SPE-139476-PA (Numerical Simulation and Economic Evaluation of Hybrid Solvent Processes)

28 Journal of Canadian Petroleum Technology

Numerical Simulation and Economic Evaluation of Hybrid Solvent Processes

T. Frauenfeld, C. Jossy, J. Ivory, Alberta Research Council

IntroductionSAGD is the main commercial technology used for in-situ re-

covery of Athabasca bitumen. Because of the increasing costs for energy (natural gas) and the increasing restrictions on fresh water usage, VAPEX(1) has been proposed.

The VAPEX process may be augmented by adding heat. Heating will reduce the oil viscosity sufficiently to produce a large increase in oil rate. The heat will serve to speed the diffusion of solvent into the oil. The heat will also serve to initiate communication between the injector and the producer.

Heat may be injected by using vapourized solvent or steam. Be-cause of the low latent heat capacity of solvent, it is expedient to heat the solvent by co-injection of steam. The result is a hybrid sol-vent process (Figure 1). This process may be operated at any set of steam and solvent rates between pure SAGD and pure VAPEX.

AbstractSolvent-based processes for recovery of heavy oil and bi-

tumen have potential application to a variety of reservoir situ-ations. Potential processes range from steam assisted gravity drainage (SAGD) to VAPEX, with a range of hybrid processes in between. Over 50 laboratory-scale and 80 field-scale simulations were run to determine optimum operating points for various hy-brid processes. The results showed that steam-butane simulations yielded two “sweet spots” where the cost objective function was lower than that for SAGD. Economic analysis was done based on a set of field-scale simulations. This analysis showed that a hy-brid solvent process for an Athabasca reservoir was an alterna-tive to SAGD. The analysis may be extended to other reservoir types as needed.

Detailed experimental, modelling and economic studies were done to determine an optimum point or points for this process.

Numerical 2D field-scale simulations were used to compare VAPEX, SAGD and hybrid solvent processes for an Athabasca bitumen reservoir. The comparisons considered propane, n-butane and n-pentane as solvents, and considered effects of steam rate, solvent rate, pressure and steam sub-cool setting of the produc-tion well. The results are displayed in more detail in the presented figures.

Scaled Laboratory Models for Heavy Oil Recovery

The Scaling Theory The numerical simulations were based on experiments done

at Alberta Research Council to model the steam-solvent hybrid process. Figure 2 shows a photo of the experimental apparatus. Figure 3 shows a diagram of the experimental model. The scaling criteria used for ARC laboratory model experiments on thermal processes are the Pujol and Boberg scaling criteria(2). This set of scaling criteria matches the ratios of gravity, viscous forces, con-ductive and convective heat transfer and diffusion, at the expense of incorrectly scaling pressure drop vs. capillary forces and disper-sion vs. diffusion. This scaling method is acceptable for SAGD, where thermal conduction is the rate-controlling step. The scaling will be less certain for VAPEX and hybrid solvent processes, where diffusion and dispersion play major roles in controlling pro-cess rates. Heat transfer, diffusion and dispersion are all impor-tant in hybrid solvent processes. These values must be determined

overburden

vaporchamber Mobilized oil

Injector

Steam heat for startup

Oil sand

Producer

Solvent + steam

overburden

vaporchamber Mobilized oil

Injector


Oil sand

Producer

Solvent + steam

Mobilized oil

Injector


Oil sand

Producer

Solvent + steam

OverburdenOverburden

UnderburdenUnderburden

VapourChamber

FIGURE 1: Schematic of the steam-solvent hybrid process. FIGURE 2: Scaled laboratory model of steam-solvent hybrid process.

July 2010, Volume 49, No. 7 29

experimentally. Experimental values of diffusion as a function of temperature are not yet available. The values were therefore de-rived by history matching(3).

Simulation Design

Several sets of 2D STARS simulations were run to perform field-scale predictions of steam-solvent hybrid process behaviour. First, simulations of experiments (grid mesh illustrated in Figure 4) were completed to validate the diffusion/dispersion parameters selected. Diffusivity of solvent in oil-phase parameters was set at 3.0E–6 m2/d. Oil-butane K-values were based on Winprop mod-elling. Viscosity of oil and oil-solvent mixtures was based on an oil viscosity measurement of 47,320 mPa.s at 30°C and the Put-tagunta correlation. Dispersion data by Sudicky(4) was used to set dispersivity at 0.005 m in the transverse direction and 0.5 m in the longitudinal direction. Relative permeability curves were similar to those used to model field-scale SAGD operations. The effect of live oil was also included. Correlated random permeability and po-rosity were used to assess the effect of permeability variations.

A set of simulations to optimize steam-propane at the field-scale was then run. Figure 5 is a typical field simulation grid mesh. The simulations considered steam rate, solvent rate and pressure. It was considered that operation of the steam-solvent process in sub-cool mode, as SAGD is operated, would be most efficient.

Optimizations were then run for the steam-butane hybrid pro-cess. Simulations were run at a constant sub-cool (25°C) in order to optimize pressure and solvent injection rate.

A similar set of simulations was run for the steam-pentane hy-brid process, to optimize the process in terms of pressure and steam rate. All simulations were compared in terms of an objective func-tion presented in the Economic Assumptions section that is related to supply cost.

Economic Assumptions In order to compare simulations where multiple performance

criteria exist, an objective function was devised. The function takes the form of:

Supply-cost=(fixed cost)+(capital)/(oil produced)+(steam cost)× (steam/oil ratio)+(solvent cost)×(net solvent/oil ratio)+ (solvent produced)/(oil produced)×(solvent recycle cost), .................... (1)

where objective function=supply cost/100.The function was calculated for all simulations. Local mini-

mums were plotted to illustrate optimum points, and selected sim-ulations subjected to economic analysis to verify the optimums.

