The Impacts of Igneous Systems on Sedimentary Basins and their Energy Resources
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Igneous processes within sedimentary basins impact energy resource plays across a range of scales from regional tectonics to reservoir porosity. Understanding these interactions has a direct influence on hydrocarbon, carbon capture, geothermal, hydrogen and helium projects. This volume demonstrates state of the art thinking around these often complex systems.
A new exploration tool in the search for native hydrogen and helium Available to Purchase
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Published:August 14, 2024
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CitationCarolina Olivares, Jennifer Findlay, Ranald Kelly, Simon Otto, Max Norman, Michael Cairns, 2024. "A new exploration tool in the search for native hydrogen and helium", The Impacts of Igneous Systems on Sedimentary Basins and their Energy Resources, B. Kilhams, S. Holford, D. Gardiner, S. Gozzard, L. Layfield, C. McLean, S. Thackrey, D. Watson
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Abstract
Native hydrogen and helium have been considered important resources in assisting the energy transition. Hydrogen and helium seeps have been reported worldwide, which may indicate large reserves within the subsurface. However, generation of hydrogen and helium is complex; poorly understood and constrained for both generation processes and migration. One source of native hydrogen is ultramafic rocks, which have experienced serpentinization together with water radiolysis. In contrast, helium generation occurs as the result of the radioactive decay of uranium and thorium present within radiogenically enriched basement. An exploration tool, dedicated to identifying areas with the geological settings and conditions favourable for native hydrogen and helium generation, has been developed and tested. Several databases have been created and integrated as part of this study (geological and geochemical generation models) to support and focus the search for both hydrogen and helium. Machine learning algorithms which extract value from geospatial data types for detecting various accumulations have been implemented. The first machine learning results demonstrate the significant value in integrating data and machine learning for high grading areas more conducive to accumulating hydrogen and helium.