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.