Research
Superhot Rock Geothermal Energy (SRGE) involves harnessing heat from extremely hot rocks (>400 °C) deep within the Earth’s crust, playing a pivotal role in reducing greenhouse gas emissions and ensuring energy security. However, one of the key challenges in deep geothermal exploration is the lack of reliable and efficient technologies for accurately mapping crustal temperatures and assessing SRGE. This project aims to create a novel approach to characterize the potential of a region for deep geothermal energy production by combining heat-transfer finite-elements solvers, reduced order modelling techniques and probabilistic inversion of multiple geophysical data in a single inverse workflow. At the end, this tool will be intended to detect the best geothermal targets in the crust and/or lithospheric mantle, in the context of greenfield & brownfield exploration frameworks of deep geothermal energy.
Host
University of Twente
Expected Results
A conceptual and numerical data-fusion platform to detect relevant compositional anomalies in the crust and lithospheric mantle at scales relevant to greenfields/brownfields exploration frameworks. Application and validation of the methodology in selected regions of interest.