Dynamic well bottom-hole flowing pressure prediction based on radial basis neural network

Abstract

Reservoir simulation provides information about the behaviour of a reservoir in various production and injection conditions. Reservoir simulator is used to predict the future behaviour and performance of a reservoir field. However, the heterogeneity of reservoir and uncertainty in the reservoir field cause some obstacles in selecting the best calculation of oil, water and gas components that lead to the production system in oil and gas. This paper presents a dynamic well Surrogate Reservoir Model (SRM) to predict reservoir bottom-hole flowing pressure by varying the production rate constraint of a well. The proposed SRM adopted Radial Basis Neural Network to predict the bottom-hole flowing pressure of well based on the output data extracted from a numerical simulation model in a considerable amount of time with production constraint values. It is found that the dynamic SRM is capable to generate the promising results in a shorter time as compared to the conventional reservoir model.