Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008 (2008), Article ID 263108, 13 pages
http://dx.doi.org/10.1155/2008/263108
Research Article

Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis

1Department of Computer Science, Memorial University of Newfoundlan, St. John's, NL, Canada A1B 3X5
2Chevron Energy Technology Company, San Ramon, CA 94583, USA

Received 15 March 2007; Accepted 9 August 2007

Academic Editor: Leonardo Vanneschi

Copyright © 2008 Tina Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Reservoir modeling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time-consuming. As a result, only a small number of simulation runs are conducted and the history-matching results are normally unsatisfactory. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. The inadequacy of the history-matching results frequently leads to high uncertainty of production forecasting. To enhance the quality of the history-matching results and improve the confidence of production forecasts, we introduce a methodology using genetic programming (GP) to construct proxies for reservoir simulators. Acting as surrogates for the computer simulators, the “cheap” GP proxies can evaluate a large number (millions) of reservoir models within a very short time frame. With such a large sampling size, the reservoir history-matching results are more informative and the production forecasts are more reliable than those based on a small number of simulation models. We have developed a workflow which incorporates the two GP proxies into the history matching and production forecast process. Additionally, we conducted a case study to demonstrate the effectiveness of this approach. The study has revealed useful reservoir information and delivered more reliable production forecasts. All of these were accomplished without introducing new computer simulation runs.