This paper summarizes the development of a methodology for the restimulation candidate selection in tight gas sands. The methodology incorporates virtual intelligence techniques (artificial neural networks, genetic algorithms and fuzzy logic) to achieve this objective. Artificial neural networks are used to develop a representative model of the completion and hydraulic fracturing process in a specific field. Genetic algorithms are used as a search and optimization tool to identify the missed incremental production based on the neural network model. Finally fuzzy logic is used to capture the unique field experiences of the engineers as well as detrimental parameters (if such parameters are indeed present) and incorporate them in the decision making process. Approximate reasoning approach is used at the decision making level to identify the restimulation candidates. Once the methodology is introduced, it is applied to an actual tight sand field in the Rocky Mountain region and the results are presented.

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