Towards Dynamic Data-Driven Optimization of Oil Well Placement

Manish Parashar1, Vincent Matossian1, Wolfgang Bangerth2,4, Hector Klie2, Benjamin Rutt3, Tahsin Kurc3, Umit Catalyurek3, and Joel Saltz3, and Mary F. Wheeler2

1TASSL, Dept. of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, New Jersey, USA
parashar@caip.rutgers.edu
vincentm@caip.rutgers.edu

2CSM, ICES, The University of Texas at Austin, Texas, USA
bangerth@ices.utexas.edu
klie@ices.utexas.edu
mfw@ices.utexas.edu

3Dept. of Biomedical Informatics, The Ohio State University, Ohio, USA
rutt@bmi.osu.edu
kurc@bmi.osu.edu
umit@bmi.osu.edu
jsaltz@bmi.osu.edu

4Institute for Geophysics, The University of Texas at Austin, Texas, USA

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.

*The research presented in this paper is supported in part by the National Science Foundation Grants ACI 9984357, EIA 0103674, EIA 0120934, ANI 0335244, CNS 0305495, CNS 0426354, IIS 0430826, ACI-9619020 (UC Subcontract 10152408), ANI-0330612, EIA-0121177, Sbr-9873326, EIA-0121523, ACI-0203846, ACI-0130437, CCF-0342615, CNS-0406386, CNS-0426241, ACI-9982087, CNS-0305495, NPACI 10181410, DOE ASCI/ASAP via grant numbers PC295251 and 82-1052856, Lawrence Livermore National Laboratory under Grant B517095 (UC Subcontract 10184497), Ohio Board of Regents brTTC brTT02-0003, and DOE DE-FG03-99ER2537.

LNCS 3515, pp. 656-663.

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