Measurement of Total Flow Rates in Horizontal Well Oil-Water Two-Phase Flows by the Application of BP Neural Network Algorithm to Production Array Logs

Abstract

In the modern petroleum industry system, oil-water two-phase flows exist widely. Among them, the total flow rate of mixture fluid in a horizontal well is difficult to obtain due to the phase segregation caused by gravity. Therefore, it is a difficult and hot issue. To obtain the total flow rate of oil-water two-phase flows in horizontal wells, in this paper, Multiple Array Production Suite (MAPS), which is also called Production Array Logs (PAL), is used to conduct simulation experiments, uses BP neural network (BPNN) algorithm to train the data samples, and establishes the prediction models of the total flow rates of oil-water two-phase flows in horizontal wells. The results showed that the average relative error was less than 10%, which justify that the BPNN has good practicability in using data of MAPS in oil-water two-phase flows horizontal wells to predict the total flow rates, and it provides a new method and theoretical support for obtaining flow rates in horizontal wells.