An Efficient Optimization Work Flow for Field-Scale In-Situ Upgrading Developments
- Guohua Gao (Shell Global Solutions US Inc.) | Jeroen C. Vink (Shell Global Solutions US Inc.) | Faruk O. Alpak (Shell International E&P Inc.) | Weijian Mo (Shell (China) P&T Limited)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2015
- Document Type
- Journal Paper
- 701 - 716
- 2015.Society of Petroleum Engineers
- Field development, In-Situ Upgrading, Optimization, Response surface model, Economic evaluation
- 4 in the last 30 days
- 321 since 2007
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In-situ upgrading process (IUP) is an attractive technology for developing unconventional extraheavy-oil reserves. Decisions are generally made on field-scale economics evaluated with dedicated commercial tools. However, it is difficult to conduct an automated IUP optimization process because of unavailable interface between the economic evaluator and commercial simulator/optimizer, and because IUP is such a highly complex process that full-field simulations are generally not feasible.
In this paper, we developed an efficient optimization work flow by addressing three technical challenges for field-scale IUP developments. The first challenge was deriving an upscaling factor modeled after analytical superposition formulation; proposing an effective method of scaling up simulation results and economic terms generated from a single-pattern IUP reservoir-simulation model to field scale; and validating this approach numerically. The second challenge was proposing a response-surface model (RSM) of field economics to analytically compute key field economical indicators, such as net present value (NPV), by use of only a few single-pattern economic terms together with the upscaling factor, and validating this approach with a commercial tool. The proposed RSM approach is more efficient, accurate, and convenient because it requires only 15–20 simulation cases as training data, compared with thousands of simulation runs required by conventional methods. The third challenge is developing a new optimization method with many attractive features: well-parallelized, highly efficient and robust, and with a much-wider spectrum of applications than gradient-based or derivative-free methods, applicable to problems without any derivative, with derivatives available for some variables, or with derivatives available for all variables.
This work flow allows us to perform automated field IUP optimizations by maximizing a full-field economics target while honoring all field-level facility constraints effectively. We have applied the work flow to optimize the IUP development of a carbonate heavy-oil asset. Our results show that the approach is robust and efficient, and leads to development options with a significantly improved field-scale NPV. This work flow can also be applied to other kinds of pattern-based field developments of shale gas and oil, and thermal processes such as steam-drive or steam-assisted gravity drainage.
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Alpak, F.O., Vink, J.C., Gao, G., et al. 2013. Techniques for Effective Simulation, Optimization, and Uncertainty Quantification of the In-situ Upgrading Process. J. Unconventional Oil Gas Resour. 3–4 (December): 1–14. http://dx.doi.org/10.1016/j.juogr.2013.09.001.
Audet, C. and Dennis, J.E. Jr. 2006. Mesh Adaptive Direct Search Algorithms for Constrained Optimization. SIAM J. Optim. 17 (1): 188–217. http://dx.doi.org/10.1137/040603371.
Bailey, W.J. and Couet, B. 2005. Framework for Field Optimization for Maximizing Asset Value. SPE Res Eval & Eng 8 (1): 7–21. SPE-87026-PA. http://dx.doi.org/10.2118/87026-PA.
Broyden, C.G. 1967. Quasi-Newton Methods and Their Application to Function Minimization. Math. Comput. 21 (1967): 368–381. http://dx.doi.org/10.1090/S0025-5718-1967-0224273-2.
Broyden, C.G. 1970. The Convergence of a Class of Double-Rank Minimization Algorithms. IMA J. Appl. Math. 6 (3): 76–90. http://dx.doi.org/10.1093/imamat/6.3.222.
Carter, J.N. 2004. Using Bayesian Statistics to Capture the Effects of Modeling Errors in Inverse Problems. Mathematical Geology 36 (2): 187–216. http://dx.doi.org/10.1023/B:MATG.0000020470.51595.6d.
