This dataset provides point-shapefiles and geotiffs, related to the figures presented in (Frick et al., 2022a, 2022b). It covers most of northern Germany, with the boundaries defined by the extent of the North German Basin, which is part of the Central European Basin System.
The files contain information on the depth (m.b.s. = meter below surface), thickness, temperature, heat in place and heat storage potential of selected geological units and the formations therein. These data are an addendum to the data presented in (Frick et al., 2022a, 2022b), resolving 5 geological units and 9 formations. The data are presented as regularly spaced point-shapefiles, with a spacing of 1000 m.
The data were produced as part of the Helmholtz Climate Initiative (HICAM), which focuses on Net Zero 2050 (mitigation) and Adapting to Extreme Events (adaptation). As part of this initiative, estimates of the heat in place and heat storage potential of the subsurface play an important part for mitigation of fossil fuel bound emissions as they pose a promising alternative (geothermal energy). The data presented here, therefore give an overview of areas which might be suited for geothermal applications in the different geothermal target units and formations. We integrated the recently published TUNB Model (BGR et al., 2021) as well as available borehole data, data from the Sandsteinfazies and GeoPoNDD projects (Franz et al., 2018, 2015) and temperature data from two models (Agemar et al., 2014; Frick et al., 2021) the process of which will be described in the following.
Py4HIP is an open-source software tool for Heat-In-Place calculations implemented as a self-explanatory Jupyter notebook written in Python (Py4HIP.ipynb)
Calculating the Heat In Place (HIP) is a standard method for assessing the geothermal potential for a defined geological unit (e.g., Nathenson, 1975; Muffler and Cataldi, 1978; Garg and Combs, 2015).
The respective implementation in Py4HIP is based on a volumetric quantification of contained energy after Muffler and Cataldi (1978), where the geological unit at hand is considered spatially variable in terms of its temperature, thickness, porosity, density and volumetric heat capacity of its solid and fluid (brine) components. The energy values provided by Py4HIP as ASCII lists and map representations correspond to the stored energy in J/m^2.