Numerical Results Numerical simulations were first run to determine numerical

dispersion as a function of total dispersion (Table 1). This gave assurance that estimates of dispersion and diffusion from Black-well(5), Sudicky(4), Farrel et al.(6) and Hayduk-Cheng(7) could be used to estimate dispersion at the laboratory-scale, field-scale dis-persion and diffusion, respectively, and that these values could be meaningfully inserted into the numerical simulator.

FIGURE 4: Grid mesh for numerical simulation of hybrid solvent process.

FIGURE 5: Grid mesh for simulation of field-scale hybrid solvent process.

90 cm

30 cm

10 cmInjectorProducer

5 cm

250 Darcy oil-saturated sand

T.C. rod #1

5 cm


5 cm


5 cm


T.C. Rod #1

FIGURE 3: Schematic of steam-solvent hybrid experimental apparatus.

TAbLE 1: Numerical simulations to estimate numerical dispersion. Add. Disp. Oil Prod. Oil @ 2000 Days H2O Inj Run Description (m2/d) Sqrt. Ad. Disp (m3) (m3) @ 2000 d(m3)

R2D-02 Lab dispersion 3.00E-04 1.73E-02 6874 4875 8986 R2d-03 No add. Dispersion 0.00E+00 0.00E+00 6698 2867 9028 R2D-04 Labd., no gas dispersion 6926 8887 R2D-05 1/2 diff + disp. 1.50E-04 1.22E-02 6845 4308 9018 R2D-06 0.2 diff + disp 6.00E-05 7.75E-03 6874 3894 8888 R2D-08 0.1 diff +disp 3.00E-05 5.48E-03 6843 3614 8900 R2D-09 Steam-CH4, no disp 0.00E+00 0.00E+00 4007 1480 8884 R2D-10 Steam-CH4,lab disp 0.00E+00 0.00E+00 4077 1615 R2D-11 Lab diff., no disp. 3.00E-04 1.73E-02 6770 3864 8870 R2D-12 1/2 lab diff., no disp 1.50E-04 1.22E-02 6837 3793 8039 R2d-03 No add. Diff. 0.00E+00 0.00E+00 6698 2867 9028 R2D-13 Steam-CH4,lab diff only 0.00E+00 0.00E+00 3941 1506 8988 R2D-14 Field disp only 2.50E-04 1.58E-02 6716 2964 R2D-15 1/2 size grid mesh 0.00E+00 0.00E+00 6625 2865 8924 R2D-16 1/4 size grid mesh 0.00E+00 0.00E+00 ? 2724 8919 R2D-07 Steam-only 3.00E-04 1.73E-02 6644 6055 8011


TAbLE 3: Numerical simulations of laboratory-scale steam-butane hybrid process.

Table 2 contains results for the laboratory model simulations with propane. The SAGD simulation outperformed all of the sim-ulations of the steam-propane process in terms of oil production. However, most of the steam-propane simulations had a lower cost objective function than did the SAGD simulation.

Table 3 contains results for the laboratory model simulations with butane. Here, most of the steam-butane simulations outper-formed the SAGD simulation in terms of oil production, but they all underperformed the SAGD simulation in terms of the cost ob-jective function.

Table 4 contains results for the field-scale simulations with steam-propane. In the field-scale simulations, the SAGD simula-tion outperformed all of the steam-propane simulations, both in terms of the oil production and the objective function. This result was different than that of the laboratory-scale simulations.

Table 5 contains the results of field-scale simulations for the steam-butane simulations. All of the steam-butane simulations out-performed the SAGD simulation in terms of oil production. Some of the steam-butane simulations also outperformed the SAGD

simulations in terms of the cost objective function. These simula-tions were those using the lower butane injection rates [i.e., C4/steam ratios of 0.1275 – 0.0319 (liquid/liquid ratio)]. Optimal pres-sures were from 1,200 – 2,200 kPa. It was concluded that steam-butane was lower in cost than SAGD as a method of producing bitumen.

Figure 6 represents simulations of laboratory model experi-ments using steam-propane at a low steam rate (240 g/h) and var-ious pressures. Each data point represents one numerical run. The simulations show an optimal oil production at 1,200 kPa, and an optimal objective function of 1.25 at 1,100 kPa.

Figure 7 represents simulations of laboratory model experi-ments of a steam-propane hybrid process at a steam injection rate of 300 g/h. In this case, the optimum for both the oil production and the minimum objective function (1.14) occurs at 1,100 kPa.

Figure 8 represents numerical simulations of steam-butane hy-brid experiments using 500 g/h of steam. The oil production and objective function are plotted vs. butane injection rate. The op-timal butane injection rate appears to be 200 cc/h. The jog in the

TAbLE 2: Numerical simulations of laboratory-scale steam-propane hybrid process.

Solvent Steam/ Solvent/ Net Oil Prod H2O Solvent Solvent Oil Oil Solvent/ Mass Steam Propane Pressure Prod (g) @ Prod Prod in Pack Ratio Ratio Oil Ratio bal. Objective Run # (g/h) (cc/h) (kPa) (g) 600 min (g) (g) (g) (cc/cc) (cc/cc) (cc/cc) Err. (g) Func.

