10. Calgary, Alberta, Canada; 2011. [4] Gao C.-H. Advances of polymer flood in heavy oil recovery. In: Paper SPE 150384 presented at the SPE heavy oil conference and exhibition, Kuwait City, Kuwait, 12–14 December; 2011. [5] Gates ID. Solvent-aided steam-assisted gravity drainage in thin oil sand reservoirs. J Pet Sci Eng 2010;74:138–46. [6] Gates ID, Chakrabarty N. Design of the steam and solvent injection strategy in expanding solvent steam-assisted gravity drainage. J Can Pet Technol 2008;47:12–20. [7] Istchenko C. Well-wormhole model for CHOPS. M.Sc. thesis, University of Calgary; 2012. [8] Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of state calculations by fast computing machines. J Chem Phys 1953;21:1087–92. [9] Miller KA. State of the art of Western Canadian heavy oil water flood technology. In: Paper 2005-251 presented at the petroleum society’s 6th Canadian international conference (56th Annual Technical Meeting), Calgary, Alberta, Canada, June 7–9; 2005. [10] Pan Y, Chen Z, Sun J, Bao X, Xiao L, Wang R. Research progress of modelling on cold heavy oil production with sand. In: Paper SPE 133587 presented at the SPE Western Regional Meeting, Anaheim, Califonia, USA, 27–29 May; 2010. [11] Stalder JL. Unlocking bitumen in thin and/or lower pressure pay using cross SAGD (XSAGD). J Can Pet Technol 2009;48:34–9. [12] Tavallali M, Maini B, Harding T. Assessment of SAGD well configuration optimization in Lloyminster heavy oil reservoir. In: Paper SPE 153128 presented at the SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, 20–22 March; 2012. [13] Teare M, Burrowes A, Baturin-Pollock, Rokosh D, Evans C, Gigantelli D, Marsh R, Ashrafi B, Tamblyn C, Ito S, Willwerth A, Yemane M, Fong J, Kirsch M, Crowfoot C, et al. Alberta’s Energy Reserves 2011 and Supply/Demand, Outlook 2012–2021, June; 2012. [14] Zhao W, Wang J, Gates I.D. Thermally-based operating strategy and well placement for thin heavy oil reservoir. In: Presented at the 33rd IEAEOR Symposium, Regina, Canada, August 26–30; 2012.
Contents lists available at SciVerse ScienceDirect
Fuel journal homepage: www.elsevier.com/locate/fuel
Optimized solvent-aided steam-flooding strategy for recovery of thin heavy oil reservoirs David W. Zhao, Jacky Wang, Ian D. Gates ⇑ Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Canada
h i g h l i g h t s In Western Canada, 80% of heavy oil resources are in reservoirs <5 m thick. Current commercial thermal techniques (CSS/SAGD) cannot be used in thin reservoirs. Cold production has very low recovery factor, <10%, for thin reservoirs. Hybrid steam–solvent flooding processes have merit for thin heavy oil reservoirs. Enhanced solvent mixing occurs in thin heavy oil reservoirs with bottom water zone.
