Parallel history matching and associated forecast at the center for interactive smart oilfield technologies |
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| Authors: | Ken-Ichi Nomura Rajiv K Kalia Aiichiro Nakano Priya Vashishta Jorge L Landa |
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| Affiliation: | (1) Collaboratory for Advanced Computing and Simulations (CACS), Center for Interactive Smart Oilfield Technologies (CiSoft), University of Southern California, Los Angeles, CA 90089-0242, USA;(2) Department of Computer Science, University of Southern California, Los Angeles, CA 90089-0242, USA;(3) Department of Physics and Astronomy, University of Southern California, Los Angeles, CA 90089-0242, USA;(4) Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089-0242, USA;(5) ChevronTexaco, 6001 Bollinger Canyon, San Ramon, CA 94583, USA |
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| Abstract: | We have developed a parallel and distributed computing framework to solve an inverse problem, which involves massive data
sets and is of great importance to petroleum industry. A Monte Carlo method, combined with proxies to avoid excessive data
processing, is employed to identify reservoir simulation models that best match the oilfield production history. Subsequently,
the selected models are used to forecast future productions with uncertainty estimates. The parallelization framework combines: (1)
message passing for tightly coupled intra-simulation decomposition; and (2) scheduler/Grid remote procedure calls for model
parameter sweeps. A preliminary numerical test has included 3,159 simulations on a 256-processor Intel Xeon cluster at the
USC-CACS. The results provide uncertainty estimates of unprecedented precision. |
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| Keywords: | Smart oilfield History matching and forecast Inverse problem Monte Carlo method Parallel and distributed computing Massive data sets |
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