Identifying the geothermal and heat storage potential of the shallow to deep subsurface of the North German Basin

The North German Basin consists of a thick sequence of sedimentary rocks featuring several Mesozoic sandstone reservoirs. These reservoirs have an enormous potential to provide green and sustainable geothermal heat and to act as a large-scale storage for injected heat. In our study, we focus on the estimation of both potentials, in particular the ability of the underground to store heat. For the North German Basin, this remains a challenging task. Based on sparse reservoir data of volatile quality, the structural and depositional architecture of the underground needs to be integrated in adequate 3D subsurface models. A major improvement was recently published in the BGR TUNB project in the form of a basin-wide consistent structural interpretation of depth horizons of the main lithostratigraphic units. We combined these structural data with temperature data from a published numerical 3D model and the geostatistical model of the geothermal information system (GeotIS). We also performed a well-log based reservoir rock component identification to estimate the reservoir net sand thicknesses and provided technical boundary conditions. The data cumulated in the computation of the geothermal heat in place and the potential for heat storage for five reservoir units and the facies therein. The analysis reveals large potentials in geologically favorable regions for both geothermal heating and aquifer thermal energy storage for all geothermal reservoirs. As an additional step, we superimposed areas of high population density and high heat demand to identify the regions where both supply and demand coexist. The results show that there are many areas of high demand (i.e., high population density, high industrial heat demand) spatially aligned with areas of high potential. However, the structural and numerical models, while being state of the art, still have uncertainties in the input data, which calls for caution when interpreting the results. Therefore, the applied methods and combined data are most powerful in identifying promising regions for economic subsurface utilization, but should be combined with detailed geological analysis beforehand to decrease exploration risk.