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Energy Sources, Part B: Economics, Planning, and Policy ISSN: 1556-7249 (Print) 1556-7257 (Online) Journal homepage: http://www.tandfonline.com/loi/uesb20 Economic optimization for operation options in thermal oil recovery process Huy X Nguyen, Wisup Bae, Dung Q Ta, Chung Taemoon & Yunsun Park To cite this article: Huy X Nguyen, Wisup Bae, Dung Q Ta, Chung Taemoon & Yunsun Park (2016) Economic optimization for operation options in thermal oil recovery process, Energy Sources, Part B: Economics, Planning, and Policy, 11:5, 418-427, DOI: 10.1080/15567249.2011.626015 To link to this article: http://dx.doi.org/10.1080/15567249.2011.626015 Published online: 13 Jun 2016 Submit your article to this journal Article views: 10 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=uesb20 Download by: [University of Lethbridge] Date: 19 June 2016, At: 18:19 ENERGY SOURCES, PART B: ECONOMICS, PLANNING, AND POLICY 2016, VOL 11, NO 5, 418–427 http://dx.doi.org/10.1080/15567249.2011.626015 Economic optimization for operation options in thermal oil recovery process Huy X Nguyena,b, Wisup Baea, Dung Q Tab, Chung Taemoona, and Yunsun Parkc Sejong University, Gwangjin-ku, Seoul, Korea; bFaculty of Geology and Petroleum Engineering, Ho Chi Minh City University of Technology, Vietnam National University - Ho Chi Minh City, Ho Chi Minh City, Viet Nam; cMyongji Univesity, Gyeonggi-Do, Korea Downloaded by [University of Lethbridge] at 18:19 19 June 2016 a ABSTRACT KEYWORDS This paper describes the uses of the Box–Behnken experimental design to optimize the factors affecting the production performance in steam-assisted gravity drainage (SAGD) operation, Peace River oil sands The response surface methodology (RSM) was employed to search for the best designs in contour plots and response surface map A total of 41 cases were run to optimize the parameters of operating conditions and the net present value (NPV) responses during 10 years of the simulation period To maximize the net present value, the optimal conditions should operate at a well pattern spacing (WPS) of 78 m, a steam injection rate of 550 m3/d, an injector producer spacing (IPS) of 14 m, injection a pressure (IP) of 6,350 kPa, and a subcool of 5°C Simulation results showed that cumulative oil for the Fast-SAGD process does not significantly increase and even NPV is the lowest among the mentioned SAGD cases The difference of 10 kPa between steam IP and reservoir pressure is not sufficient to increase the NPV for both Fast-SAGD and SAGD operations Box–Behnken design; NPV; optimization; response surface methodology; SAGD Introduction The depletion of conventional crude oil reserves in the global scenario is one of the biggest challenges for increasing energy demand in the future The enormous potential of heavy oil and bitumen resources has been proved in the Americas including Canada, Venezuela, and California However, the extremely high viscosity of bitumen at normal reservoir temperature makes it much more difficult in the oil recovery process The steam-assisted gravity drainage (SAGD) process is an effective method of producing heavy oil and bitumen utilizing two parallel horizontal wells, one above the other (Butler, 2001) The top well is the steam injector and the bottom one is the oil collector When steam is continually injected in the top well, a steam chamber forms in the reservoir and grows upward to the surroundings displacing heated oil following gravity mechanism drain into the producer The operation technical issues play an important role in increasing oil recovery and reducing the amount of steam injection However, economic risks associated with the fluctuation of oil and gas prices have affected the benefit of the oil sand project In order to maintain a high profit, optimal operating conditions need to determine the best design Previous literatures have employed the optimization of operating conditions by classical methods based on their numerical simulations and experiments (Polikar, 2000; Gong, 2002 and Shin and Polikar, 2007) However, there is not sufficient evidence of this reliability because they didn’t define the confidence level of the operating parameters and ignored the interaction parameters, which CONTACT Wisup Bae wsbae@sejong.ac.kr Sejong University, 98 Gunja-dong, Gwangjin-ku, Seoul, 143-747 Korea Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/uesb © 2016 Taylor & Francis Group, LLC ENERGY SOURCES, PART B: ECONOMICS, PLANNING, AND POLICY 419 might have led to low efficiency issues in field operation The use of the Box–Behnken design (BBD) and response surface methodology (RSM) will overcome the disadvantages of the classical method In addition, input parameters of the economic models in previous studies were not comprehensive enough, with limited consideration on only three factors: cost of steam, bitumen price, and discount rate That approach could significantly reduce the accuracy of economic analysis and it is difficult to predict the best choice of operating conditions Downloaded by [University of Lethbridge] at 18:19 19 June 2016 Numerical reservoir model The thermal reservoir simulator, STARTTM, was used to construct a reservoir model with gridblocks 151 × 850 × 25 m with no aquifer and to evaluate the production performance of SAGD and FastSAGD process Three reservoir models and a series of numerical simulations were conducted to screen these processes in Bluesky formation, Peace River region Grid, rocks, and fluid properties have been described in the previous literatures (Shin and Polikar, 2005) Constant values of porosity (28%) and directional permeability (Kv = 1.95D and Kh = 0.65D) were considered through the entire reservoir Reservoir pressure is 4,500 kPa, initial reservoir temperature is 18°C, and all thermal properties of rock and fluids were the same for all runs, except for rock thermal conductivity Steam at 95% quality was injected at 235°C For comparison purposes with the results in the literature, the Fast-SAGD models comprised two complete SAGD well pairs and two cyclic steam stimulation (CSS) wells, whereas two well pairs were used for pure SAGD models The cumulative oil is for a 10year period as the response variable to measure the production performance Economic model The economic model was built based on the previous discussion in the Canadian National Energy Board reports (2006) The cash flow method in Microsoft Excel spreadsheet is applied to calculate NPV reflecting property depreciation and 10% yearly interest rate during 10 years of the production phase The input parameters of the economic model include yearly outcomes from cumulative oil production, steam injection rate, and amount of water produced from CMG’Start simulation result The average prices of heavy oil and gas are 70$/bbl and 3.8$/mcf, respectively Drilling and completion costs are 3.0 mm$ of an SAGD well pair as capital investments, and 1.5 mm$ in FastSAGD of a CSS single well Total operating costs comprise the electric cost of 0.95$ per barrel of produced oil, water handling cost of 3$ per barrel, non-gas cost of 5$/bbl, and emission cost of $/bbl The production estimate is combined with initial capital, operating costs, and the rates of return on capital to calculate the NPV The operating costs depend on the change of gas price of steam injection volume, and water handling cost significantly affected the NPV BBD BBDs are experimental designs for RSM, developed by Box and Behnken in 1960 (Myers, 2008) The number of experiments (N) required for the development of BBD is defined as N = 2k (k − 1) + C0, (where k is the number of factors and C0 is the number of central points) Box–Behnken statistical screening design was used to statistically optimize the formulation parameters and evaluate the main effects, interaction effects, and quadratic effects of the SAGD performance After defining the significant factors, the optimum operation conditions are attained using the BBD The five-factor, three-level design used is suitable for exploring the quadratic response surfaces and for constructing second-order polynomial models The BBD was specifically selected since it requires fewer runs than a central composite design (CCD) in cases of five variables The cubic design is characterized by a set of points lying at the midpoint of each edge of a multidimensional cube and center point replicates (n = 1), whereas the “missing corners” help the experimenter avoid the combined factor extremes For statistical calculations, the relation between the coded values and actual values is described as follows: 420 H X NGUYEN ET AL Table The Box–Behnken experimental design with five independent variables (a) The original and coded levels of the operating variables Variables Symbol X1 X2 X3 X4 X5 Injector/producer spacing, m Injection pressure, kPa Steam rate (m3/d) Well pairs pattern spacing (m) Subcool temperature (°C) Coded levels 10 6,000 600 99 12 −1 4,500 360 48 +1 16 7,500 840 150 24 Downloaded by [University of Lethbridge] at 18:19 19 June 2016 (b)Independent variables and NPV responses Case 10 11 12 13 14 15 16 17 18 19 20 21 X1 −1 −1 0 0 0 0 −1 −1 0 0 X2 −1 −1 1 0 0 −1 −1 0 0 0 0 −1 X3 0 0 −1 −1 0 0 −1 −1 1 0 