Semester

Fall

Date of Graduation

2006

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Petroleum and Natural Gas Engineering

Committee Chair

Shahab Mohaghegh.

Abstract

The main goal of this thesis 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 project used the data of an actual gas storage field to test the results.;The project data include Well-bore, Completion, Perforation, Stimulation, Well-test and Reservoir Data. 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 for a well and thus a decision to re-stimulate the well or not is made accordingly.

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