Well pattern optimization based on StoSAG algorithm
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Abstract
The well pattern optimization in the oilfield is challenging and intricate work due to the heterogeneity of the permeability and viscosity. Traditionally, the well pattern optimization is conducted by comparing the results of several plans manually designed by the reservoir engineer, which is difficult to obtain the optimal well pattern. To address these challenges, a framework that integrates a reservoir simulator into the StoSAG algorithm is proposed. The well pattern operators proposed by Onwunalu and Durlofsy are applied to obtain the variations of the well pattern and used as the optimization variables. During the framework, the optimization variables are continuously adjusted by the StoSAG algorithm in order to obtain the optimal one which maximize the objective function value. The framework is applied to a synthetic reservoir. The results show that the StoSAG algorithm can be successfully applied in the well pattern optimization and remarkably improve the development effect. This method can be widely used in new oilfield development plan and offer reference for well pattern deployment.
Cited as: Wang, S., Li, Z., Wang, S., Han, X. Well pattern optimization based on StoSAG algorithm. Advances in Geo-energy Research, 2018, 2(1): 103-112, doi: 10.26804/ager.2018.01.09
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An, G., Xu, J., Zhou, W., et al. Well pattern optimization in offshore water drive heavy oilfields with complicated fluvial facies. China Offshore Oil Gas 2013, 25(3): 28-31. (in Chinese)
Awotunde, A.A. On the joint optimization of well placement and control. Paper SPE 172206 Presented at the SPE Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, 21-24 April, 2014.
Bangerth, W., Klie, H., Wheeler, M.F., et al. On optimization algorithms for the reservoir oil well placement problem. Comput. Geosci. 2006, 10(3): 303-319.
Beckner, B.L., Song, X. Field development planning using simulated annealing-optimal economic well scheduling and placement. Paper SPE 30650 Presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, 22-25 October, 1995.
Brouwer, D.R., Jansen, J.D. Dynamic water flood optimization with smart wells using optimal control theory. SPE J. 2004, 9(4): 391-402.
Chen, B., Fonseca, R.M., Leeuwenburgh, O., et al. Minimizing the risk in the robust life-cycle production optimization using stochastic simplex approximate gradient. J. Pet. Sci. Eng. 2017, 153: 331-344.
Chen, Y., Oliver, D.S., Zhang, D. Efficient ensemble-based closed-loop production optimization. SPE J. 2008, 14(4): 4248-4252.
Chen, Y., Oliver, D.S. Ensemble-based closed-loop optimiza-tion applied to Brugge field. SPE Reserv. Eval. Eng. 2010, 13(1): 56-71.
Do, S.T., Forouzanfar, F., Reynolds, A.C. Estimation of optimal well controls using the augmented Lagrangian function with approximate derivatives. IFAC Proceedings Volumes 2012, 45(8): 1-6.
Emerick, A., Almeida, L., Szwarcman, D., et al. Well placement optimization using a genetic algorithm with nonlinear constraints. Paper SPE 118808 Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2-4 February, 2009.
Essen, G.M.V., Hof, P.M.J.V.D., Jansen, J.D. Hierarchical long-term and short-term production optimization. SPE J. 2011, 16(1): 191-199.
Essen, G.M.V., Zandvliet, M.J., Hof, P.M.J.V.D., et al. Robust waterflooding optimization of multiple geological scenarios. SPE J. 2009, 14(1): 202-210.
Feng, Q., Zhang, J., Zhang, X., et al. Optimizing well placement in a coalbed methane reservoir using the particle swarm optimization algorithm. Int. J. Coal Geol. 2012, 104: 34-45.
Fonseca, R., Leeuwenburgh, O., Rossa E.D., et al. Ensemble-based multiobjective optimization of on-off control devices under geological uncertainty. SPE Reserv. Eval. Eng. 2015, 18(4): 554-563.
