The societal importance of geothermal energy is significantly increasing
because of its low carbon-dioxide footprint. However, geothermal
exploration is also subject to high risks. For a better assessment of
these risks, extensive parameter studies are required that improve the
understanding of the subsurface. This yields computationally demanding
analyses. Often this is compensated by constructing models with a small
vertical extent. This paper demonstrates that this leads to entirely
boundary-dominated and hence uninformative models. It demonstrates the
indispensable requirement to construct models with a large vertical
extent to obtain informative models with respect to the model
parameters. For this quantitative investigation, global sensitivity
studies are essential since they also consider parameter correlations.
To compensate for the computationally demanding nature of the analyses,
a physics-based machine learning approach is employed, namely the
reduced basis method, instead of reducing the physical dimensionality of
the model. The reduced basis method yields a significant cost reduction
while preserving the physics and a high accuracy, thus providing a more
efficient alternative to considering, for instance, a small vertical
extent. The reduction of the mathematical instead of physical space
leads to less restrictive models and, hence, maintains the model
prediction capabilities. The combination of methods is used for a
detailed investigation of the influence of model boundary settings in
typical regional-scale geothermal simulations and highlights potential
problems.