16e 300 100 900 2450 422.08 4500 1533 102.8 1.84 0.61 0.075 1.449 16a 300 100 1000 3023 400 4493 615.4 153 1.49 0.50 0.090 1.258 16g 300 100 1000 2750 391 4500 612 151.8 1.64 0.55 0.099 1.374 16f 300 100 1100 4000 323 4500 465 298 1.13 0.38 0.133 1.171 16b 300 100 1200 2601 300 4100 440 689.7 1.73 0.58 0.474 2.382 16c 300 100 1400 1644 264 4440 13.7 755 2.74 0.91 0.820 0.32 3.884 16d 300 100 1600 576 70.26 4500 112 650.7 7.81 2.60 2.017 10.67916j 300 200 1000 3006 895 4492 1380 156 1.50 1.00 0.093 1.299 16k 300 200 1100 3984 820 4500 1224 312 1.13 0.75 0.140 1.213 16l 300 200 1200 4927 353 4260 344 1204 0.91 0.61 0.436 9.21 1.936 16m 300 200 1300 4296 105.9 4571 136 1488 1.05 0.70 0.619 96.8 2.586 16h 240 200 1000 2732 898 3605 1383 153.5 1.32 1.10 0.100 1.273 16i 240 300 1000 2739 1406 3595 2149 155.3 1.31 1.64 0.101 0.07 1.298 16n 240 400 1000 2623 1917 3604 2902 151.6 1.37 2.29 0.103 1.367

16b-aaa 1500 0 1600 5523 0 14327 0 0 4.07 0.00 0.000 1.883

Solvent Solvent Steam/ Solvent/ Oil Inj H2O Prod Solvent Oil Oil Net Mass Steam butane Pressure Prod (g) @ Prod (g) @ in Pack Ratio Ratio Solvent/ bal. Objective Run # (g/h) (cc/h) (kPa) (g) 600 min (g) 600 min (g) (cc/cc) (cc/g) Oil Ratio Err. (g) Func.

16B-a 500 120 800 3656 730.8 7300 265 927 2.05 0.49 0.254 4.056 2.999 16B-b 500 200 800 6737 1218 7500 500 1524 1.11 0.45 0.226 169 2.334 16B-c 500 300 800 7724 1827 7500 605 2034 0.97 0.58 0.263 354 2.710 16B-d 500 400 800 7930 2436 7500 1061 2139 0.95 0.76 0.270 481 3.060 16B-db 500 400 800 7900 2436 7500 1062 583 0.95 0.76 0.074 490 2.509 16B-f 500 500 800 7975 3045 7500 1574 2138 0.94 0.94 0.268 500 3.407 16B-e 400 400 800 7890 2436 6000 970 2299 0.76 0.76 0.291 483 3.073 16B-eb 400 400 800 7859 2436 6000 965 710 0.76 0.76 0.090 567 2.510 16B-g 400 500 800 7996 3045 6000 1519 2310 0.75 0.94 0.289 576 3.394 16B-h 400 600 800 8034 3654 6000 2105 2293 0.75 1.12 0.285 587 3.718 16B-qb 400 600 700 7548 3654 6000 2288 65 0.79 1.19 0.009 436 3.066 16B-rb 400 600 900 8267 3654 5845 2047 40 0.73 1.09 0.005 610 2.852 16B-sb 400 600 1000 8414 3654 5820 1921 37 0.71 1.07 0.004 715 2.830 16B-j 400 500 900 5755 3045 7443 1523 1676 1.04 1.30 0.291 124 4.357 16B-g 400 500 800 7996 3045 7500 1519 2310 0.75 0.94 0.289 576 3.401 16B-K 400 500 700 7556 3045 6055 1706 2281 0.79 0.99 0.302 3.530 16B-i 300 508 800 7077 3093.72 4401 1634 2202 0.64 1.08 0.311 419 3.699 16B-L 300 400 800 7407 2436 4443 1010 2388 0.61 0.81 0.322 502 3.205 16B-m 300 600 800 7253 3654 4376 2183 2249 0.62 1.24 0.310 344 3.981 16b-n 300 500 800 7798 3045 4377 1576 2434 0.58 0.96 0.312 531 3.429 16B-ob 300 500 700 7246 3045 4495 1731 75 0.62 1.00 0.010 617 2.653 16B-pb 300 500 900 7552 3045 4477 540 63 0.60 0.96 0.008 2.820 16B-nb 300 500 800 7691 3045 4441 1577 60 0.59 0.94 0.008 2.536 16B-tb 240 600 800 6964 3654 3553 2218 88 0.52 1.25 0.013 645 3.117 16B-ub 240 600 700 6927 3654 3547 2385 89 0.52 1.26 0.013 656 3.092 16b-Vb 240 600 900 7864 3654 3546 2113 84.4 0.46 1.11 0.011 629 2.798 16B-Wb 500 700 900 8277 4263 7365 2678 19.4 0.91 1.23 0.002 719 3.168 16B-yb 400 700 900 8295 4263 5810 2621 41.47 0.72 1.22 0.005 690 3.105 16B-xb 280 700 900 7261 4263 4130 2714 83.4 0.58 1.40 0.011 690 3.420 16B-zb 240 700 900 6322 4263 3510 2709 80.53 0.57 1.61 0.013 3.867 16b-adb 300 800 1100 7915 4872 4394 3050 38.04 0.57 1.47 0.005 3.542 16b-acb 300 800 1000 8197 4872 4375 3154 51.3 0.55 1.42 0.006 3.408 16b-aab 300 800 900 7726 4872 4298 3332 61.87 0.58 1.50 0.008 3.570 16b-abb 300 800 800 7701 4872 4282 3446 60.33 0.58 1.51 0.008 3.555 16b-aeb 400 800 1000 8518 4872 6000 3135 1979 0.70 1.36 0.232 4.007 16b-aca 1500 0 1400 5497 0 13922 0 0 4.09 0.00 0.000 1.888 16b-aaa 1500 0 1600 5523 0 14327 0 0 4.07 0.00 0.000 1.883 16B-aba 1800 0 1800 5874 0 15237 0 0 4.60 0.00 0.000 2.072


objective function curve at 400 g/h represents a comparison be-tween an experiment, where the pressure is blown down at the end of the experiment to recover as much butane as possible, and a sim-ulation where no blowdown was used. Most simulations were run without a blowdown.

