Nanomaterial-Enhanced Organic-Rich Shales: Data-Driven Insights into Wettability Predictions for Underground Hydrogen Storage Design

Abstract Shale caprocks, often organic-rich, play a significant role in hydrogen sequestration, but their wettability is typically altered from water-wet to H2-wet due to the organic matter, compromising their sealing performance. Silica nanofluids can restore water-wet conditions, which is vital for effective containment of H2 in geological formations. However, laboratory experiments to test the effects of nanomaterials on wettability and interfacial tension (IFT) are resource-intensive and time-consuming. To address this challenge, data-driven machine learning (ML) models offer a promising alternative, allowing for the prediction of key UHS parameters—such as advancing and receding contact angles without the need for extensive laboratory testing. We have applied multiple ML models, including gradient boosting, extreme gradient boosting (XGboost), Catboost, random forest, and extra trees to predict the wettability of organic rich shale samples treated with silica nanofluids. The ML models were trained on a comprehensive dataset generated through laboratory experiments conducted under a wide range of pressure (0.5 – 1600psi) and temperature (298-323 K) conditions, using brine (1 wt.% KCl + 2 wt.% NaCl) and varying silica nanofluid concentrations (0.05–1.0 wt.%). Advanced visualization techniques, including swarm maps, box plots, and heatmap plots, were utilized to analyze the experimental dataset thoroughly. To enhance the generalization capabilities of the ML models, we employed Bayesian optimization for hyperparameter tuning, along with k-fold cross-validation and grid-search optimization techniques. Both statistical and graphical analyses were performed to show the reliability and performance of the models. The results demonstrated that the implemented ML models, particularly the XGboost and Catboost, accurately predicted the wettability behavior under various operating conditions. The training and testing average R² scores were above 0.998 and 0.949, respectively, confirming the high accuracy of the predictions. These findings highlight the reliability of the ML algorithms in predicting the complex interaction between shale, brine, and H2. Further investigation into the feature importance revealed that nanomaterials had the greatest influence on the wettability of the ternary system. The silica nanofluid concentrations played a key role in modifying wettability, with varying impacts on the receding contact angles, emphasizing the effect of nanomaterial treatment. The model's predictions were consistent with experimental results, suggesting that geological conditions, including pressure and temperature, have a profound influence on rock mineral wettability, directly impacting H2 geo-storage capacity. This study is the first to employ data-driven ML models to predict critical UHS parameters in organic-rich shales treated with silica nanofluids. The ability of the ML models to predict UHS parameters across a wide range of conditions provides a valuable tool for optimizing UHS system designs, offering a more efficient and scalable approach compared to traditional experimental methods.