Phase Stability of CH4 and CO2 Hydrates under Confinement Predicted by Machine Learning

Understanding the phase stability of gas hydrates under confinement is fundamental to the geological stability evolutions of gas hydrate systems on Earth. Herein, the phase stability of CH4 and CO2 hydrates under confinement is predicted by machine learning. Three machine learning models, including support vector machine, random forest, and gradient boosting decision tree, are constructed to predict the phase stability of CH4 and CO2 hydrates under confinement. Our machine learning results show that the prediction accuracy of the support vector machine model is highest, yet the prediction accuracy of the random forest model is lowest among those machine learning models in determining the phase stability of confined gas hydrates. Based on their performance in predicting the phase stability of confined gas hydrates, the support vector machine model with a training set fraction of 0.7 is finally chosen to deal with the unknown phase stability of confined gas hydrates. Importantly, the average accuracy of the support vector machine model can reach more than 90% in predicting the unknown phase stability of both CH4 and CO2 hydrates. The trained machine learning models can help us to quickly and accurately determine the phase stability of CH4 and CO2 hydrates under confinement in future applications.