Chen, C., Jin, L., Gao, G., et al. 2012. Assisted History Matching Using Three Derivative-Free Optimization Algorithms. Presented at the SPE Europec/EAGE Annual Conference, 4–7 June, Copenhagen, Denmark. SPE-154112-MS. http://dx.doi.org/10.2118/154112-MS.
Chen, Y., and Oliver, D.S. 2009. Ensemble-Based Closed-Loop Optimization Applied to Brugge Field. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2–4 February. SPE-118926-MS. http://dx.doi.org/10.2118/118926-MS.
Chen, Y., Oliver, D.S. and Zhang, D. 2008. Efficient Ensemble-Based Closed-Loop Optimization. Presented at the SPE Symposium on Improved Oil Recovery, Tulsa, Oklahoma, 20–23 April. SPE-112873-MS. http://dx.doi.org/10.2118/112873-MS.
Conn, A.R., Gould, N.I.M. and Toint, P.L. 2000. Trust-Region Methods. Philadelphia, Pennsylvania: SIAM.
Couet, B., Burrigde, R. and Wilkinson, D. 2000. Optimization Under Reservoir and Financial Uncertainty. Oral presentation given at the 2000 European Conference on the Mathematics of Oil Recovery, Baveno, Italy, 5–8 September.
Dantzig, G.B. 1949. Programming in a Linear Structure. Econometrica 17 (1): 73–74. http://www.jstor.org/stable/1912134.
Fan, Y., Durlofsky, L.J. and Tchelepi. 2010. Numerical Simulation of the In-Situ Upgrading of Oil Shale. SPE J. 15 (2): 368–381. SPE-118958-PA. http://dx.doi.org/10.2118/118958-PA.
Fletcher, R. 1970. A New Approach to Variable Metric Methods. Comput. J. 13 (3): 317–322. http://dx.doi.org/10.1093/comjnl/13.3.317.
Goldfarb, D. 1970. A Family of Variable Metric Methods Derived by Variational Means. Math. Comput. 24 (109): 23–26. http://www.jstor.org/stable/2004873.
Holland, J.H. 1975. Adaptation in Natural and Artificial Systems. Ann Arbor, Michigan: The University of Michigan Press.
Hooke, R. and Jeeves, T.A. 1961. “Direct Search” Solution of Numerical and Statistical Problems. J. Assoc. Comput. Mach. 8 (2): 212–229. http://dx.doi.org/10.1145/321062.321069.
Hyndman, A.W. and Luhning, R.W. 1991. Recovery and Upgrading of Bitumen and Heavy Oil in Canada. J Can Pet Technol 30 (2): 63–73. PETSOC-91-02-03. http://dx.doi.org/10.2118/91-02-03.
Isebor, O.J., Ciaurri, D.E. and Durlofsky, L.J. 2014. Generalized Field-Development Optimization with Derivative-Free Procedures. SPE J. 19 (5): 891–908. SPE-163631-PA. http://dx.doi.org/10.2118/163631-PA.
Karanikas, J.M. 2012. R&D Grand Challenges: Unconventional Resources: Cracking the Hydrocarbon Molecules In Situ. J Pet Technol 68–75. SPE-163061-MS. http://dx.doi.org/10.2118/163061-MS.
Kennedy, J. and Eberhart, R. 1995. Particle Swarm Optimization. Proc., IEEE International Conference on Neural Networks, Perth, Australia, 27 November–1 December, Vol. 4, 1942–1945. http://dx.doi.org/10.1109/ICNN.1995.488968.
Khalfan, H.F., Byrd, R.H. and Schnabel, R.B. 1993. A Theoretical and Experimental Study of the Symmetric Rank One Update. SIAM J. Optim. 3 (1): 1–24. http://dx.doi.org/10.1137/0803001.
Kirkpatrick, S., Gelatt, C.D. Jr. and Vecchi, M.P. 1983. Optimization by Simulated Annealing. Science 220 (4598): 671–680. http://dx.doi.org/10.1126/science.220.4598.671.
