Hybrid Artificial Intelligent Approach for Choke Size Estimation in Volatile and Black Oil Reservoirs

Abstract

Accurate prediction of choke size is important for successful production design and flow rate estimation. Many empirical correlations have been used to estimate choke size. The accuracy of these correlations has become inadequate for the best estimation. Recent achievements of Artificial Intelligence (AI) in petroleum engineering applications alone encourage the scientists to apply the hybrid model in order to improve AI results. In this study, a Particle Swarm Optimization (PSO) algorithm was developed to optimize neural network (NN) weights in order to improve their performance (PSONN). The Hybrid PSONN model compared with existed Fuzzy logic (FL) model which considered as the best AI model, Khamis [15]. Around 2445 and 766 data sets of the volatile and black oil reservoir from Middle East region respectively were selected. The comparative results confirmed that the hybrid model PSONN is performed better with lower relative errors and higher accuracy than the FL model. Based upon the results, we conclude that the hybrid models show a robust capability for the estimation of choke sizes that will help to flow rate estimation and choke design purposes. In future, the PSONN model can be combined with any simulator to improve the accuracy of oil flow rate and production design calculation.