a r t i c l e
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Article history: Received 21 February 2013 Received in revised form 3 May 2013 Accepted 3 May 2013 Available online 25 May 2013 Keywords: Thin heavy oil reservoirs Steam-assisted recovery Solvent-assisted recovery Energy to oil ratio Optimization
a b s t r a c t Stochastic optimization based on a simulated annealing method was carried out to determine the optimum steam and steam–solvent flooding strategies in a thin (4 m) heavy oil reservoir both in the absence and presence of a bottom water zone. The steam injection pressure optimization case determined a technically feasible operating strategy. However, the cumulative energy to produced oil ratio (cEOR) realized from the optimized process is high. In comparison, the solvent-aided steam optimization case achieved an operating strategy with a much lower cEOR and cumulative water-to-oil ratio (cWOR) than those in the optimized injection pressure-only strategy. We observed that a solvent-rich channel forms at the top of the reservoir after solvent breakthrough occurs at the production well. The formation of the solvent-rich channel led to oil–solvent mixing at the periphery of the channel as well as heat transfer to oil beyond the channel, which in turn resulted in better recovery performance. In the presence of a bottom water zone, the optimized steam injection pressure optimization strategy was found to perform poorly. However, the optimized solvent-aided strategy achieved superior performance. With solvent injection, the presence of the bottom water zone enhanced mixing of solvent and oil yielding improved oil recovery performance. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction In Western Canada, about 80% of heavy oil resources are found in reservoirs less than 5 m thick [1]. Although currently commercial thermal-based techniques such as Steam-Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) are successful for recovering bitumen and heavy oil from thick pay zones (>15 m), their application in thin heavy oil (<6 m) reservoirs are generally not thought to be economically viable. This is due to the high steam-to-oil ratio (SOR) caused by significant heat losses to the overburden relative the amount of heat delivered to the oil which renders the processes uneconomic. Heavy oil cold production (CP) employs small energy input. However, the average recovery factor
⇑ Corresponding author. Tel.: +1 (403) 220 5752; fax: +1 (403) 284 4852. E-mail address: [email protected] (I.D. Gates). 0016-2361/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2013.05.025
is typically low, usually, between 3% and 8% of the Original Oil In Place (OOIP) [1]. By employing the so called Cold Heavy Oil Production with Sand (CHOPS) technique, the recovery factor can reach as high as 15% [10]. However, the formation of wormholes during CHOPS operation creates new challenges for applying follow-up processes to recover additional oil beyond CHOPS. In conventional oil reservoirs, water flooding and polymer flooding have been the most widely used techniques for secondary recovery after the end of primary production [9,2]. For heavy oil reservoirs, however, the performance of both waterflooding and polymer strategy are poorer and commercially successful cases are mostly found for reservoirs with dead oil viscosities less than a few thousand cP [9,2,4]. Existing thermal-based recovery process studies based on laboratory experiments, field trials, or reservoir simulation of thin heavy oil (<50,000 cP) reservoirs are rare. Stalder [11] reported reservoir simulation results on the application of Cross
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Steam-Assisted Gravity Drainage (XSAGD) for a bitumen reservoir with a net pay of 10 m. The results demonstrated that there are economic advantages of XSAGD over SAGD. However, despite improved performance beyond that of SAGD, he stated that it is not enonomic to apply XSAGD to thinner reservoirs. Tavallali et al. [12] reported on the results of optimizing the SAGD well configuration in a 10 m thick Lloydminster-type heavy oil reservoir with a dead oil viscosity of 5000 cP. They suggested that placing the production well offset from the injection well is the optimum well configuration for depleting the reservoir in the shortest period of time. Solvent injection based recovery methods for bitumen and heavy oil have attracted increasing attention in recent years. Gates [5] proposed a solvent-aided thermal recovery process for 8 m thick oil sands reservoir. Gates reported that the solvent-aided process led to substantially lower steam usage and net injected energy (both steam and solvent lost to the reservoir) to oil ratio compared to that with traditional SAGD. Istchenko [7] examined a cyclic solvent process as a potential technique for post-CHOPS field operation. The results suggested that the overall recovery factor could be raised by about 50%. However, to the best of our knowledge, studies to understand and design solvent-aided thermal recovery processes for heavy oil reservoirs with thickness less than 5 m have not been done before. Here, to better understand and to derive viable thermal recovery process designs for extracting oil from thin (<5 m) heavy oil reservoirs, a reservoir simulation study has been done to investigate steam-based recovery processes with and without solvent. 2. Reservoir simulation model The reservoir model, solved in a commercial thermal reservoir simulator [3], is a two-dimensional model with two horizontal wells spaced 50 m apart as used in a previous study [14]. A detailed listing of the underlying equations, finite volume discretization, and solution algorithms is described in CMG [3]. The thickness of the heavy oil interval is equal to 4 m. The models were discretized into a regular Cartesian grid with dimensions 1 m in the cross-well direction, 1000 m in the downwell direction, and 0.4 m in the vertical direction resulting in 500 grid blocks in the model. The length
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of the perforated intervals of the horizontal wells in all models is equal to 1000 m. The reservoir simulation model and fluid properties are listed in Table 1. The spatial distributions of porosity (average equal to 0.32), horizontal permeability (average equal to 3650 mD), and oil/water saturations (average oil saturation equal to 0.65), displayed in Fig. 1, were derived from core data taken from one of Devon Canada’s heavy oil fields located in eastern Alberta. The vertical-tohorizontal permeability ratio is equal to 0.8. The initial reservoir pressure and temperature are equal to 2800 kPa and 20 °C, respectively. The solution gas-to-oil ratio at original reservoir conditions is equal to 6.17 m3/m3. All reservoir simulation models were run in parallel on a 12-core personal computer; a typical simulation took roughly 10 min to execute.
