Titel: Increasing the knowledge base for Deep Geothermal Energy Exploration in the Aachen-Weisweiler area, Germany, through 3D probabilistic modeling with GemPy
Alexander Magnus Juestel (1), Florian Wellmann (1), Frank Strozyk (2) & Miguel De La Varga (1)
Department for Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Aachen (1); Fraunhofer Institute for Energy Infrastructures and Geothermal Systems, Bochum, Germany (2)
Veranstaltung: Abstract GeoUtrecht2020
Deep geothermal energy is a key to lower local and global CO2 emissions caused by the burning of fossil fuels. Different initiatives aim at establishing deep geothermal energy production at the Weisweiler coal-fired power plant near the city of Aachen in order to replace district heat generated as a side product of coal burning1,2. But how much information do we actually have about the subsurface to carry out such a project?
The conducted investigations will provide a 3D geological and probabilistic subsurface model created with the open-source package GemPy3 developed at RWTH Aachen University. This model is in contrast to established models by  and , discussed by  and constructed in more detail for the Weisweiler area by .
The geological structures between Aachen and Weisweiler represent a SW-NE striking syncline, the Inde Syncline, embedded in the Aachen fold-and-thrust belt (AFTB)8. The syncline is offset by Cenozoic normal faults of the Lower Rhine Embayment (LRE)9. The target layers comprise of karstic Lower Carboniferous Kohlenkalk platforms and Upper/Middle Devonian Massenkalk reef carbonates outcropping along the flanks and down faulted within the LRE10.
Results show that the AFTB and the down faulted fault blocks can be modeled integrating the available surface and shallow subsurface data (Fig. 1 + 2). The probabilistic modeling provides information about uncertainties of the target layers in the subsurface. It can be shown that a planned exploration well will reduce uncertainties in the subsurface in the vicinity of the target layers enabling improved economic decisions.
Ort: Aachen, Germany