Uncertainty Quantification for Geothermal Basin- and Reservoir-Scale Applications

Numerical simulations of the governing geophysical processes are crucial for geothermal applications in order to characterize the subsurface. This characterization presents us with major challenges ranging from the correct physical and geometrical characterization to the quantification of uncertainties. Quantifying rock physics uncertainties and performing other probabilistic inverse methods is, even with current state-of-the-art finite element solver and high-performance infrastructures, computationally not feasible for complex basin- and reservoir-scale geothermal applications due to the large spatial, temporal, and parametric domain of the applications. Therefore, a common approach is to construct, for instance, models with a lower degree of resolution. The consequence of this is a significant loss of the information content of the model. Hence, with these models, we fail to improve the characterization of the subsurface, as we will demonstrate in this work. As an alternative, we propose to construct a surrogate model by using the reduced basis method. The reduced basis method constructs low-dimensional models while maintaining the input-output relationship. Hence, we do not restrict our physical domain. In this presentation, we demonstrate how this concept can be used for enabling a combined workflow of global sensitivity analysis and uncertainty quantification to improve our understanding and characterization of the subsurface.