3. Optimization algorithm 3.1. The simulated annealing method In this work, a simulated annealing (SA) algorithm is used for operating strategy optimization following that of Gates [5]. As a random search technique, the simulated annealing method was first proposed by Metropolis et al. [8] for atomistic simulation and has been used for reservoir simulation optimization. The optimization algorithm is wrapped around and controls the thermal reservoir simulator. Parameters for reservoir simulation are generated by the SA algorithm and then used for assembling the simulation input file. Then a simulation run based on the newly generated input file is performed by the reservoir simulator. Once the simulation is complete, a computer code is called to process the reservoir simulation output data and evaluate the performance of the simulated strategy. The evaluation results are then sent back to the optimizer for generating new parameter sets and next iteration of the optimization algorithm starts. In the optimization procedure, the SA algorithm conducts random searches that attempt to lower the value of the cost function. The SA parameters used in the present work were the same as those used in our previous work (please see [6] for more detailed description of the algorithm and parameters).
Fig. 1. Reservoir properties of the reservoir model. The injection well is on the left side of the domain whereas the production well is on the right side of domain. The spacing between the injection and production wells is equal to 50 m.
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Table 1 Reservoir simulation model and fluid properties. Property
Value
Depth to reservoir top (m) Net pay (m) Porosity Oil saturation Solution gas-to-oil ratio (m3/m3) Horizontal rock permeability kh (mD) kv/kh Effective rock compressibility (1/kPa) Rock heat capacity (kJ/m °C) Rock thermal conductivity (kJ/m day °C) Reference pressure (kPa) Reference depth (m) Initial reservoir temperature Dead oil viscosity (cP) 20 °C 40 °C 80 °C 160 °C 250 °C Water thermal conductivity (kJ/m day °C) Gas thermal conductivity (kJ/m day °C) Oil thermal conductivity (kJ/m day °C) Effective molecular diffusion coefficient of oil (m2/day) Effective molecular diffusion coefficient of solvent (m2/day) Methane K-value correlation in oil and solvent (hexane)
334 4 0.32 ± 0.02 0.65 ± 0.09 6.17 3650 ± 347 0.8 14 10 6 2600 660 2800 334 20 15,212 1884 125.4 9.66 3.09 53.5 5 11.5 4.32 10 6 4.32 10 5 Oil
K-value = (Kv1/P) exp (kv4/(T + Kv5)) Kv1 (kPa) Kv4 (°C) Kv5 (°C) Liquid phase components viscosity (cP) versus temperature curves (methane viscosities are liquid equivalent viscosity) Mixing rule for oil phase viscosity: ln lmix = xoil ln loil + xmethane ln lmethane + xsolvent ln lsolvent where x is mole fraction.