Figure 9 shows performance vs. pressure for steam-butane simulations that use 400 g/h steam. The optimum oil production, steam/oil ratio (SOR) and objective function (3.4) appears to be at 800 kPa.

Figure 10 represents field-scale SAGD baseline simulations. These simulations included a methane component in the Athabasca bitumen. These simulations showed a minimum objective function of 0.95 at 1,600 kPa. This (1,600 kPa) simulation was used as the

steam-only baseline to which the steam-solvent simulations were compared.

Figure 11 represents a series of field-scale simulations of a steam-propane hybrid process for a dead Athabasca bitumen. The optimal SOR, oil production and objective function occur at 1,500 kPa.

Figure 12 represents the effects of pressure on a steam-propane hybrid process for a live Athabasca bitumen. It was observed that the objective function had an optimum pressure range of 1,200 kPa – 1,500 kPa when live oil was used. Oil produced, objective func-tion and SOR shared this optimum range.

Figure 13 shows the performance predicted by a set of field-scale simulations of a steam-propane hybrid process in live Atha-basca bitumen, for a steam injection rate of 6 m3/d (per 10 m of

TAbLE 4: Numerical simulations of field-scale steam-propane hybrid process.

TAbLE 5: Numerical simulations of field-scale steam-butane hybrid process. Oil @ Oil @ H2O Inj C4 C4 Inj. Oil 1000 2000 @ 2000 H2O C4 Inj. Prod Net C4 SOR Stm Inj. (std Pressure Sub-cool Prod Days Days Days Prod @ 2000 @ 2000 in Pack @ 2000 Run Description (m3/d) m3/d) (kPa) ( C) (m3) (m3) (m3) (m3) (m3) days days (m3) DaysR2DB-17 field disp, 2100 kPa,CH4 6 1860 2100 25 7110 6587 7094 9936 10032 3.41E+06 3.33E+06 7.20E+04 1.40R2DB-15 field disp, 1900 kPa,CH4 4.46 1860 1900 25 7186 6367 7163 8685 8678 3.67E+06 3.55E+06 1.17E+05 1.21R2DB-13 field disp, 1700 kPa,CH4 4.46 1860 1700 25 7179 6266 7152 8404 8479 3.65E+06 3.52E+06 1.30E+05 1.18R2DB-11 field disp, 1500 kPa,CH4 4.46 1860 1500 25 7173 5996 7130 8174 8300 3.62E+06 3.48E+06 1.42E+05 1.15R2DB-09 field disp, 1300 kPa,CH4 4.46 1860 1300 25 7176 5436 7099 8063 8191 3.67E+06 3.50E+06 1.68E+05 1.14R2DB-05 field disp, 1100 kPa,CH4 4.46 1860 1100 25 7172 4630 7015 7610 7759 3.67E+06 3.48E+06 1.90E+05 1.08R2DB-04 field disp, 1000 kPa,CH4 4.46 1860 1000 25 7181 4194 6924 7065 7234 3.67E+06 3.46E+06 2.13E+05 1.02R2DB-01 field disp only, 900 kPa 4.46 1858 900 25 7214 3743 6806 6344 6342 3.67E+06 3.45E+06 2.21E+05 0.93R2DB-02 field disp, 800 kPa 4.46 1860 800 25 7222 3157 6471 4983 5114 3.65E+06 3.40E+06 2.58E+05 0.77R2DB-02a field disp, 800 kPa,CH4 4.46 1860 800 25 7196 3132 6432 4998 5125 3.66E+06 3.40E+06 2.63E+05 0.78R2DB-03a field disp, 700 kPa,CH4 4.46 1860 700 25 7203 2550 5469 3805 3914 3.66E+06 3.32E+06 3.42E+05 0.70R2DB-08 field disp,1400 kPa,CH4 8 240 1400 25 7068 5381 6902 6527 6688 4.76E+05 3.23E+05 1.53E+05 0.95R2DB-06 field disp,1600 kPa,CH4 8 240 1600 25 7028 5781 6924 7113 7260 4.80E+05 3.48E+05 1.32E+05 1.03R2DB-07 field disp,1800 kPa,CH4 8 240 1800 25 6988 6043 6918 7753 7866 4.75E+05 3.62E+05 1.13E+05 1.12R2DB-10 field disp,1600 kPa,CH4 8 480 1600 25 7106 6014 7047 7675 7779 9.53E+05 8.05E+05 1.48E+05 1.09R2DB-12 field disp,1800 kPa,CH4 8 480 1800 25 7057 6240 7018 8196 8261 9.54E+05 8.33E+05 1.22E+05 1.17R2DB-14 field disp,2000 kPa,CH4 8 480 2000 25 7016 6377 6983 8495 8505 9.52E+05 8.46E+05 1.05E+05 1.22R2DB-16 field disp,2200 kPa,CH4 8 480 2200 25 6984 6429 6952 8664 8681 9.43E+05 8.52E+05 9.11E+04 1.25R2DB-20 field disp,1200 kPa,CH4 8 120 1200 25 6953 4263 6553 5837 6024 2.35E+05 1.15E+05 1.20E+05 1.18R2DB-18 field disp,1400 kPa,CH4 8 120 1400 25 6923 4914 6680 6506 6687 2.41E+05 1.28E+05 1.13E+05 0.97R2DB-19 field disp,1600 kPa,CH4 8 120 1600 25 6874 5229 6703 7106 7242 2.40E+05 1.36E+05 1.05E+05 1.06R2DB-21 field disp,1200 kPa,CH4 8 60 1200 25 6797 3603 6108 5961 6103 1.19E+05 4.01E+04 7.88E+04 0.98R2DB-23 field disp,1400 kPa,CH4 8 60 1400 25 6725 4025 6188 6508 6529 1.21E+05 4.20E+04 7.86E+04 1.05R2DB-22 field disp,1600 kPa,CH4 8 60 1600 25 6695 4401 6279 7201 7319 1.20E+05 4.16E+04 7.81E+04 1.15R2D-27 S. fld. D&D, live O.,stm.o., Kh 8.0 - 0.85 0 1400 13.8 6177 4135 5867 9312 9387 0 0 0.00E+00 1.59

