Multiphase flow measurement of wet gas flow using machine learning modelling algorithms

When it comes to optimizing the efficiency of transportation pipelines, the accurate quantification of wet gas flows which are mainly comprised of gas with minor fractions of liquid - is still a relevant topic of interest. Historically, X_LM (Lockhart-Martinelli parameter) has served as a key parameter in the development of overreading correlations for the accurate determination of the mass flowrate of gas with differential pressure meters. This investigation aims to offer a potential alternative to the traditional correlation-based techniques through direct prediction of the mass flowrate of gas and liquid and thus addressing the inherent complexities of wet gas metering. The present investigation suggests a novel Machine Learning (ML) modelling algorithms to improve the prediction accuracy of both the liquid and gas flowrates. In this study, four ML models are discussed in terms of their efficacy. The study results promise significant advancements in flow measurements through introducing this proposed advanced technique.