0 −1 X4 0 0 −1 −1 1 0 0 0 0 −1 −1 X5 NPV, mm$ Cumulative oil, m3 Case X1 X2 50.8125 483,227 22 60.8751 511,355 23 −1 56.3873 518,618 24 66.9626 518,566 25 −1 0 62.8737 516,507 26 0 64.9998 520,008 27 −1 0 34.5424 332,435 28 0 41.8232 468,634 29 0 −1 58.9538 521,964 30 0 −1 54.0083 551,430 31 0 49.9187 451,364 32 0 61.9856 490,349 33 −1 0 61.3925 554,438 34 0 67.9232 524,169 35 −1 0 60.8773 532,842 36 0 73.2632 523,615 37 −1 544,636 38 −1 55.5725 −1 32.4574 413,804 39 −1 60.0808 468,195 40 1 34.2614 408,068 41 0 59.8588 505,907 X3 −1 1 0 0 −1 −1 0 0 0 0 X4 0 −1 −1 1 0 0 0 0 −1 −1 1 X5 NPV, mm$ Cumulative oil, m3 64.6547 520,116.36 62.2761 503,246.03 66.2335 524,112.37 59.6317 509,017.83 73.3699 521,710.40 31.1976 417,269.99 45.2248 459,269.14 −1 64.1218 534,117.01 −1 52.3599 549,654.29 59.5884 480,029.31 63.6248 473,658.69 −1 50.3114 570,379.42 −1 63.0476 545,944.61 60.4623 522,673.78 67.7839 481,762.79 59.2091 492,128.29 66.1866 520,670.27 27.3353 358,157.81 39.8326 446,623.30 71.2289 522,096.98 Xi ẳ Ai A0 ị=A (1) where Xi is a coded value of the variable, Ai is the actual value of the variable, A0 is the actual value of the Ai at the center point, and ΔA is the step change of the variable A design matrix comprising 41 experimental runs was constructed for five operating parameters The nonlinear computer-generated quadratic model is given as y ¼ ỵ k X iẳ1 i Xi ỵ k X iẳ1 ii Xi2 ỵ X ij Xi Xj ỵ (2) i 0.05), the final predictive equation obtained is as follows: Downloaded by [University of Lethbridge] at 18:19 19 June 2016 NPV ẳ 71:23 ỵ 5:46X1 ỵ 2:94X2 13:45X4 þ 1:67X5 À 6:96X2 À 16:7X4 À 8:37X5 ỵ 4:25X2 X5 ỵ 3:95X3 X5 a The order ranking of factors affecting on NPV b Effects of operating parameters on NPV Figure Sensitivity analysis (4) ENERGY SOURCES, PART B: ECONOMICS, PLANNING, AND POLICY 423 Effect of injector to producer spacing The vertical well spacing between injection and production wells is the most important factor in determining the oil production rate Preheating periods depend on IPS and oil viscosity The more viscous the oil and the larger the IPS are, the longer the preheating period is The sensitivity analysis results showed that there was a nonlinear relationship between IPS and NPV in Figure 1b The NPV was accelerating with an increased IPS from to 13 m, and reached a peak at an IPS of 14 m, and then NPV slightly decreased A preheating period of 80 days for an IPS of 14 m is economically adequate for successful SAGD performance Downloaded by [University of Lethbridge] at 18:19 19 June 2016 Effect of WPS The proper WPS is considered a key parameter not only for energy efficiency but also for drilling cost, affecting the oil recovery factor The distance between SAGD well pairs depends on the reservoir thickness and permeability In this study, NPV only increased quickly when WPS couldn’t be applied for less than 50 m and larger than 100 m, meaning that the WPS ranged from 50 to 85 m The highest NPV indicated that the best design of WPS should be selected in a small range of 78–85 m (Figure 1b) Effect of steam injection pressure The control of IP plays an important role in the operation process The effectiveness of IP optimization lies in the economic performance of lowered cumulative steam-oil ratio (CSOR) and increased cumulative oil production (Xia Bao, 2010) Additionally, less natural gas and water usage ensures that the technique is more energy efficient and environmentally friendly The steam IP was investigated from 4,500 to 7,500 kPa, but the operating pressure in the vicinity of 6,300 kPa was the most appropriate to achieve the highest NPV (Figure 1b) Effect of steam injection rate Steam injection rate is operated in the range of 360–840 m3/d As the steam injection rate is increased, bitumen production increases, but the CSOR also increases due to the low thermal efficiency, leading to higher operating costs Effect of Strap Strap is called steam trap control, which is very important in SAGD as well as Fast-SAGD to prevent or reduce steam production from the reservoir (Shin and Polikar, 2007) Steam trap control is the way to maintain the producing fluid’s temperature just below the saturation temperature of the steam Values in the range of 0–24°C have been applied to screen for all cases of numerical simulations Numerical simulations indicated that the change of subcool was complicated depending on IP and the amount of injected steam The result suggested that subcool in the vicinity of 8°C was proper for an SAGD operation in the Peace River region (Figure 1b) Optimization of operating conditions by RSM Response surface optimization is more