Evaluation of Machine Learning Methods for Prediction of Multiphase Production Rates

Abstract Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain. Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flow metering systems has become a focal point for many companies. This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven virtual metering approach for continuous production monitoring purposes has been demonstrated via a series of simulation-based cases. Amongst the used algorithms the deep learning methods provided the most accurate results combined with reasonable time for model training.