Oil @ Oil @ H2O Inj H2O Solvent Solvent Net Solvent Stm Inj. C3 Inj. Pressure Sub-cool Oil Prod. 1000 d 2000 d @ 2000 Prod Inj. @ Prod @ in Pack SOR Net C3ORRun Description (m3/d) (std m3/d) (kPa) (˚C) (m3) (m3) (m3) days (m3) (m3) 2000 d 2000 d (m3) @ 2000 d @ 2000 d

R2D-14 field disp only, 1200 kPa 4.46E+00 1.86E+03 1200 5 6716 1717 2964 8912 9023 3.69E+06 3.65E+06 4.50E+04 3.01 0.063R2D-17 no add disp. disp, 1400 kPa 4.46 1858 1400 5 6691 2286 3365 8920 9092 3.69E+06 3.58E+06 1.04E+05 2.65 0.129R2D-18 field scale diff, 1200 kPa 4.46 1858 1200 5 6799 2200 3738 8933 9091 3.69E+06 3.58E+06 1.04E+05 2.39 0.116R2D-19 Sudicky field disp&diff, 1200 kPa 4.46 1858 1200 5 6821 2359 3926 8928 9097 3.70E+06 3.59E+06 1.06E+05 2.27 0.112R2D-22 Sud. Field D&D, 1300 kPa 4.46 1858 1300 5 7014 2670 4202 8908 9041 3.68E+06 3.53E+06 1.49E+05 2.12 0.148R2D-20 S” field D & D, 1400 kPa 4.46 1858 1400 5 7218 3016 4430 8902 9041 3.67E+06 3.53E+06 1.40E+05 2.01 0.132R2D-20a S” field D & D, 1500 kPa 4.46 1858 1500 5 3243 4510 6941 9099 3.69E+06 3.55E+06 1.34E+05 1.54 0.124R2D-21 Sud. Field D&D, 1600 kPa 4.46 1858 1600 5 6647 2317 3498 8912 9060 3.69E+06 3.64E+06 5.40E+04 2.55 0.064R2D-23 S. field D&D, live oil, stm. Only 4.45 0 1100-2360 5 6480 3035 5563 8958 9127 0 0 0.00E+00 1.61 0.000R2D-24 S. fld. D&D, live O.,stm.o., Kh 4.46 0 1080-2360 5 6250 3151 5669 8975 8989 0 0 0.00E+00 1.58 0.000R2D-25 S. fld. D&D, live O.,stm.o., Kh 2.98-1.03 0 800 5 4733 1891 3316 4994 4981 0 0 0.00E+00 1.51 0.000R2D-26 S. fld. D&D, live O.,stm.o., Kh 5.8 - 0.85 0 1200 5 5930 3370 4988 7562 7645 0 0 0.00E+00 1.52 0.000R2D-27 S. fld. D&D, live O.,stm.o., Kh 8.0 - 0.85 0 1600 5 6177 4135 5867 9312 9387 0 0 0.00E+00 1.59 0.000R2D-28 S. fld. D&D, live O.,stm.o., Kh 12.0 - 0.9 0 2000 5 6292 4884 6178 10613 10678 0 0 0.00E+00 1.72 0.000R2D-29 S. fld. D&D, live O.,stm.o., Kh 14.0 - 0.9 0 2200 5 6333 5244 6261 11161 11187 0 0 0.00E+00 1.78 0.000R2D-31 S. fld. D&D, live O.,stm.o., Kh 4.46 900 1450 5 3230 2480 3150 4016 3682 1.78E+06 1.43E+06 3.46E+05 1.27 0.458R2D-33 S” field D & D, live O., 1400 kPa 4.46 2300 1400 5 7261 2648 4020 8940 9091 4.56E+06 4.44E+06 1.22E+05 2.22 0.126R2D-35 S” field D & D, live O., 3.0stm 3 1858 1000 5 4652 1195 2132 5934 5898 3.68E+06 3.64E+06 3.30E+04 2.78 0.064R2D-34 S” field D & D, live O., 3.0stm 3 1858 1200 5 6804 2027 3541 5963 6075 3.70E+06 3.59E+06 1.12E+05 1.68 0.132R2D-37 S” field D & D, live O., 3.0stm 3 1858 1300 5 7061 2235 3606 5920 6043 3.68E+06 3.55E+06 1.34E+05 1.64 0.155R2D-36 S” field D & D, live O., 3.0stm 3 1858 1400 5 7212 2334 3522 5955 6049 3.68E+06 3.56E+06 1.27E+05 1.69 0.150r2d-39B S” field D & D, live O., 3.0stm 3 1858 1500 5 7128 2204 3339 5944 6075 3.69E+06 3.58E+06 1.10E+05 1.78 0.137R2d-39a S” field D & D, live O., 3.0stm 3 1858 1600 5 6096 1669 2695 5945 3.69E+06 3.67E+06 2.00E+04 2.21 0.031R2d-41 S” field D & D, live O., 6.0stm 6 1858 1200 5 6852 2504 4114 11843 11969 3.70E+06 3.60E+06 1.03E+05 2.88 0.104R2D-38 S” field D & D, live O., 6.0stm 6 1858 1400 5 7196 2927 4600 11887 11986 3.68E+06 3.55E+06 1.32E+05 2.58 0.120R2D-45 S” field D & D, live O., 6.0stm 6 1858 1500 5 7367 3050 4458 11897 11993 3.68E+06 3.56E+06 1.20E+05 2.67 0.112R2d-39 S” field D & D, live O., 3.0stm 6 1858 1600 5 6784 2354 3700 11900 11904 3.68E+06 3.66E+06 2.20E+04 3.22 0.025r2D-40 S” field D & D, live O., 6.0stm 6 1858 1800 5 6831 2390 3783 11911 11908 3.68E+06 3.67E+06 1.20E+04 3.15 0.013R2d-42 S” field D & D, live O., 6.0stm 6 1858 2000 5 6852 2458 3899 11918 11954 3.69E+06 3.67E+06 2.30E+04 3.06 0.025R2D-43 S” field D & D, live O., 5.35stm 5.35 1858 1400 5 7206 2815 4380 10546 10672 3.68E+06 3.55E+06 1.32E+05 2.41 0.126R2D-46 S” field D & D, live O., 5.35stm 5.35 1858 1500 5 7394 2957 4241 10604 10718 3.69E+06 3.57E+06 1.18E+05 2.50 0.116R2D-44 S” field D & D, live O., 5.35stm 5.35 1858 1600 5 6714 2245 3521 10608 10618 3.69E+06 3.67E+06 2.10E+04 3.01 0.025R2D-47 S” field D & D, live O., 75C sub 4.46 1858 1400 75 3926 1232 1710 1562 1616 3.67E+06 3.59E+06 7.70E+04 0.91 0.188R2D-51 S” field D & D, live O., 65C sub 4.46 1858 1400 65 1714 2572 2662 2746 3.68E+06 3.56E+06 1.17E+05 1.03 0.190R2D-52 S” field D & D, live O., 45C sub 4.46 1858 1400 45 2276 3272 4267 4363 3.68E+06 3.55E+06 1.34E+05 1.30 0.171R2D-53 S” field D & D, live O., 25C sub 4.46 1858 1400 25 7291 2756 3960 7658 7772 3.68E+06 3.55E+06 1.35E+05 1.93 0.142R2D-47 S” field D & D, live O., 75C sub 4.46 1858 1400 75 3926 1232 1710 1562 1616 3.67E+06 3.59E+06 7.70E+04 0.91 0.188R2D-48 S” field D & D, live O., 75C subC 4.46 1858 1600 75 6754 1626 2685 1544 1603 3.58E+06 2.88E+06 7.06E+05 0.58 1.096R2D-49 S” field D & D, live O., 55C subc 4.46 1858 1800 55 7132 1991 4470 4166 4342 3.69E+06 2.59E+06 1.09E+06 0.93 1.019R2D-50 S” field D & D, live O., 45C subc 4.46 1858 2000 45 2559 4455 5425 5599 3.69E+06 2.94E+06 7.43E+05 1.22 0.695R2D-54 S” field D & D, live O., 65C subc 4.46 1858 1500 65 7400 2047 3394 3099 3201 3.35E+06 2.97E+06 3.81E+05 0.91 0.468R2D-55 S” field D & D, live O., 45C subc 4.46 1858 1500 45 5500 1743 2796 3235 3352 3.68E+06 3.48E+06 1.95E+05 1.16 0.291R2D-56 S” field D & D, live O., 25C subc 4.46 1858 1500 25 7700 2837 3619 5680 5776 3.68E+06 3.54E+06 1.43E+05 1.57 0.165R2D-57 S” field D & D, live O., 25C subc 4.46 1858 1500 15 7411 2765 3927 8831 8878 3.68E+06 3.57E+06 1.13E+05 2.25 0.120R2D-58 S” field D & D, live O., 4.46 stm. 4.46 1858 1500 5 7409 2772 3927 8837 8930 3.69E+06 3.56E+06 1.30E+05 2.25 0.138R2D-56b S” field D & D, live O., 25C subc 4.46 1858 1500 25 7800 2837 3617 5681 5779 3.69E+06 3.55E+06 1.39E+05 1.57 0.160R2D-52b S” field D & D, live O., 45C sub 4.46 1858 1500 45 2272 3275 4271 4372 3.68E+06 3.55E+06 1.27E+05 1.30 0.162


