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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.
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Trang 2Economic optimization for operation options in thermal oil
recovery process
Huy X Nguyena,b, Wisup Baea, Dung Q Tab, Chung Taemoona, and Yunsun Parkc
a Sejong University, Gwangjin-ku, Seoul, Korea; b Faculty 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; c Myongji Univesity, Gyeonggi-Do, Korea
ABSTRACT
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
sur-face 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.
KEYWORDS
Box –Behnken design; NPV; optimization; response surface methodology; SAGD
1 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, VOL 11, NO 5, 418 –427
http://dx.doi.org/10.1080/15567249.2011.626015
Trang 3might 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
2 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 Fast-SAGD 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 10-year period as the response variable to measure the production performance
3 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 Fast-SAGD 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 1
$/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
4 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 poly-nomial 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:
Trang 4Xi¼ Að i A0Þ=ΔA (1) where Xiis a coded value of the variable, Aiis 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
y¼ β0þXk
i¼1βiXiþXk
i¼1βiiXi2þX
where Y is the measured response associated with each factor-level combination, β0 is an intercept, βi is the regression coefficient computed from the observed experimental values of Y, and Xi is the coded level of the independent variables The terms XiXj and Xi2 represent the interaction and quadratic terms, respectively The dependent and independent variables are selected along with their low, medium, and high levels, which were selected based on the results from preliminary experimentation (Table 1a) The operating conditions of injector producer spacing (IPS), injection pressure (IP), maximum steam injection rate (MSIR), subcool temperature (Strap), and well pattern spacing (WPS) used to prepare the 41 formulations and the respective observed responses are given inTable 1b
5 Results and discussions
The experimental design model has five operating variables based on BBD The advantages of BBD permit the use of relatively few combinations of variables for determining the second-order function
Table 1 The Box –Behnken experimental design with five independent variables.
(a) The original and coded levels of the operating variables
Variables Symbol Coded levels
Injector/producer spacing, m X 1 4 10 16 Injection pressure, kPa X 2 4,500 6,000 7,500 Steam rate (m 3 /d) X 3 360 600 840 Well pairs pattern spacing (m) X 4 48 99 150 Subcool temperature (°C) X 5 0 12 24 (b)Independent variables and NPV responses
Case X 1 X 2 X 3 X 4 X 5 NPV, mm$ Cumulative oil, m 3 Case X 1 X 2 X 3 X 4 X 5 NPV, mm$ Cumulative oil, m 3
1 −1 −1 0 0 0 50.8125 483,227 22 0 1 −1 0 0 64.6547 520,116.36
2 1 −1 0 0 0 60.8751 511,355 23 0 −1 1 0 0 62.2761 503,246.03
3 −1 1 0 0 0 56.3873 518,618 24 0 1 1 0 0 66.2335 524,112.37
4 1 1 0 0 0 66.9626 518,566 25 −1 0 0 −1 0 59.6317 509,017.83
5 0 0 −1 −1 0 62.8737 516,507 26 1 0 0 −1 0 73.3699 521,710.40
6 0 0 1 −1 0 64.9998 520,008 27 −1 0 0 1 0 31.1976 417,269.99
7 0 0 −1 1 0 34.5424 332,435 28 1 0 0 1 0 45.2248 459,269.14
8 0 0 1 1 0 41.8232 468,634 29 0 0 −1 0 −1 64.1218 534,117.01
9 0 −1 0 0 −1 58.9538 521,964 30 0 0 1 0 −1 52.3599 549,654.29
10 0 1 0 0 −1 54.0083 551,430 31 0 0 −1 0 1 59.5884 480,029.31
11 0 −1 0 0 1 49.9187 451,364 32 0 0 1 0 1 63.6248 473,658.69
12 0 1 0 0 1 61.9856 490,349 33 −1 0 0 0 −1 50.3114 570,379.42
13 −1 0 −1 0 0 61.3925 554,438 34 1 0 0 0 −1 63.0476 545,944.61
14 1 0 −1 0 0 67.9232 524,169 35 −1 0 0 0 1 60.4623 522,673.78
15 −1 0 1 0 0 60.8773 532,842 36 1 0 0 0 1 67.7839 481,762.79
16 1 0 1 0 0 73.2632 523,615 37 0 −1 0 −1 0 59.2091 492,128.29
17 0 0 0 −1 −1 55.5725 544,636 38 0 1 0 −1 0 66.1866 520,670.27
18 0 0 0 1 −1 32.4574 413,804 39 0 −1 0 1 0 27.3353 358,157.81
19 0 0 0 −1 1 60.0808 468,195 40 0 1 0 1 0 39.8326 446,623.30
20 0 0 0 1 1 34.2614 408,068 41 0 0 0 0 0 71.2289 522,096.98
21 0 −1 −1 0 0 59.8588 505,907
Trang 5A total of 41 experiments were required to be performed to calculate 21 coefficients of the second-order polynomial equation FromTable 1bit is seen that cumulative oil of case 33 was the highest; however, its NPV was lower than other cases due to the change of WPS, Strap, and IPS The highest NPV of case 26 was obtained under the design condition of IPS 16 m, IP 6,000 kPa, MSIR 600 m3/d, WPS 48m, and Strap 12°C The coefficient of the response quadratic equation was estimated using multiple regression analysis technique included in the RSM The quadratic model thus obtained is as follows:
NPV¼ 71:23 þ 5:46X1þ 2:94X2þ 0:65X3 13:45X4þ 1:67X5 3:28X1 6:96X2
2:38X3 16:7X4 8:37X5 þ 0:13X1X2þ 1:46X1X3þ 0:07X1X4 1:35X1X5
0:21X2X3þ 1:38X2X4þ 4:25X2X5þ 1:29X3X4þ 3:95X3X5 0:68X4X5 (3) Statistical analysis of the model was performed to evaluate the analysis of variance (ANOVA)
in Table 2a The R2 value closer to 1 denotes better correlation between the observed and predicted values The higher values of R2 (0.971) and adjusted R2 (0.942) also indicated the efficiency of the model, suggesting that 97.1 and 94.2% variation could be accounted for by the model equation, respectively At the same time, a very low value of coefficient of residual
Table 2 ANOVA analysis, response quadratic function, and economic performance of SAGD and Fast-SAGD operating processes (a) ANOVA analysis
Total 41 1E+05 3333
Constant 1 1E+05 1E+05
Total Corrected 40 5672 141.8 11.9077 Regression 20 5507 275.4 33.43 0 16.5937
N = 41, DF = 20 Q2= 0.884 R2=0.971, R2Adj =0.942 RSD = 2.87 (b) Regression coefficients of the predicted quadratic polynomial model
NPV Estimate Standard Error P Constant 71.2289 2.87001 1.69E-16
X 1 5.46111 0.717503 2.49E-07
X 2 2.93824 0.717503 0.000563
X 3 0.656393 0.717503 0.371179
X 4 −13.4531 0.717503 3.69E-14
X 5 1.67957 0.717503 0.029713
X 1 −3.28508 1.63091 0.057629
X 2 −6.95445 1.63091 0.000379
X 3 −2.37755 1.63091 0.160418
X 4 −16.6954 1.63091 2.13E-09
X 1 X 2 0.128175 1.43501 0.929715
X 1 X 3 1.4638 1.43501 0.319879
X 1 X 4 0.07225 1.43501 0.960346
X 1 X 5 −1.35365 1.43501 0.356781
X 2 X 3 −0.20963 1.43501 0.88532
X 2 X 4 1.37995 1.43501 0.347718
X 2 X 5 4.25309 1.43501 0.007675
X 3 X 4 1.28868 1.43501 0.379853
X 3 X 5 3.94957 1.43501 0.012284
X 4 X 5 −0.67607 1.43501 0.642648 (c) Economic performance of SAGD and Fast-SAGD operating processes
Cases IPS, m)\ IP, kPa MSIR, m 3 /d WPS, m Subcool, °C Cumulative oil, m 3 NPV, $mm Fast-SAGD 10 4510 (SAGD) 400 (SAGD) 150(SAGD) 5 558,293 72.41
8000(CSS) 800 (CSS) 38(CSS) SAGD 1 10 8000 600 80 5 568,894 75.42 SAGD 2 14 4500 550 78 5 564,422 80.55
Trang 6standard deviation (RSD = 2.87) clearly indicated a high degree of precision and reliability of the experimental values and in relation to the power of prediction, Q2= 0.88
The regression coefficients of Eq (3) are listed inTable 2bwith standard errors and P-values The P-values were used as a tool to check the significance of each coefficient with confidence level 95%, which in turn might indicate the pattern of interactions between variables The independent variables studied (X1, X2, X4, X5) and three quadratic terms (X2, X4 , X5 ) significantly affected the NPV The sensitivity analysis also showed that the single and interaction parameters of WPS most significantly influenced the amount of oil recovery as well as NPV, and then also Strap and IP (Figure 1a) Neglecting the insignificant terms (P > 0.05), the final predictive equation obtained is as follows:
NPV¼ 71:23 þ 5:46X1þ 2:94X2 13:45X4þ 1:67X5 6:96X2 16:7X4 8:37X5
a The order ranking of factors affecting on NPV
b Effects of operating parameters on NPV
Figure 1 Sensitivity analysis.
Trang 7Effect 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 inFigure 1b The NPV was accelerating with an increased IPS from 4 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
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 optimiza-tion 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 two-dimensional contour plots, as presented inFigure 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
Trang 8conditions 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 $mm
To ensure the predicted result was not biased toward the practical value, experimental rechecking was performed using this deduced optimal condition within the 95% confidence intervals The total
of cumulative oil produced from the reservoir was about 564,422 m3in the simulated operation of the SAGD process (Table 2candFigure 3b) The oil production rate reached a peak with 147,159 m3
in the first year, and then dramatically reduced until the end of the 10th year of operation These outcomes were taken into account for the economic model to estimate an NPV of 80.55$ 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
Figure 2 Response surface plots.
Trang 9steam 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 inFigure 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 2candFigure 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 R2close to 1.0 and Q2of 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
a.SAGD1 model (Base case)
b.SAGD2 model (Box-Behnkendesign)
c.Fast-SAGD model
Figure 3 The growth of steam chamber in SAGD and Fast-SAGD processes.
Trang 10at 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
6 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
Figure 4 The production performance of SAGD and Fast-SAGD processes in Peace River region.