Swelling Phenomena of the Nonswelling Clay Induced by CO2 and Water Cooperative Adsorption in Janus-Surface Micropores

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.

Nanoscale Understanding on CO2 Diffusion and Adsorption in Clay Matrix Nanopores: Implications for Carbon Geosequestration

Carbon capture and storage (CCS) in subsurface reservoirs represents a highly promising and viable strategy for mitigating global carbon emissions. In the context of CCS implementation, it is particularly crucial to understand the complex molecular diffusive and adsorptive behaviors of anthropogenic carbon dioxide (CO2) in the subsurface at the nanoscale. Yet, conventional molecular models typically represent only single-slit pores and overlook the complexity of interconnected nanopores. In this work, finite kaolinite lamellar assemblages with abundant nanopores (r < 2 nm) were used. Molecular dynamics simulations were performed to quantify the spatial distribution correlations, adsorption preference, diffusivity, and residence time of the CO2 molecules in kaolinite nanopores. The movement of the CO2 molecules primarily occurs in the central and proximity regions of the siloxane surfaces, progressing from larger to smaller nanopores. CO2 prefers smaller nanopores over larger ones. The diffusion coefficients increase, while residence times decrease, with the pore size increasing, differing from typical slit-pore models due to the pore shape and interconnectivity. The perspectives in this study, which would be challenging in conventional slit-pore models, will facilitate our comprehension of the CO2 molecular behaviors in the complex subsurface clay sediments for developing quantitative estimation techniques throughout the CCS project durations.