Lewis, R.M. and Torczon, V.J. 2000. Pattern Search Methods for Linearly Constrained Minimization. SIAM J. Optim. 10 (3): 917–941. http://dx.doi.org/10.1137/S1052623497331373.
Lewis, R.M., Torczon, V.J. and Trosset, M.W. 2000. Direct Search Methods: Then and Now. J. Comput. Appl. Math. 124 (1–2): 191–207. http://dx.doi.org/10.1016/S0377-0427(00)00423-4.
LINDO Systems. 2014. LINDOTM Software for Integer Programming, Linear Programming, Nonlinear Programming, Stochastic Programming, Global Optimization. www.lindo.com.
Nash, S.G. and Sofer, A. 1996. Linear and Nonlinear Programming. Blacklick, Ohio: McGraw-Hill Science/Engineering/Math.
Onwunalu, J.E. and Durlofsky, L.J. 2010. Application of a Particle Swarm Optimization Algorithm for Determining Optimum Well Location and Type. Computat. Geosci. 14 (1): 183–198. http://dx.doi.org/10.1007/s10596-009-9142-1.
Pintér Consulting Service, Inc. 2014. Nonlinear Systems Modeling and Optimization Software Development and Applications. www.pinterconsulting.com.
Powell, M.J.D. 2004. Least Frobenius Norm Updating of Quadratic Models That Satisfy Interpolation Conditions. Math. Program. B 100 (1): 183–215. http://dx.doi.org/10.1007/s10107-003-0490-7.
Raghuraman, B., Couët, B., Savundararaj, P. et al. 2003. Valuation of Technology and Information for Reservoir Risk Management. SPE Res Eval & Eng 6 (5): 307–316. SPE-86568-PA. http://dx.doi.org/10.2118/86568-PA.
Sarma, P., Chen, W.H., Durlofsky, L.J., et al. 2006. Production Optimization with Adjoint Models Under Nonlinear Control-State Path Inequality Constraints. Presented at the Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands, 11–13 April. SPE-99959-MS. http://dx.doi.org/10.2118/99959-MS.
Shanno, D.F. 1970. Conditioning of Quasi-Newton Methods for Function Minimization. Math. Comput. 24 (111): 647–656. http://www.jstor.org/stable/2004840.
Simmer, M. and Thompson, D.C. 1977. Bitumen Upgrading—Its Importance to the In-Situ Producer. J Can Pet Technol 40 (8): 45–53. PETSOC-77-02-05. http://dx.doi.org/10.2118/77-02-05.
Snow, R.H. 2011. In-Situ Upgrading of Bitumen and Shale Oil by RF Electrical Heating. Presented at the SPE Heavy Oil Conference and Exhibition, Kuwait City, Kuwait, 12–14 December. SPE-150694-MS. http://dx.doi.org/10.2118/150694-MS.
Vink, J.C. and Gao, G. 2014. Helical Boundary Conditions to Capture Inter-Pattern Flow in In-Situ Upgrading Process Pattern Simulation. Presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, The Netherlands, 27–29 October. SPE-170639-MS. http://dx.doi.org/10.2118/170639-MS.
Wang, C., Li, G. and Reynolds, A.C. 2009. Production Optimization in the Closed-Loop Reservoir Management. SPE J. 14 (3): 506–523. SPE-109805-PA. http://dx.doi.org/10.2118/109805-PA.
Wild, S.M. 2009. Derivative Free Optimization Algorithms for Computationally Expensive Functions. PhD dissertation, Cornell University, Ithaca, New York (January 2009).
Xu, H.H., Okazawa, N.E., Moore, R.G., et al. 2001. In Situ Upgrading of Heavy Oil. J Can Pet Technol 40 (8): 45–53. PETSOC-01-08-04. http://dx.doi.org/10.2118/01-08-04.
Zhao, H., Li, G., Reynolds, A.C., et al. 2013. Large-Scale History Matching with Quadratic Interpolation Models. Computat. Geosci. 17 (1): 117–138. http://dx.doi.org/10.1007/s10596-012-9320-4.