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Risk Management for Petroleum Reservoir Production: A Simulation-Based Study of Prediction |
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| Authors: | James Glimm Shuling Hou Hongjoong Kim Yoon-ha Lee David H Sharp Kenny Ye Qisu Zou |
| Institution: | (1) Department of Applied Mathematics and Statistics, SUNY at Stony Brook, Stony Brook, NY 11794-3600, USA;(2) Brookhaven National Laboratory, Center for Data Intensive Computing, Brookhaven, NY 11973, USA;(3) Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA |
| Abstract: | We consider numerical solutions of the Darcy and Buckley–Leverett equations for flow in porous media. These solutions depend on a realization of a random field that describes the reservoir permeability. The main content of this paper is to formulate and analyze a probability model for the numerical coarse grid solution error. We explore the extent to which the coarse grid oil production rate is sufficient to predict future oil production rates. We find that very early oil production data is sufficient to reduce the prediction error in oil production by about 30%, relative to the prior probability prediction. |
| Keywords: | Bayes' prediction error model porous media flow |
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