Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme

This study explores the use of machine learning algorithms to predict hydrogen wettability in underground storage sites. The motivation for this research is the need to find a safe and efficient way to store hydrogen, which has become increasingly important as the world shifts toward using more clean energy sources. The study used four different machine learning algorithms including XGBoost, RF, LGRB, and Adaboost_DT to analyze 513 data points collected from previous literature. The input features included pressure, temperature, salinity, and substrate types, while the target output was hydrogen wettability. This study found that the XGBoost algorithm with four inputs produced the most precise predictions with the highest R2 value of 0.941, the lowest RMSE value of 4.455, and MAE of 2.861 for the overall databank. Based on SHAP values, the substrates are the most impactful variables of the XGBoost model. The predicted hydrogen column height was also evaluated for a specific storage site in Australia's basalt formation. At 308 K, the predicted hydrogen column height decreased from 1991 to 1319 m, while at 343 K, it decreased from 1510 to 784 m. These predictions were compared to the real column height that fell from 1660 to 928 m in the same pressure range. Overall, the study's findings provide a valuable guide for predicting wettability and evaluating hydrogen column height in specific storage sites. The use of machine learning algorithms can significantly reduce the time, cost, and unpredictability associated with traditional methods of assessing hydrogen wettability in underground storage sites.

Automated summary

This study uses machine learning algorithms to predict hydrogen wettability in underground storage sites. Four different algorithms were used and XGBoost produced the most precise predictions. The study also evaluated the predicted hydrogen column height in a specific storage site and found that it matched well with the real column height. Overall, the findings of this study provide a valuable guide for predicting wettability and evaluating hydrogen column height, and the use of machine learning algorithms can reduce time, cost, and unpredictability.