Surrogate reservoir modeling-prediction of Bottom-Hole Flowing Pressure using Radial Basis Neural Network

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. Due to intrinsic uncertainty in the reservoir simulation models, large number of computational resources such as simulation runs and long processing time are required to predict the properties in a reservoir. This paper presents an application of Surrogate Reservoir Model (SRM) for predicting the Bottom-Hole Flowing Pressure (BHFP) at different time step for an initially under-saturated reservoir. The developed SRM is based on Artificial Neural Network to regenerate the results of a numerical simulation model in considerable amount of time. The output of the reservoir simulation consists of oil production, gas rate, average reservoir pressure, saturation and BHFP etc. The proposed SRM adopted Radial Basis Neural Network to predict the BHFP based on the output data extracted from the Black Oil Applied Simulation Tool (BOAST). It is found that the developed SRM is capable in supporting fast track analysis, decision optimization and manage to generate the results in a shorter time as compared to the conventional reservoir model.