Application of machine learning in modelling gas dispersion coefficients for hydrogen storage in porous media

Abstract

Underground hydrogen storage (UHS) in geological formations is recognized as a promising solution for managing renewable energy intermittency and supporting large-scale energy systems. However, the mixing of hydrogen with cushion gases such as methane (CH4), nitrogen (N2), and carbon dioxide (CO2) remains a critical challenge, directly affecting storage efficiency and gas purity. The gas dispersion coefficient (KL) plays a fundamental role in governing this mixing behaviour, yet its accurate prediction under realistic reservoir conditions has been limited in previous studies. This research presents a novel approach by integrating core flooding experiments with advanced machine learning (ML) techniques to estimate KL values with high precision. Unlike earlier studies that primarily relied on analytical models or limited experimental data, this work combines systematic ML modelling with extensive laboratory data to capture the complex, nonlinear nature of gas dispersion in porous media. The results indicate that support vector regression (SVR) provides superior predictive performance for all tested gases. Specifically, for CH4, N2, and CO2, the SVR model achieved coefficient of determination (R2) values of 0.9968, 0.9977, and 0.9973, respectively, along with low mean absolute deviation (MAD) values of 0.014, 0.008, and 0.013, and root mean square error (RMSE) values of 0.017, 0.011, and 0.016. These findings provide valuable insights for optimizing cushion gas selection and improving the accuracy of UHS system design, ultimately enhancing storage reliability and contributing to more efficient and sustainable large-scale hydrogen storage.