Predicting Gas Hydrate Equilibria in Multicomponent Systems with a Machine Learning Approach
Qazi Nasir, Wameath S. Abdul‐Majeed, Humbul Suleman
Abstract
Abstract Natural gas production and transportation can be threatened by gas hydrate plugging, especially in offshore environments with low temperatures and high pressures. This can cause pipeline blockages, increased back pressure, production stoppage, and pipeline ruptures. Machine learning (ML) models were created to predict gas hydrate formation in multicomponent systems with and without inhibitors. The models utilized input parameters such as gas‐mixture specific gravity, pressure, and inhibitor concentrations, which were fed into five supervised ML algorithms. The accuracy of the models' predictions was compared, and three models had accuracy greater than 90%, while two had accuracies between 80 and 90%.