Applying Genetic Programming to Reservoir History Matching Problem

History matching is the process of updating a petroleum reservoir model using production data. It is a required step before a reservoir model is accepted for forecasting production. The process is normally carried out by 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. In this work, we introduce a methodology using genetic programming (GP) to construct a proxy for reservoir simulator. Acting as a surrogate for the computer simulator, the "cheap" GP proxy can evaluate a large number (millions) of reservoir models within a very short time frame. Collectively, the identified goodmatching reservoir models provide us with comprehensive information about the reservoir. Moreover, we can use these models to forecast future production, which is closer to the reality than the forecasts derived from a small number of computer simulation runs. We have applied the proposed technique to a West African oil field that has complex geology. The results show that GP is able to deliver high quality proxies. Meanwhile, important information about the reservoirs was revealed from the study. Overall, the project has successfully achieved the goal of improving the quality of history matuching results without increasing the number of reservoir simulation runs. This result suggests this novel history matching approach might be effective for other reservoirs with complex geology or a significant amount of production data.