advantageous than the traditional single parameter optimization in that it saves time, space, and raw material Response surfaces were plotted to study the effects of parameters and their interactions on NPV Three-dimensional response surface plots and twodimensional contour plots, as presented in Figure 2, are very useful to see the interaction effects of the factors on the NPV responses The authors recognized that the suitability of the operating Downloaded by [University of Lethbridge] at 18:19 19 June 2016 424 H X NGUYEN ET AL Figure Response surface plots conditions to maximize the NPV was the red smallest region, where the maximum NPV reaches over 81 $mm The optimal conditions determined a WPS of 78 m, steam injection rate of 550 m3/d, IP spacing of 14 m, IP of 6,350 kPa, and subcool of 5°C Among the five main operation variables, the most significant factors affecting the SAGD performance were WPS and Strap according to the regression coefficients significance of the quadratic polynomial model and gradient of slope in the three-dimensional response surface plot Validation of the models In order to validate the adequacy of the model equations (Eq (4)), a verification experiment was carried out under the optimal conditions: with IPS 14 m, IP 6,350 kPa, MSIR 550 m3/d, WPS 78 m, and Strap 5°C Under the optimal conditions, the model predicted a maximum response of 81 $mm To ensure the predicted result was not biased toward the practical value, experimental rechecking was performed using this deduced optimal condition within the 95% confidence intervals The total of cumulative oil produced from the reservoir was about 564,422 m3 in the simulated operation of the SAGD process (Table 2c and Figure 3b) The oil production rate reached a peak with 147,159 m3 in the first year, and then dramatically reduced until the end of the 10th year of operation These outcomes were taken into account for the economic model to estimate an NPV of 80.55$ mm as the highest NPV among the experimental cases in Table 1b The results of analysis indicated that the experimental values were in good agreement with the predicted ones, and also suggested that the models of Eq (4) are satisfactory and accurate Optimization for Fast-SAGD process The Fast-SAGD models comprised two full SAGD well pairs and two CSS wells, uses offset wells, which are placed horizontally about 50 m away from the SAGD producer and each offset well (Polikar, 2000) These offset wells are operated alternatively as injector and producer When the ENERGY SOURCES, PART B: ECONOMICS, PLANNING, AND POLICY 465d 1095d 100d 913d 1825d 425 3650d a.SAGD1 model (Base case) 1460d 3650d b.SAGD2 model (Box-Behnkendesign) Downloaded by [University of Lethbridge] at 18:19 19 June 2016 800d 1085d 1460d 3650d c.Fast-SAGD model Figure The growth of steam chamber in SAGD and Fast-SAGD processes steam chamber reaches the top of the reservoir after the SAGD operation has begun, the CSS operation starts at the first offset well SAGD well design: The operating conditions of SAGD well pairs in Fast-SAGD are also similar to those in the SAGD system, but the SAGD wells pattern has a spacing of 150 m (Shin and Polikar, 2007) This result is the most suitable for Bluesky formation because of high cumulative oil in an earlier production period and the relatively low value of CSOR Offset well design: proposed that the most favorable operating conditions for Peace River reservoir, which is thin and moderately permeable, give offset well spacing of 38 m, with a maximum IP of 8,000 kPa, a maximum steam injection rate of 800 m3/d, and steam IP of 8,000 kPa at the offset well, CSS startup time of 1.5 years (Table 2c) Growth steam chamber performance and the amount of oil recovery in the Fast-SAGD process is shown in Figure 3c Comparison of economic efficiency of SAGD and Fast-SAGD processes The optimal point of BBD is called the SAGD2 model For the SAGD1 base case, Shin proposed the most favorable SAGD operating conditions for Peace River reservoirs: the proper preheating period for IP spacing of 10 m is 150 days, steam injection rate of 600 m3/d at IP of 4,500 kPa, subcool of 5°C, and WPS of 80 m (Figure 3a) Simulation results indicated that cumulative oil for Fast-SAGD process does not significantly increase and even NPV is the lowest among the mentioned SAGD cases In addition, cumulative oil recovery of the SAGD1 base case is higher than those of SAGD2 and Fast-SAGD cases, besides having the lowest CSOR (Table 2c and Figure 4b) However, from the economic point of view, the SAGD2 model (BBD) achieved the highest NPV, with the predicted values