Characterization of Natural Hydrogen Systems in Serpentinization Environments

A specialized workflow incorporating two innovative computing algorithms has been developed to understand and quantify natural hydrogen generation through the serpentinization of ultramafic rocks. This approach integrates geological, geophysical, structural, and petrophysical parameters. The area is divided into three critical zones: Surface, Shallow, and Deep. The Surface layer provides insights into hydrogen presence and migration paths. Detailed exploration of the reservoir and its seal is enabled by the Shallow layer, while the Deep layer focuses on the mechanisms behind hydrogen formation. Semi-circular structures indicative of natural hydrogen are detected by the NHSD (Natural Hydrogen Seeps Detection) algorithm, which applies deep learning to satellite imagery. In the Shallow layer, the reservoir seal is assessed using geological and geophysical modeling, which also examines the presence of fractures that act as migration paths. In the Deep layer, models employing gravity and magnetic data inversion, together with temperature distributions in a 3D model, target conditions favorable for serpentinization. The QNHG (Quantifying Natural Hydrogen Generation) algorithm calculates daily hydrogen production by scaling laboratory experiments to field conditions. This algorithm considers factors such as production rates, water/rock ratios, serpentinization front velocity, temperature, and fracture systems in peridotites—all adjusted to the characteristics of the study area. This refined system offers a comprehensive method for characterizing natural hydrogen environments, adaptable to various geological settings.