624ed66f-f038-4380-aa67-54320a010855 eng utf8 series Marianne B. Wiese Geological Survey of Denmark and Greenland (GEUS) mbw@geus.dk pointOfContact 2022-04-28 ISO 19115/INSPIRE_TG2/CZ4 2003/cor.1/2006 ETRS89 (4258) [INSPIRE] Local - depth Deep Geothermal 3D Model of the Danish subsurface 2022-04-28 publication 2016 modelDigital Information about the deep geological setting in Denmark originates largely from hydrocarbon exploration activities with seismic profiles and wells covering most of the country, however with an uneven distribution (Vosgerau et al. 2016). The interpretation of these data provides information on the regional structural setting and spatial distribution of sedimentary units. The derived subsurface 3D structural (and geological) model with main lithological units includes information on potential geothermal reservoirs with burial depth and spatial distribution. Well data contain information about the reservoir quality as well as information on temperature and geochemistry of the formation water, where such data were measured (e.g., Weibel et al. 2017; Kristensen et al. 2016; Olivarius et al. 2015). Five important geothermal reservoirs have been identified based on their stratigraphical and spatial extent known for well data. Focus has been on the combined Bunter Sandstone-Skagerrak reservoir and the Gassum reservoir, with current geothermal production (Mathiesen et al. 2020). The 3D geological model is included in a user-friendly WebGIS portal from 2015 which has reduced the exploration risks significantly and has stimulated the interest from the industry. It provides a robust and consistent frame for more comprehensive estimates of the geothermal potential. Estimates in local geothermal license areas must be based on a more detailed analysis of the local dataset defining local geological models that may serve as the geoscientific background for technical and economic considerations. Marianne B. Wiese Geological Survey of Denmark and Greenland (GEUS) mbw@geus.dk custodian Anders Mathiesen Geological Survey of Denmark and Greenland (GEUS) anm@geus.dk pointOfContact unknown Geology GEMET - INSPIRE themes, version 1.0 2008-06-01 publication National Spatial scope 2019-05-22 publication model geothermal energy geothermal potential GeoERA Keyword Thesaurus 2020-01-01 publication Geothermal potential otherRestrictions Creative Commons BY-SA 4.0 The 3D geological model is a result of regional interpretation and mapping to identify regional structural setting and spatial distribution of sedimentary units. Estimates in local geothermal license areas must be based on a more detailed analysis of the local dataset defining local geological models that may serve as the geoscientific background for technical and economic considerations. otherRestrictions no limitations to public access grid 400 dan eng geoscientificInformation 8.074 15.193 54.56 57.752 Denmark 1970 2015 800 3000 ASCII GRID unknown https://3dviewer.europe-geology.eu/?model_id=xx WWW:LINK-1.0-http--link information https://data.geus.dk/geoterm/ WWW:LINK-1.0-http--link information COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See referenced specification The geographical coverage and quality of the data vary considerably. The mostly 2D seismic data combined with information from deep wells have been used for mapping of depth, thickness and lateral extent of lithostratigraphic units, and with special emphasis on units known to contain geothermal reservoir sandstones, as well as for identification and mapping of major faults (Vosgerau et al. 2016). Regional maps were interpreted in two-way travel time (TWT) and converted to depth using well information. The deepest mapped seismic reflector is the Top Pre-Zechstein horizon. The lack of coverage and high-quality data hampers the interpretation and mapping in local areas and the mapping of the deepest seismic horizons and consequently, mapping uncertainties can be large in areas with limited data coverage.