Gas Hydrate Dissociation Temperature Prediction in Porous Media: Effects of Pore Size and Modeling Approach

Understanding the dissociation behavior of gas hydrates in confined porous media is crucial for assessing their stability and potential applications in energy storage, carbon capture, and climate modeling. In this study, we develop two distinct approaches to predict the equilibrium dissociation temperature of gas hydrates in porous materials with varying pore sizes: a thermodynamic model based on the activity approach and a suite of machine learning (ML) models. The thermodynamic model explicitly accounts for the effects of confinement on hydrate phase stability and was validated using an unfiltered data set for methane (CH4) and propane (C3H8) hydrates, achieving low average absolute deviations (AAD%) of 0.17 and 0.62%, respectively. To complement and generalize these predictions, we trained four ML models: Decision Tree, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron. These models used input features such as pore diameter, system pressure, and critical gas properties. A group-based data splitting strategy was applied, with propane data exclusively reserved for testing to assess true generalization. The SVM model exhibited the highest predictive performance on unseen data, with an AAD% of 0.52%. To enhance interpretability, SHapley Additive exPlanations (SHAP) analysis was employed. The results confirmed alignment between the ML model’s decision logic and known physical principles and identified critical temperature, pressure, and pore size as the most influential features. While group-based splitting improved robustness, discrete SHAP patterns suggest that a broader variety of gases in the training data could further enhance generalizability. Overall, this integration of physics-based and data-driven modeling provides accurate and interpretable predictions of hydrate dissociation behavior in porous systems, supporting future developments in both geological and industrial applications.