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DGGV-E-Publikationen

Titel: Hydro-Mechanical Simulation in Geothermal Reservoirs: Physics and Surrogate Modeling

Autoren:
Ryan Santoso1, Denise Degen1, Mauro Cacace2, Florian Wellmann1

Institutionen:
1Computational Geoscience and Reservoir Engineering (CGRE), RWTH Aachen University, Germany; 2German Research Center for Geoscience (GFZ), Germany

Veranstaltung: GeoKarlsruhe 2021

Datum: 2021

DOI: 10.48380/dggv-1jcx-sn57

Zusammenfassung:
Hydro-mechanical (HM) simulations are essential aspects of geothermal reservoir studies to assess the heat production and the associated-environmental impacts, such as seismicity. HM simulations are numerically expensive (especially for large-scale simulations) since they require a relatively fine mesh to capture the complex interplay between the fluid-flow and geomechanical processes. This aspect makes it difficult to perform detailed studies on uncertainties in HM simulations. In this work, we present a comprehensive review and numerical demonstrations about critical elements in HM simulations for geothermal applications. We then discuss potential surrogate models to reduce the computational cost in performing the simulations, specifically for uncertainty quantification and optimization purposes.

There are four important elements in HM simulations for geothermal applications: the equation of state, the porosity-permeability relationship for both the matrix and fracture, the stress-dependent porosity in the matrix and fracture, and lateral and vertical heterogeneities. We compile the discussion from more than 60 papers and numerically show the significance of these parameters using the MOOSE simulator. The incorporation of these parameters into HM simulations leads to realistic descriptions of geothermal applications. However, accommodating for these complex physics also elevates the computational cost.

We compile surrogate-modeling approaches (dedicated for HM problems) from more than 40 papers. The approaches span from reduced-basis to polynomials chaos expansion methods and machine learning approaches. We found that the combination of reduced-basis methods and machine learning approaches enables to effectively deal with non-linearity in HM simulations, to preserve the physics, and to reduce computational cost for further uncertainty quantification.



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