504,547 879.84
Oil–water relative permeability curves
Solvent (hexane) 1,006,200 2697.55
265.99 T (°C)
loil
lmethane
224.37
lsolvent
5 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 180 200 220 240 260 280 300
1203489 57014.11 15212.75 4937.991 1884.388 822.580 401.772 215.666 125.376 77.989 51.395 35.585 25.707 19.266 14.904 11.853 9.657 6.811 5.128 4.065 3.355 2.859 2.501 2.234
115.042 98.059 72.880 55.644 43.496 34.707 28.201 23.285 19.500 16.538 14.185 12.290 10.745 9.471 8.410 7.519 6.762 5.559 4.656 3.961 3.416 2.980 2.625 2.400
0.360 0.342 0.309 0.281 0.257 0.237 0.219 0.203 0.190 0.178 0.167 0.156 0.149 0.141 0.134 0.128 0.122 0.112 0.104 0.097 0.090 0.085 0.080 0.078
Sw 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 0.5500 0.6000 0.6500 0.7000 0.7500 0.8000 0.8500 0.9000 0.9500 1.0000
krw 0.0000 0.0002 0.0016 0.0055 0.0130 0.0254 0.0440 0.0698 0.1040 0.1480 0.2040 0.2710 0.3520 0.4470 0.5590 0.6870 0.8340 1.0000
krow 0.9920 0.9790 0.9500 0.7200 0.6000 0.4700 0.3500 0.2400 0.1650 0.1100 0.0700 0.0400 0.0150 0.0000 0.0000 0.0000 0.0000 0.0000
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D.W. Zhao et al. / Fuel 112 (2013) 50–59 Table 1 (continued) Property
Value
Gas–liquid relative permeability curves
Sl 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 0.5500 0.6000 0.6500 0.7000 0.7500 0.8000 0.8500 0.9000 0.9500 1.0000
krg 1.0000 0.9500 0.8400 0.7200 0.6000 0.4700 0.3500 0.2400 0.1650 0.0930 0.0750 0.0450 0.0270 0.0200 0.0100 0.0050 0.0000 0.0000
krog 0.0000 0.0002 0.0016 0.0055 0.0130 0.0254 0.0440 0.0698 0.1040 0.1480 0.2040 0.2710 0.3520 0.4470 0.5590 0.6870 0.8340 0.9920
Table 2 List of optimization parameters. Parameter
Fig. 2. Cost function versus iteration number as the optimization proceeds for Case 4.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection Injection
well well well well well well well well well well well well well well well well well well well well
pressure pressure pressure pressure pressure pressure pressure pressure pressure pressure solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction solvent fraction
Onset time (months)
Base value, Allowed range
0 4 9 15 21 27 35 43 51 63 0 4 9 15 21 27 35 43 51 63
3600 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 3000 kPa, 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2 0, 0–0.2
1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa 1200–4200 kPa
3.2. Adjustable parameters and cost function For optimization, the adjustable parameters are the injection pressures and fraction of solvent in the injection stream over specified time intervals, summarized in Table 2. In the present study, the solvent used is hexane – it is a reasonable surrogate for diluent, a solvent based on gas condensates often used in Alberta for diluting heavy oil and bitumen to meet viscosity standards for pipelines. In total, 10 pressure parameters with base value of 3000 kPa and optimization range set equal to 1200–4200 kPa, and 10 solvent volume fraction parameters with base value equal to 0.0 m3/m3 and range 0.0–0.2 m3/m3 are used to optimize the process. The cost function against which the adjustable parameters are optimized is a function of the net present value (NPV) – this includes costs for lost solvent within the reservoir for the steam-solvent processes. For the simple economic model used here, the following economic factors are considered: initial capital investment (including well drilling and field equipment), operating costs, fixed costs, variable costs, water treatment costs, and operating revenue. The following assumptions formed the basis of our evaluation: well drilling cost and other initial investment $2,500,000
(for a single well), discount rate of 10%, variable cost to be 10% of the operating revenue, heavy oil price $80.00/bbl [13], natural gas price $4.4/GJ, thermal efficiency equal to 0.75, solvent price $95.00/bbl, and waste water treatment cost is $2.00/m3. Then the cost function (CF) is formally defined as: CF = (7 106 – NPV)/1 106. This indexes the value of the CF to range, in general, between 0 and 10 with lower values of the CF being more optimal. The optimum operating strategy is chosen as the case with the lowest CF among all of the cases executed. An example of the evolution of the cost function for Case 4 (described below) versus iteration number is displayed in Fig. 2. The results demonstrate that the SA algorithm is capable of stepping out of local minima to seek out the global minimum. 4. Details of investigated cases for optimization 4.1. Case 1. Steam injection pressure optimization In this case, only steam injection pressures are taken as target for optimization. Both the injection and production well is located 0.6 m above the reservoir bottom.
