Prediction of hydrogen−brine interfacial tension at subsurface conditions: Implications for hydrogen geo-storage

Le résumé fourni par la source

Underground hydrogen storage (UHS) offers a promising approach for the storage of significant volumes of hydrogen gas (H2) within deep geological formations, which can later be utilized for energy generation when necessary. Interfacial tension (IFT) between H2 and the formation brine plays a vital role in influencing the distribution of H2 at the pore scale and, ultimately, the storage capacity. In this research, we developed four intelligent models: Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP). These models were designed to predict the IFT utilizing pressure, temperature, and molality. Additionally, we fine-tuned three explicit correlations previously developed in our research. To assess the influence of each parameter on IFT, we conducted a comprehensive analysis of raw data to exclude doubtful samples. This was followed by rigorous model development, including hyperparameter tuning, and finally, an examination of developed models using testing data. The results clearly demonstrate the superiority of the RF model, achieving high accuracy and reliability with coefficients of determination (R2), root mean square error (RMSE), and average absolute relative deviation (AARD) values of 0.96, 1.50, and 1.84 %, respectively. The exemplary performance of the RF model can be attributed to its inherent characteristics. The ensemble approach of RF excels in capturing complex data relationships, thereby enhancing predictive accuracy and solidifying its superiority over other models in this study. Furthermore, the feature importance analysis revealed that temperature has the most significant influence, followed by molality and pressure. Moreover, we assessed the predictive accuracy of these models through external testing using data not used in the initial training and testing stages. Our study highlights the exceptional predictive power of the RF model, emphasizing the practical importance of selecting RF for enhanced accuracy and reliability. The proposed method shows significant potential for industrial applications, especially in optimizing underground H2 storage.

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