A Comparative Study of Oil–Water Two-Phase Flow Pattern Prediction Based on the GA-BP Neural Network and Random Forest Algorithm

Abstract

As global oil demand continues to increase, in recent years, countries have continued to expand the development of oil reserves, highlighting the importance of oil. In order to adapt to different strata distribution conditions, domestic drilling technology is becoming more and more perfect, resulting in a gradual increase in horizontal and inclined wells. Because of the influence of various downhole factors, the flow pattern in the wellbore will be more complex. Accurately identifying the flow pattern of multiphase flow under different well deviation conditions is very important to interpreting the production log output profile accurately. At the same time, in order to keep up with the footsteps of artificial intelligence, big data and artificial intelligence algorithms are applied to the oil industry. This paper uses the GA-BP neural network and random forest algorithm to conduct fluid flow pattern prediction research on the logging data of different water cuts at different inclinations and flow rates. It compares the predicted results with experimental fluid flow patterns. Finally, we can determine the feasibility of these two algorithms for predicting flow patterns. We use the multiphase flow simulation experiment device in the experiment. During the process, the flow patterns are observed and recorded by visual inspection, and the flow pattern is distinguished by referring to the theoretical diagram of the oil-water two-phase flow pattern. The prediction results show that the accuracy of these two algorithms can reach 81.25% and 93.75%, respectively, which verifies the effectiveness of these two algorithms in the prediction of oil–water two-phase flow patterns and provides a new idea for the prediction of oil–water two-phase flow patterns and other phases.