A neural stochastic optimization framework for oil parameter estimation

Authors:
Rafael E. Banchs;Hector Klie;Adolfo Rodriguez;Sunil G. Thomas;Mary F. Wheeler
Affiliations:
GPS, TSC, Polytechnic University of Catalonia, Barcelona, Spain;CSM, ICES, The University of Texas at Austin, Texas;CSM, ICES, The University of Texas at Austin, Texas;CSM, ICES, The University of Texas at Austin, Texas;CSM, ICES, The University of Texas at Austin, Texas
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 5
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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.