GEOLOGICAL HYDROGEN EXPLORATION: ROLES OF INTEGRATED GEOPHYSICS

Climate change is arguably one of the most pressing challenges of our times and the transition to a net-zero carbon energy supply is a key component to its solution. The transition will require a multifaceted approach that includes carbon capture utilization and storage (CCUS) and a significant build-up in renewable energy. However, the magnitude of CCUS and renewable energy development necessary to achieve this goal will require an unprecedented investment in new infrastructure and supply of raw materials (e.g., critical minerals), which are significant obstacles to meeting this objective. Consequently, all new forms of low-carbon energy are receiving attention. Within this context, there is a growing awareness of the potential for geological hydrogen (H₂) as a low-carbon primary energy resource that can readily be introduced into the existing energy supply. This is exemplified by the recent successful exploration and utilization of geologic H₂ in Mali and the ongoing assessment and exploration efforts in Australia, the U.S., and elsewhere. It is anticipated that geophysical data will play a crucial role in subsurface H₂ exploration, given the need to locate and characterize the geologic components that could lead to economic accumulations of geologic H₂, particularly at great depths. The complexity of geological settings conducive to hydrogen generation and accumulation will require the development of new integrated geophysical techniques for effective characterization of hydrogen system elements and delineation of potential resource targets. This presentation will discuss the integration and application of two major recent developments in exploration geophysics applicable to geologic H₂ exploration. The first is the efficient geophysical survey design and acquisition (e.g., ergodic sampling) to enable rapid mapping of large areas at minimal cost. The second development is geologic differentiation that predicts geology at great depths by integrating multiple geophysical datasets, for example gravity, magnetics, and electromagnetics. Machine learning is expected to play a critical role in the optimization of these approaches. Reconfiguring and recombining these innovations could lead to the most expedient route to the development of effective subsurface tools for geologic H₂ exploration.