Application of BP neural network to prediction of recovery effect of air-foam flooding in heavy oil

Abstract

Air-foam flooding is an effective technology to enhance oil recovery in heavy oil production. In this study, Back Propagation (BP) neural network model is built and optimized to predict the recovery effect of heavy oil with the injection parameters data of the oxygen-reduced air-foam flooding obtained through experiments. The study shows that the prediction results of the BP neural network is quite accurate, and the average absolute relative error of prediction results is less than 0.21%. This also illustrates the feasibility of using the BP network prediction model in predicting heavy oil recovery effect.