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in this region). Both the injection and production well are located 1.4 m above the water zone. Only steam injection pressure is optimized (no solvent injection is used). 4.4. Case 4. Steam injection pressure and solvent fraction optimization in the presence of 2 m bottom water zone
Fig. 3. Optimized steam injection pressure strategy for the Case 1.
4.2. Case 2. Steam injection pressure and solvent fraction optimization In this case, the reservoir model and well configurations are identical to those in Case 1. The parameters for optimization are the steam injection pressure and solvent volume fraction in the injection steam. 4.3. Case 3. Steam injection pressure optimization in the presence of 2 m bottom water zone In the present case, the reservoir model is derived from Case 1 by adding a 2-m thick bottom water zone (water saturation is 1.0
In the present case, the reservoir model and well configuration are identical to that of Case 3. Both steam injection pressure and solvent volume fraction are optimized to achieve the best operating strategy. In all the above cases, the production wells are subject to two constraints: first, a bottom hole pressure of 500 kPa and a total maximum liquid production rate equal to 500 m3 per day. The total number of reservoir simulations executions that were conducted was equal to 800 in each optimization run – the optimal solution was the best one achieved among the 800 runs. The optimized strategies are further improved by adopting a two to three-month blowdown period at the point of time when maximum NPV is achieved. 5. Results and discussion 5.1. Case 1. Steam injection pressure optimization Fig. 3 shows the optimized steam injection pressure operating strategy. The optimized strategy yielded a maximum NPV after 73 months. In the best case, the pressure started at 3300 kPa and stepped up to 4200 kPa before it bottomed at 2275 kPa. Thereafter, the pressure increased to 4200 kPa and then dropped down to 3300 kPa at the end of the 73-month operating period. As shown
Table 3 Comparison of optimized operating strategies in all the four cases in terms of cumulative oil production, cumulative water produced to oil produced ratio (cWOR), cumulative energy injected to oil ratio (cEOR), operating time and net present value (NPV).
a
Case
Cumulative oil production (m3)
cWOR (m3/m3)
cEOR (GJ/m3)
Operating time (month)
NPVa ($million)
1 2 3 4
32,278 31,518 23,145 32,136
7.0 4.7 9.8 4.9
16.2 11.5 21.7 11.2
73 48 32 26
2.7 4.8 1.9 6.1
The blowdown performance is not considered in the NPV calculation which means the real NPV could be slightly higher than the presented values.
Fig. 4. Optimized steam injection pressure and solvent fraction for the Case 2.
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Fig. 5. Comparison of oil production rates and cumulative oil production of the optimized strategies of the Case 1 (pressure) and Case 2 (pressure + solvent).
equal to 7.0 m3/m3. The cumulative energy-to-produced oil ratio (cEOR) is found to be 16.2 GJ/m3, that is larger than the value of 10 GJ/m3 which is typical for SAGD. This strategy realized a NPV equal to $2.7 million, which is larger than the NPV of $1.2 million obtained from constant injection pressure equal to 3000 kPa.
