Preliminary studies for an integrated assessment of the hydrothermal potential of the Pechelbronn Group in the northern Upper Rhine Graben
Meike Hintze
CORRESPONDING AUTHOR
Geothermal Science and Technology, TU Darmstadt, 64287 Darmstadt,
Germany
Darmstadt Graduate School of Energy Science and Engineering,
64287 Darmstadt, Germany
Barbara Plasse
Geothermal Science and Technology, TU Darmstadt, 64287 Darmstadt,
Germany
Kristian Bär
Geothermal Science and Technology, TU Darmstadt, 64287 Darmstadt,
Germany
Ingo Sass
Geothermal Science and Technology, TU Darmstadt, 64287 Darmstadt,
Germany
Darmstadt Graduate School of Energy Science and Engineering,
64287 Darmstadt, Germany
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The assessement of fracture networks is a key element for underground reservoir studies. The available methods for such assessement are unfortunately very limited in the case of complex 3 dimensions geometries. The paper shows a new method to overcome these limitations through automatic detection from images of outcrops.
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The crystalline basement is considered a ubiquitous and almost inexhaustible source of geothermal energy in the Upper Rhine Graben. Interdisciplinary investigations of relevant reservoir properties were carried out on analogous rocks in the Odenwald. The highest hydraulic conductivities are expected near large-scale fault zones. In addition, the combination of structural geological and geophysical methods allows a refined mapping of potentially permeable zones.
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Sebastian Weinert, Kristian Bär, and Ingo Sass
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Leandra M. Weydt, Ángel Andrés Ramírez-Guzmán, Antonio Pola, Baptiste Lepillier, Juliane Kummerow, Giuseppe Mandrone, Cesare Comina, Paromita Deb, Gianluca Norini, Eduardo Gonzalez-Partida, Denis Ramón Avellán, José Luis Macías, Kristian Bär, and Ingo Sass
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Petrophysical and mechanical rock properties are essential for reservoir characterization of the deep subsurface and are commonly used for the population of numerical models or the interpretation of geophysical data. The database presented here aims at providing easily accessible information on rock properties and chemical analyses complemented by extensive metadata (location, stratigraphy, petrography) covering volcanic, sedimentary, metamorphic and igneous rocks from Jurassic to Holocene age.
Kristian Bär, Thomas Reinsch, and Judith Bott
Earth Syst. Sci. Data, 12, 2485–2515, https://doi.org/10.5194/essd-12-2485-2020, https://doi.org/10.5194/essd-12-2485-2020, 2020
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Machine learning techniques are a promising alternative for processing (phase segmentation) of 3-D X-ray computer tomographic rock images. Here the performance and accuracy of different machine learning techniques are tested. The aim is to classify pore space, rock grains and matrix of four distinct rock samples. The porosity obtained based on the segmented XCT images is cross-validated with laboratory measurements. Accuracies of the different methods are discussed and recommendations proposed.
S. Homuth, A. E. Götz, and I. Sass
Geoth. Energ. Sci., 3, 41–49, https://doi.org/10.5194/gtes-3-41-2015, https://doi.org/10.5194/gtes-3-41-2015, 2015
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Short summary
The presented study is conducted within the scope of the joint research project "Hessen 3D 2.0" (BMWI-FKZ: 0325944) and aims at assessing the hydrothermal potential of the Pechelbronn Group for direct heat use by means of an integrated 3-D structural-geothermal model that serves to locate potential exploration areas. The assessment is based on reservoir temperature, (net)thickness of the reservoir horizon as well as on petrophysical, thermal and hydraulic rock properties.
The presented study is conducted within the scope of the joint research project "Hessen 3D 2.0"...