Oil Production Monitoring Using Gradient Boosting Machine Learning Algorithm

Abstract

Data-driven solutions for multiphase flowrate estimation in oil and gas production systems are among the alternatives to first principles virtual flow metering systems and hardware flow metering installations. Some of the most popular data-driven methods in this area are based on artificial neural networks which have been proven to be good virtual flow metering tools. However, neural networks are known to be sensitive to the scaling of input data, difficult to tune and provide a black-box solution with occasionally unexplainable behavior under certain conditions. As an alternative, in this paper, we explore capabilities of the Gradient Boosting algorithm in predicting oil flowrates using available field measurements. To do this, we use an efficient implementation of the algorithm named XGBoost. In contrast to neural networks, this algorithm is insensible to data scaling, can be more intuitive in tuning as well as it provides an opportunity to analyze feature influence which is embedded in algorithm learning. We show that the algorithm provides accurate flowrate predictions under various conditions and can be used as a back-up as well as a standalone multiphase flow metering solution.