Propane Hybrid Solvent Simulations, Effect of Pressure @ 240 g/h steam

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FIGURE 6: Propane hybrid solvent simulations, effect of pressure @ 240 g/h steam.

Propane Hybrid Solvent Simulations, Effect of Pressure @ 300 g/h steam

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FIGURE 7: Propane hybrid solvent simulations, effect of pressure @ 300 g/h steam.

SAGD Field SImulations - Effect of Pressure

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Oil @ 2000 days (m3)Oil prod. (m3)Oil @ 1000 d (m3)SOR @ 2000 dObjective func.

FIGURE 10: SAGD field simulations – effect of pressure.

Hybrid Solvent - C4 Rate Sensitivity, 500 g/h Steam

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FIGURE 8: Hybrid solvent – C4 rate sensitivity, 500 g/h steam.

Dead Oil, Steam-Propane @ 4.6 m3/d steam, 1858 m3/d C3, Effect of Pressure

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Oil prod. (m3)Oil @ 2000 days (m3)Oil @ 1000d (m3)SOR @ 2000 dNet C3OR @ 2000 dObjective func.

FIGURE 11: Dead oil, steam-propane @ 4.6 m3/d steam, 1858 m3/d C3, effect of pressure.

Steam-C4 Hybrid, Inj. Press. vs. Oil Prod., 400 g/h steam, 500 cc/h C4

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FIGURE 9: Steam-C4 hybrid, inj. press. vs. oil prod., 400 g/h steam, 500 cc/h C4.

Steam-Propane @ 3.0 m3/d steam, 1858 m3/d C3, Effect of Pressure

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Oil prod. (m3)Oil @ 2000 days (m3)Oil @ 1000 d (m3)SOR @ 2000 dNet C3OR @ 2000 dObjective func.

