TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractThe 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 paper proposes a fuzzy nonlinear programming approach to accommodate these uncertainties and applies to a variety of optimization processes. Specifically, the fuzzy λ -formulation is combined with a hybrid co-evolutionary genetic algorithm for an optimal design of gas production systems. Both the multiple conflicting objective and constraint functions for production systems of gas field are formulated in a feasible fuzzy domain. Then, the genetic algorithm is used as a primary optimization scheme for solving optimum gas production rates of each well and pipeline segment diameters to minimize investment cost with given constraints in order to enhance ultimate recovery. The synthetic optimization method can find a globally compromise solution and offer a new alternative with significant improvement over the existing conventional techniques. It has been successfully applied to a wide variety of operational problems including well rate allocation and optimal design of pipeline networks. The reliability of the proposed approach is validated by a synthetic practical example yielding more improved results, offering a powerful tool for cost savings in future planning and the optimization of gas production operations.

TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractThe 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 paper proposes a fuzzy nonlinear programming approach to accommodate these uncertainties and applies to a variety of optimization processes. Specifically, the fuzzy λ -formulation is combined with a hybrid co-evolutionary genetic algorithm for an optimal design of gas production systems. Both the multiple conflicting objective and constraint functions for production systems of gas field are formulated in a feasible fuzzy domain. Then, the genetic algorithm is used as a primary optimization scheme for solving optimum gas production rates of each well and pipeline segment diameters to minimize investment cost with given constraints in order to enhance ultimate recovery. The synthetic optimization method can find a globally compromise solution and offer a new alternative with significant improvement over the existing conventional techniques. It has been successfully applied to a wide variety of operational problems including well rate allocation and optimal design of pipeline networks. The reliability of the proposed approach is validated by a synthetic practical example yielding more improved results, offering a powerful tool for cost savings in future planning and the optimization of gas production operations.

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The design of production systems of gas fields is a difficult task because of the nonlinear nature of the optimization problem and the complex interactions between each operational parameter. Conventional methods, which are usually stated in precise mathematical forms, cannot include the uncertainties associated with vague or imprecise information in the objective and constraint functions.This paper proposes a fuzzy nonlinear programming approach to accommodate these uncertainties and applies it to a variety of optimization processes. Specifically, the fuzzy -formulation is combined with a hybrid coevolutionary genetic algorithm for the optimal design of gas-production systems. Both the multiple conflicting objective and constraint functions for production systems of gas fields are formulated in a feasible fuzzy domain. Then, the genetic algorithm is used as a primary optimization scheme for solving the optimum gas-production rates of each well and the pipeline segment diameters to minimize the investment cost with a given set of constraints in order to enhance the ultimate recovery.The synthetic-optimization method can find a global compromise 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. This method constitutes an offering of a powerful tool for cost savings in the planning and optimization of gas-production operations.

No abstract