Reservoir simulation is one of the main if not the most important tool reservoir engineers use to forecast a reservoir performance. Nevertheless, developing and operating a reservoir simulator tool in the first place can be an arduous task that requires a set of highly skilled individuals in science, advanced mathematics, programing, and reservoir engineering and powerful computational models (Ertekin, et al., 2001). The reliability of a reservoir simulator depends on the availability and the quality of the reservoir properties. These properties are obtained from open-hole logs, core studies and well testing analysis which can sometimes be prohibitively cost intensive. Another important component of the overall process affecting the reservoir performance is the multilateral well configuration. Achieving the right design of a multilateral well configuration is a complex problem due to the vast possibilities of well forms that need to be evaluated. In light of the above, this paper demonstrates the development and the application of a set of integrated artificial expert systems in the area of forecasting, reservoir evaluation and multilateral well design. The applied method is implemented to volumetric single phase gas reservoirs with a variety of rock and fluid properties spanning tight to conventional sands. The developed approach delivers a proxy to the conventional numerical simulator for predicting reservoir performance, an expert system for estimating reservoir properties from rate decline data, and an expert system for recommending multilateral well design parameters for a target recovery profile. Furthermore, graphical user interfaces (GUIs) in conjunction with the expert systems structured are developed and assembled together for standalone installation. These GUIs allow the engineer to edit and input data, produce results numerically and graphically, compare results with a commercial numerical simulator, and generate an interactive 3-D visualization of the multilateral well. It is expected that the developed expert systems will immensely reduce time requirements and effectively enhance the overall decision-making process. However it is emphasized that this paper is not suggesting the replacement of existing and well established procedures, protocols and know-how with the developed expert systems, but rather applying them as auxiliary, pre- screening or complimentary systems where and when applicable to ease of the computational overload, deliver a solution to the inverse-looking problems and enhance the overall decision-making process.

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