Fonseca, R.R.M., Chen, B., Jansen, J.D., et al. A stochastic simplex approximate gradient (StoSAG) for optimization under uncertainty. Int. J. Numer. Methods Eng. 2017, 109(13): 1756-1776.
Guyaguler, B. Optimization of well placement and assessment of uncertainty. California, Stanford University, 2002.
Isebor, O.J., Durlofsky, L.J., Ciaurri, D.E. A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls. Comput. Geosci. 2014, 18(3): 463-482.
Li, L., Jafarpour, B., Mohammad-Khaninezhad, M.R. A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty. Comput. Geosci. 2013, 17(1): 167-188.
Montleau, P.D., Cominelli, A., Neylon, K., et al. Production optimization under constraints using adjoint gradients. Paper No. A041 Presented at the 10th European Confer-ence on the Mathematics of Oil Recovery-Amsterdam, The Netherlands, 4-7 September, 2006.
Oliveira, D.F., Reynolds, A. An adaptive hierarchical algo-rithm for estimation of optimal well controls. SPE J. 2014, 19(5): 909-930.
Onwunalu, J. Optimization of nonconventional well placement using genetic algorithms and statistical proxy. California, Stanford University, 2006.
Onwunalu, J.E., Durlofsky, L. A new well-pattern-optimization procedure for large-scale field development. SPE J. 2011, 16(3): 594-607.
Sarma, P., Chen, W.H. Efficient well placement optimization with gradient-based algorithms and adjoint models. Paper SPE 112257 presented at the SPE Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands, 25-27 February, 2008.
Spall, J.C. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Autom. Control. 1992, 37(3): 332-341.
Spall, J.C. Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans. Aerosp. Electron. Syst. 1998, 34(3): 817-823.
Spall, J.C. Adaptive stochastic approximation by the simulta-neous perturbation method. IEEE Trans. Autom. Control. 2000, 45(10): 1839-1853.
Wang, C., Li, G., Reynolds, A.C. Production optimization in closed-loop reservoir management. SPE J. 2009, 14(3): 506-523.
Xu, Q., Jiang, W., Wang, X., et al. Optimization of fracturing parameters in well pattern with horizontal and vertical wells combined in Super-low permeability oil reservoir. Special Oil & Gas Reservoirs 2014, 21(2): 111-114. (in Chinese)
Yao, J., Wei, S., Zhang, K., et al. Constrained reservoir production optimization. Journal of China University of Petroleum 2012, 36(2): 125-129. (in Chinese)
Zhang, K., Chen, Y., Zhang, L., et al. Well pattern optimization using NEWUOA algorithm. J. Pet. Sci. Eng. 2015, 134: 257-272.
Zhang, K., Li, Y., Yao, J., et al. Theoretical research on production optimization of oil reservoirs. Acta Petrolei Sinica 2010, 31(1): 78-83. (in Chinese)
Zhao, H., Cao, L., Li, Y., et al. Production optimization of oil reservoir based on an improved simultaneous perturbation stochastic approximation algorithm. Acta Petrolei Sinica 2011, 32(6): 1031-1036. (in Chinese)
Zhao, H., Chen, C., Do, S.T., et al. Maximization of a dynamic quadratic interpolation model for production optimization. SPE J. 2013, 18(6): 1012-1025.
Zhao, H., Li, Y., Kang, Z. Robust optimization in oil reservoir production. Acta Petrolei Sinica 2013, 34(5): 947-953.
(in Chinese) Zhou, Y.Y., Li, Y., Wang, D.P. Research on water flooding effect improved by vectorial well arrangement for reservoir with permeability heterogeneity in plane. Rock and Soil Mechanics 2008, 29(1): 135-139. (in Chinese)
Zhou, Z.J., Song, H.C., Meng, L.B., et al. Numerical simula-tion of well pattern optimization in low permeability fractured reservoir-an example of Liangjing oilfield. Xinjiang Petroleum Geology 2002, 23(3): 228-230. (in Chinese)
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