Data-driven strategy for contact angle prediction in underground hydrogen storage using machine learning

作者：Mehdi Nassabeh, Zhenjiang You, Alireza Keshavarz, Stefan Iglauer · 发表于：Journal of Energy Storage · 年份：2025 · DOI：10.1016/j.est.2025.115908 · 被引用次数：17 · 研究领域：Methane Hydrates and Related Phenomena、Superconducting Materials and Applications、Coal Properties and Utilization

In response to the surging global demand for clean energy solutions and sustainability, hydrogen is increasingly recognized as a key player in the transition towards a low-carbon future, necessitating efficient storage and transportation methods. The utilization of natural geological formations for underground storage solutions is gaining prominence, ensuring continuous energy supply and enhancing safety measures. However, this approach presents challenges in understanding gas-rock interactions. To bridge the gap, this study proposes a data-driven strategy for contact angle prediction using machine learning techniques. The research leverages a comprehensive dataset compiled from diverse literature sources, comprising 1045 rows and over 5200 data points. Input features such as pressure, injection rate, temperature, salinity, rock type, and substrate were incorporated. Various artificial intelligence algorithms, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Feedforward Deep Neural Network (FNN) and Recurrent Deep Neural Network (RNN), were employed to predict contact angle, with the FNN algorithm demonstrating superior performance accuracy compared to others. The strengths of the FNN algorithm lie in its ability to model nonlinear relationships, scalability to large datasets, robustness to noisy inputs, generalization to unseen data, parallelizable training processes, and architectural flexibility. Results show that the FNN algorithm demonstrates higher acc...