Comparing the quality of basic reservoir rock properties is a common practiceÂ toÂ locateÂ newÂ infillÂ orÂ developmentÂ wellsÂ forÂ optimizingÂ oilÂ field development using reservoir simulation. The conventional technique employs a manualÂ trial-and-errorÂ processÂ toÂ findÂ newÂ wellÂ locations,Â whichÂ provesÂ toÂ be time-consuming, especially for large fields. Concerning this practical matter, an alternative in the form of a robust technique is introduced in order to reduce time andÂ effortÂ inÂ findingÂ newÂ wellÂ locationsÂ capableÂ ofÂ producingÂ theÂ highestÂ oil recovery. The objective of this research was to apply a genetic algorithm (GA) for determining well locations using reservoir simulation, in order to avoid the conventionalÂ manualÂ trial-and-errorÂ method.Â ThisÂ GAÂ involvedÂ theÂ basicÂ rock properties,Â i.e.Â porosity,Â permeability,Â andÂ oil Â saturation,Â ofÂ eachÂ gridÂ block obtained from a reservoir simulation model, to which a newly generated fitness function was applied, formulated by translating common engineeringÂ practice in reservoirÂ simulationÂ intoÂ aÂ mathematicalÂ equationÂ andÂ thenÂ intoÂ aÂ computer program. The maximum fitness value indicates the best grid location for a new well. In order to validate the proposed GA method and evaluate the performance of the program, two fields with different production profile characteristics were used, fields X and Y. The proposed method proved to be a robust and accurate method to find the best new well locations for oil field development. The key to the success of the proposedÂ GA method lies in the formulation of the objective functions.
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