Fractured Reservoirs History Matching based on Proxy Model and Intelligent Optimization Algorithms

Document Type: Research Paper

Authors

1 Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran

2 Department of Reservoir Evaluation, National Iranian South Oil Company (NISOC), Ahwaz, Iran

Abstract

   In this paper, a new robust approach based on Least Square Support Vector Machine (LSSVM) as a proxy model is used for an automatic fractured reservoir history matching. The proxy model is made to model the history match objective function (mismatch values) based on the history data of the field. This model is then used to minimize the objective function through Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA). This procedure leads to matching of history of the field in which a set of reservoir parameters is used. The final sets of parameters are then applied for the full simulation model to validate the technique. The obtained results showed that due to high speed and need for little data sets, LSSVM is the best tool to build a proxy model. Also the comparison of PSO and ICA showed that PSO is less time-consuming and more effective. 

Keywords


[1] Zhang, X., Hou, H., Wang, D., Mu, T., Wu, J. and Lu, X., (2012), “An Automatic History Matching Method of Reservoir Numerical Simulation Based on Improved Genetic Algorithm”, International Workshop on Information and Electronics Engineering (IWIEE), Procedia Engineering, Vol. 29, pp. 3924-3928.

[2] Bjorndalen, N., Kuru, E. and Schiozer, D.J., (2008), “Application of Neural Network and Global Optimization in History Matching”, Journal of Canadian Petroleum Technology, PETSOC-08-11-22-TN, Vol. 47, Issue. 11.

[3] Jurecka, F., (2007), “Robust Design Optimization Based on Metamodeling Techniques”, Ph.D. thesis, Technische Universität München, München.

[4] Lophaven, S.N., Nielsen, H.B. and Sondergaard, J., (2002), “DACE: A Mathlab Kriging Toolbox Version 2.0. Technical Report IMM-TR-2002-12”, Technical University of Denmark, Lyngby, Denmark, 1st August.

[5] Zubarev, D.I., (2009), “Pros and cons of applying proxy models as a substitute for full reservoir simulations”, SPE 124815, in Proceedings of the SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, USA.

[6] Cullick, A.S., Johnson, D. and Shi, G., (2006), “Improved and More-Rapid History Matching With a Nonlinear Proxy and Global Optimization”, SPE 101933, SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, U.S.A., 24–27 September, http://dx.doi.org/10.2118/101933-MS.

[7] Yu, T., Wilkinson, D. and Castellini, A., (2008), “Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis”, Journal of Artificial Evolution and Applications, Vol. 2008, Article ID 263108.

[8] Rammay, M.H. and Abdulraheem, A., (2014), “Automated History Matching Using Combination of Adaptive Neuro Fuzzy System (ANFIS) and Differential Evolution Algorithm”, Society of Petroleum Engineers, SPE Large Scale Computing and Big Data Challenges in Reservoir Simulation Conference and Exhibition, Istanbul, Turkey, SPE-172992-MS, 15-17 September.

[9] Maschio, C. and Schiozer, D. J., (2014), “Bayesian history matching using artificial neural network and Markov Chain Monte Carlo”, Engineering, Vol. 123, pp. 62-71.

[10] Goodwin, N., (2015), “Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods”, SPE Reservoir Simulation Symposium, 23-25 February, Houston, Texas, USA, SPE-173301-MS.

[11] He, J., Xie, J., Wen, X. and Chen, W., (2015), “Improved Proxy For History Matching Using Proxy-for-data Approach And Reduced Order Modeling”, SPE Western Regional Meeting, 27-30 April, Garden Grove, California, USA, SPE-174055-MS.

[12] Boser, B. E., Guyon, I. M. and Vapnik, V., (1992), “A training algorithm for optimal margin”. In: Haussler, D. (Ed.), Proceedings of the Annual Workshop on Computational Learning Theory. Pittsburgh, PA, ACM, New York, NY, pp. 144-152.

[13] Cortes, C. and Vapnik, V., (1995), “Support vector networks. Machine Learning”, Vol. 20, pp. 273-297.

[14] Guyon, I., Boser, B. and Vapnik, V., (1993), “Automatic capacity tuning of very large VC-dimension classifiers”. In: Hanson, S.J., Cowan, J.D., Giles, C.L. (Eds.), Advances in Neural Information Processing Systems. Morgan Kaufmann Publishers, San Mateo, CA, Vol. 5, pp. 147-155.

[15] Scho ̈lkopf, B., Burges, C. and Vapnik, V., (1995), “Extracting support data for a given task”, In: Fayyad, U.M., Uthurusamy, R. (Eds.), Proceedings of the First International Conference on Knowledge Discovery & Data Mining, Menlo Park, CA, pp. 252-257.

