The main goal of this paper is to modify and apply the state-of-the-art intelligent, optimum portfolio management to the gas storage field in order to optimize the return on investment associated with well remedial operations. It continues the development of a methodology for candidate selection and stimulation design and optimization using Artificial Intelligence techniques.

The data of an actual gas storage field was used to test the results. The project data include Well-bore, Completion, Perforation, Stimulation, Well-test and Reservoir Data. To make candidate selection for gas storage fields operators predict the effectiveness of the stimulation commonly using three parameters. One in Peak Day rate second is Absolute open flow and third is change in skin provided permeability values in the field don't vary much. The software developed in parallel with this selection methodology includes an easy to use interface that allows the user to edit the data for a gas storage field, perform well-test analysis and use neural networks in association with Genetic optimization tool. The software ranks the well according to maximum change in skin value and recommends the best stimulation slurry based on the weitage given to the skin and cost of stimulation. A decision to select the ranked wells for re-stimulate can be made accordingly.

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