Unraveling hydrogen induced geochemical reaction mechanisms through coupled geochemical modeling and machine learning

• Published in: Applied geochemistry Vol. 183; p. 106330
• Main Authors: Driba, D.L., Beckingham, Lauren E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2025; Elsevier
• Subjects: 08 HYDROGEN; algorithms; energy; evolution; Geochemical modeling; Geochemical reactions; geochemistry; Hydrogen; Hydrogen PhreeqC; Hydrogen storage; Machine learning; Non-negative matrix factorization; PhreeqC; Reaction stage; Reactive transport modeling; salinity; sulfates; temperature; Non-negative matrix factorization; PhreeqC; Reaction stage; Hydrogen; Geochemical modeling; Machine learning
• ISSN: 0883-2927
• DOI: 10.1016/j.apgeochem.2025.106330

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. This study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone. •Simulated H2-induced brine-mineral interactions using geochemical batch modeling.•Non-negative matrix factorization (NMF) based unsupervised machine learning technique used to analyze simulation output matrix.•NMF distinguishes four distinct reaction stages, aiding in interpreting reaction control mechanisms.