Global sensitivity analysis to optimize basin-scale conductive model calibration – A case study from the Upper Rhine Graben. (September 2021) - Degen, Denise; Veroy, Karen; Freymark, Jessica; Scheck-Wenderoth, Magdalena; Poulet, Thomas; Wellmann, Florian
Highlights: Comparison of local and global sensitivity analysis demonstrating the need of global sensitivity analyses for robust model calibrations. Compensation for data sparsity through different weighting schemes. Physics-based machine learning approach to ensure the feasibility of the study. Robust sensitivity-driven model calibration to reliably identify model errors. Compensation of model errors through model calibration. Abstract: Calibrating geothermal simulations is a critical step, both in scientific and industrial contexts, with suitable model parameterizations being optimized to reduce discrepancies between simulated and measured temperatures. Here we present a methodology to identify model errors in the calibration and compensate for measurement sparsity. With an application to the Upper Rhine Graben, we demonstrate the essential need for global sensitivity studies to robustly calibrate geothermal models, showing that local studies overestimate the influence of some parameters. We ensure the feasibility of the study through a physics-based machine learning approach (reduced basis method), reducing computation time by several orders of magnitude.