Insights into the mechanical stability of tetrahydrofuran hydrates from experimental, machine learning, and molecular...

Natural gas hydrates (NGHs) hold immense potential as a future energy resource and for sustainable applications such as gas capture and storage. Due to the challenging formation conditions, however, their mechanical properties remain poorly understood. Herein, the mechanical characteristics of tetrahydrofuran (THF) hydrates, a proxy for methane hydrates, were investigated at different ice contents, strain rates, and temperatures using uniaxial compressive experiments. The results unveil a distinct behavior in the peak strength of THF hydrates with a varying ice content, strain rate and temperature, exhibiting an increase as the strain rate and temperature decrease, in contrast to the peak strength-strain rate relationship observed in polycrystalline ice. Based on the experimental data, four machine learning (ML) models including extreme gradient boosting (XGboost), multilayer perceptron (MLP), gradient boosting decision tree (GBDT) and decision tree (DT) were developed to predict the peak strength. The XGboost model demonstrates superior predictive performance, emphasizing the significant influence of ice content and temperature on the peak strength of hydrates. Furthermore, molecular dynamics (MD) simulations were employed to gain insights into the dissociation and formation processes of clathrate cages, as well as phase transitions and amorphization occurring at grain boundaries (GBs) involving diverse unconventional clathrate cages, including 5¹²6⁵, 4¹5¹⁰6², 4¹5¹⁰6⁴, 4²5⁸6¹ and 4²5⁸6², with 4²5⁸6¹ and 4²5⁸6² cages being predominant. This study enhances our understanding of the mechanical properties and deformation mechanisms of hydrates and provides a ML-based predictive framework for estimating the compressive strength of hydrates under diverse coupling conditions. The findings have significant implications for stability assessments of NGHs and the exploitation of NGH resources.