Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis. (17th December 2007)

• Record Type: Journal Article
• Authors: Yu, Tina; Wilkinson, Dave; Castellini, Alexandre
• Other Names: Vanneschi Leonardo Academic Editor.
• Is Part Of: Journal of artificial evolution and applications. Volume 2008(2008)
• Publication Date: 2007-12-17
• Subjects: Evolutionary programming (Computer science) -- Periodicals; Evolutionary programming (Computer science); Periodicals; Electronic journals; 006.3823
• DOI: 10.1155/2008/263108
• Languages: English
• ISSNs: 1687-6229

Abstract

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.