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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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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