Application of Grid-Enabled Technologies for Solving Optimization Problems in Data-Driven Reservoir Studies*

Manish Parashar1, Hector Klie2, Umit Catalyurek3, Tahsin Kurc3, Vincent Matossian1, Joel Saltz3, and Mary F. Wheeler2

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1Dept. 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
klie@ices.utexas.edu
mfw@ices.utexas.edu

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

Abstract. This paper presents use of numerical simulations coupled with optimization techniques in reservoir modeling and production optimization. We describe three main components of an autonomic oil production management framework. This framework implements a dynamic, data-driven approach and enables execution in a Grid environment for large scale optimization formulations in reservoir modeling.

*This work is partly supported under the National Science Foundation under Grants ACI-9619020 (UC Subcontract 10152408), ANI-0330612, EIA-0121177, Sbr-9873326, EIA-0121523, ACI-0203846, ACI-0130437, ACI-9982087, NPACI 10181410, ACI 9984357, EIA 0103674 and EIA- 0120934, Lawrence Livermore National Laboratory under Grant B517095 (UC Subcontract 10184497), Ohio Board of Regents brTTC brTT02-0003, and DOE DE-FG03-99ER2537.

LNCS 3038, pp. 805-812.

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