An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement

Authors:
Wolfgang Bangerth;Hector Klie;Vincent Matossian;Manish Parashar;Mary F. Wheeler
Affiliations:
Center for Subsurface Modeling, The University of Texas at Austin, Austin;Center for Subsurface Modeling, The University of Texas at Austin, Austin;The Applied Software Systems Laboratory, Rutgers University, Piscataway;The Applied Software Systems Laboratory, Rutgers University, Piscataway;Center for Subsurface Modeling, The University of Texas at Austin, Austin
Venue:
Cluster Computing
Year:
2005

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Abstract

The adequate location of wells in oil and environmental applications has a significant economic impact on reservoir management. However, the determination of optimal well locations is both challenging and computationally expensive. The overall goal of this research is to use the emerging Grid infrastructure to realize an autonomic self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem.