A Multi-Scale Framework for Evaluating Hydrogen Generation in Serpentinization Settings

This study presents a versatile methodological framework, implemented as a Python-based tool called PoNHy (Potential for Natural Hydrogen), designed to assess hydrogen generation in serpentinization environments using geophysical and laboratory data. As a practical application, the approach robustness is demonstrated in the Mauleon Basin localized in the north-western Pyrenees, where extensive data availability facilitates detailed analyses and validation. The workflow begins with a thorough assessment of key petrophysical properties such as density, magnetic susceptibility, and thermal conductivity. These properties guide the interpretation of underlying geological structures and help refining the initial subsurface models. Building on this foundation, gravity and magnetic data are inverted to determine the distribution and volume of source rocks, as well as their degree of serpentinization. Thermal modeling then delineates subsurface temperature regimes, which play a critical role in the serpentinization reactions and subsequent hydrogen production. To translate laboratory-derived hydrogen production rates into realistic field estimates, the framework integrates parameters from both lab experiments and field observations. Factors such as the water-to-rock ratio, fracture spacing, mineral composition, and specific surface area of reacting materials influence fluid flow, reaction rates, and the overall efficiency of hydrogen generation. By integrating these parameters alongside corrections for the degree of serpentinization, our new methodology provides a more accurate representation of subsurface conditions. This comprehensive integration yields hydrogen generation estimates that better reflect in situ conditions, ultimately improving our understanding of natural hydrogen volumes. Such insights are critical for subsequent transport models aimed at identifying potential reservoirs.