Titel: Increasing the knowledge base for Deep Geothermal Energy Exploration in the Aachen-Weisweiler area, Germany, through 3D probabilistic modeling with GemPy
Alexander Jüstel1,2, Florian Wellmann2, Frank Strozyk1
1Fraunhofer IEG, Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems, Am Hochschulcampus 1, 44801 Bochum, Germany; 2RWTH Aachen University, Computational Geoscience and Reservoir Engineering, Wüllnerstraße 2, 52062 Aachen, Germany
Veranstaltung: GeoKarlsruhe 2021
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, Germany, in order to replace district heat generated as a side product of coal burning. But how much information do we actually have about or need of the subsurface to carry out such a project?
The conducted investigations will provide a 3D geological and probabilistic subsurface model of the area between Aachen and Weisweiler created with the open-source package GemPy developed at RWTH Aachen University. This model is in contrast to established regional models and more detailed local models.
The geological structures between Aachen and Weisweiler represent a SW-NE striking syncline, the Inde Syncline, embedded in the Aachen fold-and-thrust belt. The syncline is offset by Cenozoic normal faults of the Lower Rhine Embayment. 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 Lower Rhine Embayment.
Results show that the Aachen fold-and-thrust belt and the down faulted fault blocks can be modeled integrating the available surface and sparse shallow subsurface data. The probabilistic modeling provides information about uncertainties of the target layers in the subsurface. It can be deduced that a planned exploration well for fall/winter 2021 will reduce uncertainties in the subsurface in the vicinity of the target layers enabling improved economic decisions.
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