Towards dynamic data-driven optimization of oil well placement

Authors:
Manish Parashar;Vincent Matossian;Wolfgang Bangerth;Hector Klie;Benjamin Rutt;Tahsin Kurc;Umit Catalyurek;Joel Saltz;Mary F. Wheeler
Affiliations:
TASSL, Dept. of Electrical & Computer Engineering, The State University of New Jersey, Rutgers, New Jersey;TASSL, Dept. of Electrical & Computer Engineering, The State University of New Jersey, Rutgers, New Jersey;CSM, ICES, The University of Texas at Austin, Texas;CSM, ICES, The University of Texas at Austin, Texas;Dept. of Biomedical Informatics, The Ohio State University, Ohio;Dept. of Biomedical Informatics, The Ohio State University, Ohio;Dept. of Biomedical Informatics, The Ohio State University, Ohio;Dept. of Biomedical Informatics, The Ohio State University, Ohio;CSM, ICES, The University of Texas at Austin, Texas
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Year:
2005

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Abstract

The adequate location of wells in oil and environmental applications has a significant economical 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 dynamic data-driven self-optimizing reservoir framework. In this paper, we present the use of distributed data to dynamically drive the optimization of well placement in an oil reservoir.