5.2. Case 2. Steam injection pressure and solvent fraction optimization
Fig. 6. Comparison of cEORs of the optimized Case 1 (pressure) and Case 2 (pressure + solvent).
in Table 3, the optimized strategy achieved a cumulative oil production of 32,278 m3 (recovery factor equal to 77%) with a cWOR
Fig. 4 shows the optimized strategy employing both steam and solvent injection for this case. The optimized strategy realized a maximum NPV after 48 months. The results of this case are different from that of Case 1. Here, the pressure started with relatively low values ranging from 1500 to 2600 kPa over the first 21 months. It then increased to between 3800 and 4200 kPa over the next 14 months, which was followed by a substantial pressure reduction to 1200 kPa, which then remained for the last 13 months of its total 48-month operating time. The solvent injection strategy exhibited a cyclic pattern with relative higher value (0.14– 0.20 m3/m3) during the period of 10th to 27th month and a much lower value of 0.007 m3/m3 for its last 5 months. The optimized strategy of Case 2 realized a cumulative oil production equal to 31,518 m3 (recovery factor equal to 75%), which is similar to that in the Case 1. However, it depleted the reservoir in about 48 months, a 34% reduction in operating time. Fig. 5 shows
Fig. 7. Temperature (°C) profiles of optimized Case 2 (pressure + solvent).
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Fig. 8. Mole fraction of solvent in both vapor and oil phases of the optimized Case 2.
the oil production rates of the optimized strategies of the Cases 1 and 2. Although the optimized strategy of the Case 2 underperformed that of the Case 1 in terms of oil production rate over the first 2 years of operation, it achieved much higher oil rates over the next 2 years. It also achieved relatively low WOR (4.7 m3/m3). As shown in Fig. 6, the cEOR of the Case 2 optimized strategy is high over the first 2 years. This is due to low oil production rate during the same period while solvent injection was employed (solvent injected but not produced is considered to be energy injection cost). With high oil rates in the next 2 year, the cEOR quickly decreased and ended up with an overall value of 11.5 GJ/m3, which is better than that of Case 1 and comparable with typical SAGD performance. Therefore it realized a higher NPV of $4.8 million (versus $2.7 million for Case 1). Fig. 7 shows the temperature profiles within the reservoir after 22, 28, and 36 months or operation, respectively. The results reveal that heat transfer from injection well is relative slow during the first 2 years but quickly picked up its pace in the later stage. This is in line with the initial low steam injection rates. The improved injection in the later stage is due to breakthrough of solvent in the production after 2 years. As shown in Fig. 8, a thin layer of solvent advances at the top of reservoir and eventually forms a solvent channel. This formation of solvent channel promotes heat transfer in the direction of production well and dilutes the heavy oil in the upper part of the reservoir along its path. The increased
reservoir temperature and oil dilution results in a substantial decrease of oil viscosity, as exhibited in Fig. 9. The oil viscosity reduction gave rise to faster oil flow to production well under the flood pressure gradient. In the optimized case, the solvent recovery factor is equal 98%, that is, 98% of the injected solvent is produced. 5.3. Case 3. Steam injection pressure optimization in the presence of 2 m bottom water zone The optimized operating strategy achieved maximum NPV after 32 months of operation. The optimized steam injection pressure for this case is displayed in Fig. 10. The injection pressure started at about 2700 kPa and then increased in a stepwise manner to 3900 kPa and remained in the range from 3700 to 3900 kPa for 16 months. At the last one third of the total 32-month operating time (approximately 11 months), the injection pressure was equal to about 1200 kPa. Due to the existence of the 2 m bottom water zone, water cut was observed to be between 95.6% and 99.7% during the 1 year. Injected steam quickly lost its heat to the bottom water zone. As the operation continued, the warmed-up bottom water zone heated the oil zone and oil rate exceeded 10 m3/day after 1 year. Although the optimized strategy realized cumulative oil production equal to 23,145 m3 (recovery factor equal to 55%), it was found to have high cWOR (9.8 m3/m3) and cEOR (21.7 m3/m3) and therefore a lower
Fig. 9. Viscosity (cP) distribution of the optimized Case 2 (the grid blocks shown in white represent region with zero oil saturation).
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NPV than that achieved in Case 1. This suggested that the presence of bottom water makes it more difficult to produce oil in a steam injection operating strategy based on pressure-only control. 5.4. Case 4. Steam injection pressure and solvent fraction optimization in the presence of 2 m bottom water zone
Fig. 10. Optimized operating strategy of Case 3.
Fig. 11 shows the optimized steam injection pressure and solvent volume fraction in this case. The optimized operating strategy achieved a maximum NPV after 26 months of operation. The resulting optimized strategy had a relatively high injection pressure during the majority of the operating process with lower injection pressure towards the end of the production operation. A similar trend was also found for solvent injection except that its initial high value period is shorter than that of the high pressure interval. As shown in Fig. 12, the optimized case yielded a cumulative oil production volume equal to 32,137 m3 (recovery factor equal to 77%), a 39% increase compared to that of the optimized Case 3. With introduction of solvent, the oil production rate exceeded
Fig. 11. Optimized operating strategy of Case 4.
Fig. 12. Oil production rate and cumulative oil production of optimized Case 3 (pressure) and Case 4 (pressure + solvent).
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Fig. 13. Distribution of mole fraction of solvent in vapor and oil phases of optimized strategy of Case 4.
60 m3/day after 6 months of operation. Both the cWOR (4.9 m3/m3) and cEOR (11.2 GJ/m3) are comparable to those in the optimized results of the Case 2. In addition, due to the short operation time (26 months), it achieves the largest NPV in all the four investigated cases (see Table 3). The mechanistic factors accounting for the superior performance of the present case is as follows. First, the fluid injectivity is larger than that in the Cases 1 and 2 due to the existence of bottom water zone. The injected steam loses its enthalpy to the bottom water zone but this is recovered later as the heated water zone heats the oil zone. Second, the injected solvent travels
through the reservoir in the bottom water zone towards the production well. As shown in Fig. 13, a solvent-rich zone is found in the bottom water region. Due to its smaller density, solvent rises in the water zone and mixes with the oil. The ternary phase distribution shown in Fig. 14 suggests that the solvent front proceeds faster than the propagation of the steam chamber. The combined effects of heating the oil zone and solvent mixing both decrease the oil viscosity (as shown in Fig. 15). The decreased oil viscosity realizes relatively high oil flow and as a consequence of short breakthrough time, a shorter operating period. In addition, with a blowdown strategy, about 99% of solvent can be recovered.
Fig. 14. Ternary phase distributions of the optimized Case 4.
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Fig. 15. Viscosity (cP) distribution of the optimized Case 4 (the grid blocks shown in white represent region with zero oil saturation).
6. Conclusions Acknowledgements In the present work, stochastic optimization was conducted to determine optimum steam and steam–solvent flooding strategies in thin heavy oil reservoir in cases with and without a bottom water zone. The key observations are as follows. 1. The steam injection pressure optimization procedure realized a feasible operating strategy. However, the achieved cumulative energy injected to produced oil ratio (cEOR) is much higher than that obtained for SAGD in thicker oil sands reservoirs. 2. The steam–solvent optimization procedure achieved operating strategy with lower cEOR and WOR than those in the optimized injection pressure-only strategy. The results revealed that a solvent channel forms at the top of the reservoir after the breakthrough of solvent in the production well. The formation of the solvent channel promotes oil–solvent mixing as well as heat transfer to the oil zone which in turn results in better reservoir performance. 3. The presence of a bottom water zone was found to adversely affect the performance of the strategy achieved in steam injection pressure-based optimization case. High water-cut due to water coning leads to relatively high water-to-oil ratio and cEOR. Although the heated bottom water zone could mobilize oil located above the bottom water in a relatively short period of time, the overall economics is the worst of all the four investigated optimized cases. 4. With solvent introduction in the injected fluid, the presence of bottom water was shown to be a positive factor in promoting oil recovery. The bottom water enhanced mixing of solvent and oil due to the formation of a solvent-rich zone in the bottom water region. The optimized strategy in this case achieved the best economic performance. It is well known that the existence of bottom water worsens cold production and CHOPS performance; however, based on the results obtained in this study, we propose that solvent-aided steam-flooding should be considered as a potential operating strategy for thin (<5 m) heavy oil reservoirs, especially those with bottom water intervals. 5. The net present value calculations suggest, under present economic and reservoir model assumptions, that solvent co-injection with steam achieves improved economic performance compared to that of steam-alone operations.
