HYBRID PHYSICS-INFORMED MACHINE LEARNING FRAMEWORK FOR PREDICTING METHANE HYDRATE RESERVOIR PRODUCTIVITY

Abstract

Methane hydrates hold enormous quantities of natural gas in a form that could meaningfully add to the world's future energy supply, yet accurately forecasting how productive a given reservoir will be remains difficult. The obstacle is coupling: thermal, hydraulic, mechanical, and geochemical processes all interact during hydrate dissociation and gas release, and untangling their combined effect on production is not straightforward. This paper puts forward a hybrid physics-informed machine learning (HPIML) framework that folds core reservoir-flow equations into a data-driven model, with the aim of improving both prediction accuracy and the model's ability to generalize. Physical constraints are combined with deep learning models trained on numerical-simulation output and, where available, field data, so that gas production rate, pressure evolution, hydrate saturation, and permeability can all be estimated across a range of production scenarios. Because the physics terms regularize training, the model is less prone to overfitting than a purely data-driven counterpart while still tracking known reservoir behavior. The framework is expected to outperform conventional empirical and purely data-driven methods on accuracy, computational cost, and interpretability, and it includes a sensitivity-analysis component that flags which geological and operational variables matter most for methane recovery. Taken together, the study points to a practical way of pairing physics-based knowledge with machine learning to support better decisions around methane hydrate exploitation, tighter reservoir management, and lower uncertainty as production moves toward commercial scale a step toward sustainable hydrate development that fits within a broader, carbon-conscious approach to petroleum engineering.