Abstract
The main objective of the present work is to propose and evaluate a neural stochastic optimization framework for reservoir parameter estimation, for which a history matching procedure is implemented by combining three independent sources of spatial and temporal information: production data, time-lapse seismic and sensor information. In order to efficiently perform large-scale parameter estimation, a coupled multilevel, stochastic and learning search methodology is proposed. At a given resolution level, the parameter space is globally explored and sampled by the simultaneous perturbation stochastic approximation (SPSA) algorithm. The estimation and sampling performed by SPSA is further enhanced by a neural learning engine that evaluates the objective function sensitiveness with respect to parameter estimates in the vicinity of the most promising optimal solutions.
The research presented in this paper is supported in part by the National Science Foundation ITR Grant EIA-0121523/EIA-0120934 and the Spanish Ministry of Education and Science.
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References
Lumley, D.: Time-lapse seismic reservoir monitoring. Geophysics 66, 50–53 (2001)
Versteeg, R., Ankeny, M., Harbour, J., Heath, G., Kostelnik, K., Matson, E., Moor, K., Richardson, A.: A structured approach to the use of near-surface geophysics in long-term monitoring. Expert Systems with Applications 23, 700–703 (2004)
van der Baan, M., Jutten, C.: Neural networks in geophysical applications. Geophysics 65, 1032–1047 (2000)
Nikravesh, M.: Soft computing-based computational intelligent for reservoir characterization. Expert Systems with Applications 26, 19–38 (2004)
Bangerth, W., Klie, H., Parashar, M., Mantosian, V., Wheeler, M.F.: An autonomic reservoir framework for the stochastic optimization of well placement. Cluster Computing 8, 255–269 (2005)
Parashar, M., Klie, H., Catalyurek, U., Kurc, T., Bangerth, W., Matossian, V., Saltz, J., Wheeler, M.F.: Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. Future Generation of Computer Systems 21, 19–26 (2005)
Spall, J.C.: Introduction to stochastic search and optimization: Estimation, simulation and control. John Wiley & Sons, Inc., New Jersey (2003)
Keane, A., Nair, P.: Computational Approaches for Aerospace Design: The Pursuit of Excellence. Wiley, England (2005)
Parashar, M., Wheeler, J.A., Pope, G., Wang, K., Wang, P.: A new generation EOS compositional reservoir simulator. Part II: Framework and multiprocessing. In: Fourteenth SPE Symposium on Reservoir Simulation, Dalas, Texas, pp. 31–38 (1997)
Wang, P., Yotov, I., Wheeler, M.F., Arbogast, T., Dawson, C.N., Parashar, M., Sepehrnoori, K.: A new generation EOS compositional reservoir simulator. Part I: Formulation and Discretization. In: Fourteenth SPE Symposium on Reservoir Simulation, Society of Petroleum Engineers, Dalas, Texas pp. 55–64 (1997)
Bourbie, T., Coussy, O., Zinszner, B.: Acoustics of Porous Media. Institut fran¸cais du p´etrole publications, Editions TECHNIP (1987)
Nishi, K.: A three dimensional robust seismic ray tracer for volcanic regions. Earth Planets Space 53, 101–109 (2001)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, New York (1994)
Christie, M., Blunt, M.: Tenth SPE Comparative Solution Project: A Comparison of Upscaling Techniques. SPE Reservoir Engineering 12, 308–317 (2001)
Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)
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Banchs, R.E., Klie, H., Rodriguez, A., Thomas, S.G., Wheeler, M.F. (2006). A Neural Stochastic Optimization Framework for Oil Parameter Estimation. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_18
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DOI: https://doi.org/10.1007/11875581_18
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