Acknowledgement is extended to the Petroleum Technology Research Centre (PTRC) for their financial support and the University of Calgary for providing financial and logistical support as well as Computer Modelling Group for the use of its thermal reservoir simulator, STARS™. References [1] Adams DM. Experiences with waterflooding Lloydminster heavy-oil reservoirs. J Can Pet Technol 1982;34:1643–50. [2] Asghari K, Nakutnyy P. Experimental results of polymer flooding of heavy oil reservoirs. In: Paper 2008-189 presented at the Canadian international petroleum conference/SPE gas technology symposium 2008 joint conference (the petroleum society’s 59th annual technical meeting). Calgary, Alberta, Canada, 17–19 June; 2008. [3] Computer Modelling Group (CMG) Ltd. STARS™ Users Manual, Version 2011.10. Calgary, Alberta, Canada; 2011. [4] Gao C.-H. Advances of polymer flood in heavy oil recovery. In: Paper SPE 150384 presented at the SPE heavy oil conference and exhibition, Kuwait City, Kuwait, 12–14 December; 2011. [5] Gates ID. Solvent-aided steam-assisted gravity drainage in thin oil sand reservoirs. J Pet Sci Eng 2010;74:138–46. [6] Gates ID, Chakrabarty N. Design of the steam and solvent injection strategy in expanding solvent steam-assisted gravity drainage. J Can Pet Technol 2008;47:12–20. [7] Istchenko C. Well-wormhole model for CHOPS. M.Sc. thesis, University of Calgary; 2012. [8] Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of state calculations by fast computing machines. J Chem Phys 1953;21:1087–92. [9] Miller KA. State of the art of Western Canadian heavy oil water flood technology. In: Paper 2005-251 presented at the petroleum society’s 6th Canadian international conference (56th Annual Technical Meeting), Calgary, Alberta, Canada, June 7–9; 2005. [10] Pan Y, Chen Z, Sun J, Bao X, Xiao L, Wang R. Research progress of modelling on cold heavy oil production with sand. In: Paper SPE 133587 presented at the SPE Western Regional Meeting, Anaheim, Califonia, USA, 27–29 May; 2010. [11] Stalder JL. Unlocking bitumen in thin and/or lower pressure pay using cross SAGD (XSAGD). J Can Pet Technol 2009;48:34–9. [12] Tavallali M, Maini B, Harding T. Assessment of SAGD well configuration optimization in Lloyminster heavy oil reservoir. In: Paper SPE 153128 presented at the SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, 20–22 March; 2012. [13] Teare M, Burrowes A, Baturin-Pollock, Rokosh D, Evans C, Gigantelli D, Marsh R, Ashrafi B, Tamblyn C, Ito S, Willwerth A, Yemane M, Fong J, Kirsch M, Crowfoot C, et al. Alberta’s Energy Reserves 2011 and Supply/Demand, Outlook 2012–2021, June; 2012. [14] Zhao W, Wang J, Gates I.D. Thermally-based operating strategy and well placement for thin heavy oil reservoir. In: Presented at the 33rd IEAEOR Symposium, Regina, Canada, August 26–30; 2012.