Stochastic Sequential Optimization for Full Field Development of Gas Condensate Reservoirs

Abstract

This study presents a new methodology for optimizing a full-field development plan using areal segmentation and stochastic sequential optimization. Previous works related to field development optimization have required a huge number of simulation runs to optimize well locations, and some of works with sequential optimization have shown unreliable results on the first step optimization. The author introduced both areal segmentation and stochastic sequential optimization, and integrated them with a standard genetic algorithm. Areal segmentation utilizes the well locations defined before the optimization, and enhances the optimization efficiency by reducing number of required simulation runs while optimizing locations of individual wells. Stochastic sequential optimization helps to have higher credibility on the result of the first step optimization by incorporating Monte-Carlo simulation with the variables of the second step optimization. The applicability of the methodology is verified by successfully optimizing full field development plan of a gas condensate field in offshore, Vietnam. Stochastic sequential optimization helps to make a reliable decision for the first step optimization under economic uncertainties, and areal segmentation plays a key role to accelerate the optimization procedure by reducing the number of simulation runs tremendously. Finally, the optimum plan achieves 10% higher cumulative oil recovery and 11-15% higher economic value than the base plan. The proposed techniques in this study can be applied to optimize not only full field development plan but also reservoir management plan, and it would be helpful to improve economics of many kinds of oil and gas projects under economic and geological uncertainties.