Getech, a world-leading locator of subsurface resources, has launched a solution to pinpoint sites rich in natural hydrogen.
Combining knowledge of natural hydrogen’s genetic systems with Getech’s proprietary data platform, products and machine learning analysis, the firm can predict the location of natural hydrogen deposits in the subsurface.
This emerging low-carbon, cost-efficient energy source has significant potential to support industrial decarbonisation.
Often referred to as white or gold hydrogen, natural hydrogen is a promising commodity, with recent significant investments in new explorers including Denver-based Koloma, which has raised a reported $91 million (€83 million) in funds from investors including Microsoft founder Bill Gates.
Getech’s approach categorises sources, migration paths, reservoir traps and seals which are then integrated with its data tools including the Globe digital platform.
The platform models the earth’s subsurface and uses computational modeling and AI machine learning to provide favourability maps, identifying the sweet spots for developing natural hydrogen.
Richard Bennett, executive chairman of Getech, says: ‘Natural hydrogen has huge potential as an efficient energy source as it has no greenhouse gas emissions on combustion and can therefore replace carbon-intensive fuels in many applications as part of the energy transition.’
He adds: ‘We combine our deep understanding of the processes behind the formation of natural hydrogen resources with our global platform of geological, geophysical and past-climate data to identify the locations of potential new discoveries. These results can be invaluable during initial exploration screenings that help locate and quantify sites of interest and de-risk subsequent development phases.’
Getech has significant expertise in locating subsurface critical minerals and has already successfully deployed approaches to target sediment-hosted copper, zinc-lead and sedimentary lithium deposits and sees significant benefit in applying the proven genetic approach to target.
