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Uncertainty Quantification for Basin-Scale Geothermal Conduction Models
  • Denise Degen,
  • Karen Veroy,
  • Florian Wellmann
Denise Degen
RWTH Aachen University, RWTH Aachen University

Corresponding Author:[email protected]

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Karen Veroy
Eindhoven University of Technology, Eindhoven University of Technology
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Florian Wellmann
RWTH Aachen University, RWTH Aachen University
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Geothermal energy plays an important role in the energy transition by providing a renewable energy source with a low CO2 footprint. For this reason, this paper uses state-of-the-art simulations for geothermal applications, enabling predictions for a responsible usage of this earth's resource. Especially in complex simulations, it is still common practice to provide a single deterministic outcome although it is widely recognized that the characterization of the subsurface is associated with partly high uncertainties. Therefore, often a probabilistic approach would be preferable, as a way to quantify and communicate uncertainties, but is infeasible due to long simulation times. We present here a method to generate full state predictions based on a reduced basis method that significantly reduces simulation time, thus enabling studies that require a large number of simulations, such as probabilistic simulations and inverse approaches. We implemented this approach in an existing simulation framework and showcase the application in a geothermal study, where we generate 2D and 3D predictive uncertainty maps. These maps allow a detailed model insight, identifying regions with both high temperatures and low uncertainties. Due to the flexible implementation, the methods are transferable to other geophysical simulations, where both the state and the uncertainty are important.
10 Mar 2022Published in Scientific Reports volume 12 issue 1. 10.1038/s41598-022-08017-2