A hybrid artificial intelligence method for the optimization of integrated gas production system

The optimization of gas production system has been largely based on a nodal analysis approach of individual wells. This approach limits the overall benefit of a field study because it ignores complicated flow interactions among wells during the optimization process, often resulting suboptimal operations. A major impediment in the formal optimization of large petroleum producing fields is the excessive computation time during optimizing processes. The conventional deterministic algorithm can not take into consideration the uncertainties regarding nonlinear and complex systems. This paper proposes a new hybrid artificial intelligence approach for field-wide optimization of integrated production system including gas reservoir, well flow, and surface production network. An advanced polynomial neural network (PNN) with layer over-passing structure has been developed to replace a relatively time consuming reservoir simulator through robust and systematic search algorithm. The networks are subject to some form of training based on a representative sample of simulations that can be used as a re-useable knowledge base of information for addressing many different management questions. For an optimal design of gas production systems, this study uses an integrated simulation model implemented by coupling a PNN with surface production network and optimization scheme based on fuzzy nonlinear programming approach to accommodate uncertainties by combining the fuzzy λ formulation with a co-evolutionary genetic algorithm. The proposed approach significantly reduces computational effort for optimization of the development scheme within reasonable accuracy. The hybrid optimization method can find a globally compromise solution and offer a new alternative with significant improvement over the existing conventional techniques. This technique provides optimum production rates of each well of the entire production system to deliver the target rates over the period of the gas contract requirements. This system has been validated by synthetic case study offering an important element for management of gas production strategies and field development.