Data-Driven Prediction of Storage Column Height for H₂-Brine Systems: Accelerating Underground Hydrogen Storage

A practical solution to energy transition and the increasing demand for energy is underground hydrogen storage (UHS). The contribution of hydrogen (H₂) as a clean energy source has proven to be an effective substitute for future use to meet the net-zero target and reduce anthropogenic greenhouse gas emissions. One of the most important factors affecting H₂ displacement and storage capacity under geological circumstances is storage column height. The objective of this study is to underscore the importance of large-scale H₂ storage and use reliable machine learning algorithms to evaluate and predict the H₂ storage column height under varied thermophysical and salinity conditions. In this study, the dataset of 540 datapoints for the evaluation and prediction of storage column height is generated, which involves three main parameters: density difference (Δρ), interfacial tension (IFT) and contact angle (θ). The correlation of contact angles against various reservoir depths is used and H₂ storage column height is evaluated. Thermophysical conditions include pressures (0.1-20 MPa), temperatures (25-70°C), and salinities including deionized water, seawater and brines of 1 and 3 molar concentrations for various salts (NaCl, KCl, MgCl₂, CaCl₂, and Na₂SO₄) from our experimental data. The H₂ storage column height (h) is predicted using three machine learning (ML) models, viz., random forest (RF), decision tree (DT) and gradient boosting (GB). Statistical data analysis is performed to generate the distribution of dataset and correlation coefficient is calculated while feature importance is determined to identify the relationship of each input parameter with output parameter using Pearson, Spearman, and Kendall models. RF and GB, as demonstrated in this study, have shown promising results in providing accurate predictions while maintaining generalizability. Various error assessment metrics including MSE, RMSE, MAPE and R² are utilized for the evaluation. Prediction of column height resulted in R² values of 0.995 for training and 0.999 for testing with RF model. Whereas the GB model also resulted in superior performance with R² values of 0.997 during the training phase and 0.995 during the testing phase. However, the DT model resulted in R² values of 1 and 0.994 during the training and testing phases respectively. While MSE value of 0 is obtained for DT model which indicated overfitting. The findings of this study suggest that data-driven ML models can be a powerful tool for accurately predicting the H₂ storage column height and can be effectively used to determine the displacement of H₂ and storage capacity, reducing the time and cost associated with determination using traditional methods. In addition, advanced ML algorithms can be explored in the future to overcome the challenges pertinent to the determination of storage column height.