agreeing with the experimental values reasonably well with R2 close to 1.0 and Q2 of 0.88 for NPV response; however, the NPV of the Fast-SAGD process is the lowest because of the increasing capital cost for additional offset wells Actually, the difference of 10 kPa between steam IP and reservoir pressure is not sufficient to increase the NPV for both Fast-SAGD and base case SAGD1 operations Increased oil recovery is a necessary condition to increase profits, however oil and gas prices should be considered Downloaded by [University of Lethbridge] at 18:19 19 June 2016 426 H X NGUYEN ET AL Figure The production performance of SAGD and Fast-SAGD processes in Peace River region at each period for operating SAGD projects In this case, the selection of the Fast-SAGD process was noneconomic because the amount of oil recovery would not be enough to compensate for the additional cost of CSS wells Thus, the conventional SAGD process still applies commonly in field operation with its economic efficiency, especially the use of BBD is a new approach to obtain the maximum economic gain in SAGD operation design Conclusions - The response surface method proved to be a useful and powerful tool in developing optimum conditions The statistical analysis based on a BBD showed that an IPS of 14m, IP of 6,350 kPa, steam injection rate of 550 m3/d, Strap of 5°C, and spacing between two well pairs of 78 m were the best operating conditions to maximize the NPV Under the most suitable conditions, maximum ENERGY SOURCES, PART B: ECONOMICS, PLANNING, AND POLICY 427 NPV of 80.55 $mm can be achieved The predicted values matched the experimental values reasonably well with R2 of 0.974 and Q2 of 0.884 for NPV response - The results evidenced that the difference of 10 kPa between steam IP and reservoir pressure is not enough to increase the NPV for both Fast-SAGD and the base case of SAGD1 operations The production performances of SAGD1 base case and Fast-SAGD process have the same CSOR value, but cumulative oil production is the highest in the SAGD1 process However, the NPV of the SAGD2 operation process was the maximum - Compared to conventional SAGD, the Fast-SAGD process was insignificantly incremental in bitumen recovery as well as economic efficiency in Peace River region Acknowledgments Downloaded by [University of Lethbridge] at 18:19 19 June 2016 The authors wish to thank Computer Modelling Group Ltd and Schlumberger K.K for the encouragement in writing this paper Funding Financial support for this work is gratefully acknowledged from the Ministry of Knowledge Economy (MKE) and Korea Institute of Energy Technology Evaluation and Planning: ETI Project (KETEP) References Bao, X., Chen, Z., Wei, Y., and Dong, C 2010 Numerical simulation and optimization of the SAGD process in Surmont Oil sands lease Paper SPE 137579 Presented at Abu Dhabi International Petroleum Exhibition& Conference, UAE, and 1–4 Nov 2010 Butler, R M 2001 Some recent development in SAGD J Can Petrol Technol Distinguished Author Ser 40:18–22 Canadian National Energy Board 2006 Canada’s Oil Sands Opportunities and Challenges to 2015: An Update Available at: https://www.neb-one.gc.ca/nrg/sttstc/crdlndptrlmprdct/rprt/archive/pprtntsndchllngs20152006/ pprtntsndchllngs20152006-eng.pdf Gong J., and Polikar, M 2002 Fast SAGD and geomechnical mechanism Paper CIPC 2002-163 Presented at the Canadian International Petroleum Conference, Calgary, Canada, 11–13 June Myers, R H 2008 Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Ed New York: John Wiley and Sons, pp 13–135 Polikar, M 2000 Fast SAGD: Half the wells and 30% less steam Paper SPE 65509 at the International Conference on Horizontal Well Technology, Calgary, Canada, 6–8 November Shin H., and Polikar, M 2005 New economic indicator to evaluate SAGD performance Paper SPE 94024 presented at the SPE Western Regional Meeting, Irvine, CA, USA, 30 Mar Shin, H., and Polikar, M 2007 Review of reservoir parameters to optimize SAGD and Fast-SAGD operating conditions J Can Petrol Technol 46:35–41 ... important role in the operation process The effectiveness of IP optimization lies in the economic performance of lowered cumulative steam -oil ratio (CSOR) and increased cumulative oil production... PART B: ECONOMICS, PLANNING, AND POLICY 2016, VOL 11, NO 5, 418–427 http://dx.doi.org/10.1080/15567249.2011.626015 Economic optimization for operation options in thermal oil recovery process. .. mechanism drain into the producer The operation technical issues play an important role in increasing oil recovery and reducing the amount of steam injection However, economic risks associated with