Optimization of Gas Production Systems Using Fuzzy Nonlinear Programming and Co-evolutionary Genetic Algorithm

Abstract

The design of production systems of gas fields has been a difficult task because of the nonlinear nature of optimization problem and the complex interactions between each operational parameter. Conventional methods, which usually are stated in precise mathematical forms, cannot include the uncertainties regarding vague or imprecise information in the objective and constraint functions. This article proposes a fuzzy nonlinear programming approach to accommodate these uncertainties and applies to a variety of optimization processes. Specifically, the fuzzy-formulation is combined with a hybrid co-evolutionary genetic algorithm for solving optimum gas production rates of each well to minimize investment cost with given constraints in order to enhance ultimate recovery. The synthetic optimization method can find a globally compromised solution and offer a new alternative with significant improvement over the existing conventional techniques. The reliability of the proposed approach is validated by a synthetic practical example yielding more improved results.

Keywords:

• co-evolutionary genetic algorithm
• fuzzy nonlinear programming
• production system

Acknowledgment

The authors gratefully acknowledge the financial support of the Korea Ministry of Science and Technology under National Research Laboratory Program, contract M1010400042-01J000001700.