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.
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Parashar, M. et al. (2004). Application of Grid-Enabled Technologies for Solving Optimization Problems in Data-Driven Reservoir Studies. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24688-6_104
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DOI: https://doi.org/10.1007/978-3-540-24688-6_104
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