FIGURE 12: Steam-propane @ 3.0 m3/d steam, 1858 m3/d C3, effect of pressure.

Steam-C3 @ 6.0 m3/d steam, 1858 m3/d C3, Effect of Pressure

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Oil prod. (m3)Oil @ 2000 days (m3)0il @ 1000 d (m3)SOR @ 2000 dNet C3OR @ 2000 dObjective func.



wellbore). The optimum objective function is 1.84 at 1,400 kPa, as compared to the objective function of 3.4 in Figure 9.

Figure 14 represents field-scale simulations of a steam-propane hybrid process with a steam injection rate of 5.35 m3/d per 10 m wellbore. The optimum pressure in this case is 1,400 kPa – 1,500 kPa, and the objective function value is 1.81, slightly lower than for the simulations in Figure 13.

Figure 15 shows results from field-scale simulations using a steam rate of 4.46 m3/d/10 m, a C3 rate of 1858 std m3/d/10 m and a pressure of 1,400 kPa. The amount of sub-cool was from 25°C to 65°C. An additional set of sub-cool simulations was run, shown in Figure 16. This set of simulations was run at 1,500 kPa. The op-timum amount of sub-cooling was found to be 25°C. A sub-cool setting of 25°C was chosen for all succeeding simulations.

Figure 17 represents steam-butane hybrid simulations using 4.46 m3/d/10 m steam, 1858 std m3/d/10m butane and 25°C sub-cool. The lowest objective function was at 2,200 kPa. Figure 18 shows a

set of simulations of a steam-butane hybrid process at 8 m3/d/10 m steam and 480 std m3/d/10 m butane. The optimum pressure was at 2,200 kPa. The objective function score was 0.931, lower than the score of 1.016 for the best steam butane simulation at high butane rates. In Figure 19, the butane rate was cut to 120 std m3/d. The result was a minimum objective function of 0.931 at an optimum pressure of 2,200 kPa. In Figure 19, the simulations are run for cases where the butane injection rate is set to 120 std m3/d/10 m, or 1/4 the rate in Figure 18. The result was a minimum objective func-tion at 1,400 kPa, with an objective function value of 0.876. This is the lowest objective function found in this numerical study.

Figure 20 shows the results of a set of simulations of a steam-pentane hybrid process. The simulations show a minimum objec-tive function at 1,000 kPa and a jog in the curve at 800 kPa, where an increase in the sub-cool from 25°C – 35°C significantly reduced the SOR, but did not significantly change the cost objective func-tion, which had a minimum value of 1.095.

Steam-Propane @1,500 kPa, 1858 m3/d C3, Effect of Sub-cool

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FIGURE 16: Steam-propane @ 1,500 kPa, 1858 m3/d C3, effect of sub-cool.

Steam-Propane @5.35 m3/d steam, 1858 m3/d C3, Effect of Pressure

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Steam-Butane @4.46 m3/d steam, 1858 m3/d C4, 25°C Subcool

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FIGURE 17: Steam-butane @ 4.46 m3/d steam, 1858 m3/d C4, 25˚C sub-cool.

Steam-Propane @ 1,400 kPa, 1858 m3/d C3, Effect of Sub-cool

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FIGURE 15: Steam-propane @ 1,400 kPa, 1858 m3/d C3, effect of sub-cool.

Steam-Butane @8.0 m3/d steam, 480 m3/d C4, 25˚C Sub-cool

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FIGURE 18: Steam-butane @ 8.0 m3/d steam, 480 m3/d C4, 25˚C sub-cool.

Steam-Butane @8.0 m3/d steam, 120 m3/d C4, 25˚C Sub-cool

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FIGURE 19: Steam-butane @ 8.0 m3d steam, 120 m3/d C4, 25˚C sub-cool.


Economic Analysis ResultsA supply cost economic analysis was done on several “best

case” simulations. The results were used to produce a more accu-rate prediction of supply cost, and to validate the objective func-tion ranking of numerical simulations. Figure 21 is the incremental and cumulative cost by year for a SAGD baseline. This simula-tion shows a supply cost of CAD$124/m3 at 5 years. The objec-tive function was 0.95. This was a 1,600 kPa SAGD case, and was used as the benchmark cost value, to which other simulations were compared.

Figure 22 shows the cost performance of a steam-propane simu-lation using 1858 std m3/d/10 m propane at 1,500 kPa. The supply cost, at CAD$142/m3 is substantially higher than the SAGD cost, and roughly in agreement with the objective function score of 1.56 for this simulation.

Figure 23 shows economic performance of a steam-butane sim-ulation having a 60 std m3/d butane injection rate. The simulation had a supply cost of CAD$86/m3 at 7 years, and an objective func-tion of 0.872. This case also economically out-performed SAGD.

Figure 24 shows the supply cost profile for a steam-pentane hybrid at 120 std m3/d C5. The cost at maturity is CAD$110/m3. Figure 25 shows a steam-hexane hybrid simulation at 240 std m3/d C6. The supply cost at maturity is CAD$108/m3.

Discussion All of the steam-propane hybrid simulations resulted in a higher

objective function (cost) than the SAGD simulations. Two oper-ating points in the steam-butane domain showed lower objective functions and supply cost than did the SAGD simulations. The low butane-high steam simulations scored the lowest cost objec-tive function and the lowest supply cost. Steam-pentane and steam-hexane processes has a lower supply cost than SAGD, but higher than the steam-butane hybrid process.

Laboratory-scale simulations differed from field-scale simula-tions in terms of optimal pressure and optimal net solvent/oil ratio. Reasons for the difference between laboratory-scale and field-scale simulations are: heat loss through the side walls in laboratory

Steam-Propane Hybrid, 1858 m3/d C3, 1,500 kPa, Inc. and Cum. Oil Cost

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FIGURE 22: Steam-propane hybrid, 1858 m3/d C3, 1,500 kPa, incremental and cumulative oil cost.

Steam-Pentane @ 4.46 m3/d steam, 1860 m3/d C5, 25˚C Sub-cool

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FIGURE 20: Steam-pentane @ 4.46 m3/d steam, 1860 m3/d C5, 25˚C sub-cool.

Steam-Butane Hybrid, 60 std m3/d C4, Incremental and Cumulative Oil Cost

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FIGURE 23: Steam-butane hybrid, 60 std m3/d C4, incremental and cumulative oil cost.

SAGD, 1,400 kPa, 600 m3/d steam , Incremental and Cumulative Oil Cost

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FIGURE 21: SAGD, 1,400 kPa, 600 m3/d steam, incremental and cumulative oil cost.

Steam-Pentane Hybrid, 120 std m3/d C5, Incremental and Cumulative Oil Cost

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FIGURE 24: Steam-pentane hybrid, 120 std m3/d C5, incremental and cumulative oil cost.

Steam-Hexane Hybrid, 240 m3/d C6, Incremental and Cum. Oil Cost

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FIGURE 25: Steam-hexane hybrid, 240 m3/d C6, incremental and cumulative oil cost.


model experiments and dead oil in laboratory models vs. live oil in a field reservoir.

Numerical simulations of laboratory experiments produced consistent under-prediction of the steam-butane performance. One possible reason for this is the use of a constant molecular diffu-sivity term rather than temperature-dependent or viscosity-depen-dent diffusivity. Diffusivity may therefore be under-represented in some simulations.

SAGD baseline simulations showed that the optimal pressure for SAGD was 1,600 kPa for the field-scale simulation at the con-ditions considered.

A definite optimum pressure existed for the dead oil/propane simulations (1,500 kPa), in terms of the objective (cost) function. Simulations of a “live oil scenario,” in which the bitumen has a small component of dissolved methane, had a much broader op-timum pressure range. Steam butane simulations similarly pro-duced a broad optimal pressure range for a minimum objective (cost) function.

The use of a 25°C sub-cool setting to control the production well increased the efficiency of the steam-propane process. The optimal sub-cool was from 25°C – 45°C for the steam-butane simulations.

Numerical dispersion contributed significantly to the total dis-persion in hybrid solvent simulations. The fraction of numerical dispersion to total dispersion was approximately 0.25 in field-scale simulations.

The objective function used is an adequate guide for quickly evaluating simulations, but a full economic analysis is required to get a better prediction of process cost.

ConclusionsThe steam-butane hybrid process was predicted to have op-

timum operating points, which had lower SOR and lower objective (cost) function than SAGD.

The lowest cost process for production of Athabasca bitumen was predicted to be the steam-butane hybrid process using a bu-tane/steam ratio of 0.082 m3/m3 liquid.

A low-cost optimum for the steam-butane hybrid process will also occur at a butane/steam ratio of approximately 0.11 m3/m3 liquid.

The steam-butane hybrid process had lower supply cost than steam-propane, steam-pentane or steam-hexane.

The simulations could be improved by including mechanisms, such as viscosity-dependent diffusivity, improved oil-solvent phase behaviour, and the effects that explain produced oil viscosity.

Acknowledgements The authors thank the AERI/ARC/Core/Industry Research pro-

gram for their financial and technical support. Discussions with Xiaohui Deng, concerning the numerical simulation of the experi-ments and the economic analysis inputs, were much appreciated. Thanks to Valerie Pinkoski for final formatting and editing of the manuscript.

NomeNclatureºC = temperatureC4/steam ratio = m3/m3 liquid vol.g/h = grams/hourkPa = pressurem3/d steam flow = liquid H2O equivalentm3/d gas flow = gas at standard conditions (std m3/d)

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This paper (2009-108) was accepted for presentation at the 10th Canadian International Petroleum Conference (the 60th Annual Technical Meeting of the Petroleum Society), Calgary, 16-18 June, 2009, and revised for pub-lication. Original manuscript received for review 26 March 2009. Revised paper received for review 4 May 2010. Paper peer approved 10 May 2010 as SPE Paper 139476.

Authors’ BiographiesTed Frauenfeld is a senior research en-gineer in the heavy oil and oil sands busi-ness unit at Alberta Research Council. He has been a co-leader of the hybrid steam-solvent strategic area and co-ordinates sev-eral projects investigating the application of solvents to heavy oil and bitumen recovery processes. Frauenfeld has worked on a va-riety of oil recovery processes, including thermal recovery and miscible flooding. He holds a B.S. degree in mechanical en-

gineering and an M.S. degree in petroleum engineering, both from the University of Alberta. Frauenfeld is a member of APEGGA and SPE. He is currently living in the South Okanagan in B.C.

Chris Jossy is a research technologist with the heavy oil and oil sands group at Alberta Research Council. He is responsible for the laboratory model experiments investigating thermal and non-thermal recovery pro-cesses for heavy oils. Jossy has worked on a variety of projects related to the recovery of heavy oil and bitumen. He holds a diploma in electronics engineering technology from NAIT.

John Ivory has been a research engineer at the Alberta Research Council since 1981 in the areas of enhanced oil recovery (pri-marily steam and solvent based processes and in-situ combustion) and gas separa-tion and purification using membranes, ad-sorption or absorption technologies. He has extensive experience in both designing ex-periments and performing numerical sim-ulations related to heavy oil and bitumen recovery using cold production, solvent in-

jection, steam injection and in-situ combustion. Ivory is currently leader of ARC’s Reservoir Simulation Group.

Documents

SPE-139476-PA (Numerical Simulation and Economic Evaluation of Hybrid Solvent Processes)