Applications of Advanced Polynomial Neural Networks for Prediction of Multi-well Reservoir Performance

Abstract

The major impediment in the formal optimization of large petroleum-producing fields is the cost of computing the state of the objects being optimized. Previous studies for prediction of reservoir performance with respect to time have used local inflow performance relationships or material balance models. These approaches, however, ignored flow interactions among wells during the optimization process, often resulting in suboptimal operations. In this study, a new 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. The proposed approach significantly reduces computational effort for optimizing the development scheme within reasonable accuracy and outperforms other neural network models.