[16] Vapnik, V., Golowich, S. and Smola, A., (1997), “Support vector method for function approximation, regression estimation, and signal processing”, In: Mozer, M.C., Jordan, M.I., Petsche, T. (Eds.), Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 9, pp. 281-287.

[17] Smolatand Bernhard Scholkof, A. J., (2003), “A tutorial on support vector regression”.

[18] Suykens, J.A.K. and Vandewalle, J., (1999), “Neural Processing Letters”, Vol. 9, pp. 293-300.

[19] Samui, p., (2011), “Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs”, Engineering, Vol. 3, pp. 431-434.

[20] Elmabrouk, S. Kh., (2012), “Application of function approximations to reservoir engineering”, Ph.D. thesis, Department of Petroleum Engineering, University of Regina.

[21] Shi, D.F. and Nabil, N.G., (2007), “Tool wear predictive model based on least squares support vector machines”, Mechanical Systems and Signal Processing, Vol. 21, pp. 1799-1814.

[22] Kennedy, J. and Eberhart, R., (1995), “Particle Swarm Optimization”, Proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ, USA, Vol. 4, pp. 1942-1948.

[23] Riazi, S.H., Heydari, H., Ahmadpour, E., Gholami, A. and Parvizi, S., (2014), “Development of novel correlation for prediction of hydrate formation temperature based on intelligent optimization algorithms”, Journal of Natural Gas Science and Engineering, Vol. 18, pp. 377-384.

[24] Kennedy, J. and Mendes, R., (2002), “Population structure and particle swarm performance”, Proc. of IEEE Conference on Evolutionary Computation, Vol. 2, pp. 1671-1676.

[25] Hassan, R., Cohanim, B., Weck, O. and Venter, G., (2005), “A copmarison of particle swarm optimization and the genetic algorithm”, Proceedings of the 1st AIAA multidisciplinary design optimization specialist conference, 18-21 April, Austin, Texas, pp. 1-13.

[26] Martinez-Soto, R., Rodriguez, A., Castillo, O. and Aguilar, T., (2012), “Gain Optimization for Inertia Wheel Pendulum Stabilization using Particle Swarm Optimization and Genetic Algorithms” Computing, Information and Control, Vol. 8, pp. 4421-4430.

[27] Mohamed, L., Christie, M. and Demyanov, V., (2010), “Reservoir Model History Matching With Particle Swarms”, Institute of Petroleum Engineering, Heriot–Watt University, Edinburgh, UK, SPE 129152.

[28] Mokhtari, Gh., Ghanizadeh, A.J. and Ebrahimi, E., (2012), “Application of Imperialist Competitive Algorithm to Solve Constrained Economic Dispatch”, Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran, International Journal on Electrical Engineering and Informatics, Vol. 4, No. 4.

[29] Atashpaz-Gargari, E. and Lucas, C., (2007), “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition”, IEEE Congress on Evolutionary Computation, Singapore, pp. 4661- 4667.

[30] Towsyfyan, H., Adnani-Salehi, S. A., Ghayyem, M. and Mosaedi, F., (2013), “The Comparison of Imperialist Competitive Algorithm Applied and Genetic Algorithm for Machining Allocation of Clutch Assembly”, International Journal of Engineering, Vol. 26, No. 12, pp. 1485-1494.

[31] Zabalza-Mezghani, I., Manceau, E., Feraille, M. and Jourdan, A., (2004), “Uncertainty management: From geological scenarios to production scheme optimization”, Journal of Petroleum Science and Engineering, Vol. 44, pp. 11-25.

[32] Espinoza, M., Suykens, J.A.K. and De Moor, B., (2003), “Least squares support vector machines and primal space estimation”, Proceedings of the 42nd IEEE Conference on Decision and Control Maui Hawaii, USA, Vol. 4, pp. 3451-3456.

[33] Bijami, E., Abshari, R., Askari, J., Hoseinnia, S. and Farsangi, M. M., (2011), “Optimal Design of Damping Controllers for Multi-machine Power Systems Using Metaheuristic Techniques”, International Review of Electrical Engineering (IREE), Vol. 6, No. 4, pp. 1883-1894.

[34] Rajabioun, R., Hashemzadeh, F., Atashpaz-Gargari, E., Mesgari, B. and Rajaiee Salmasi, F., (2008), “Identification of a MIMO Evaporator and its Decentralized PID Controller Tuning Using Colonial Competitive Algorithm”, Proceedings of the 17th World Congress, The International Federation of Automatic Control, pp. 9952-9957.

[35] Nardone, P., (2009), “Well Testing Project Management: Onshore and Offshore Operations”, book, Gulf professional press, pp. 61.