Estimating the Subsurface Thermal Conductivity and Its Uncertainty for Shallow Geothermal Energy Use—A Workflow and Geoportal Based on Publicly Available Data

Abstract

Ground-source heat pumps with borehole heat exchangers (BHE) are an efficient and sustainable option to heat and cool buildings. The design and performance of BHEs strongly depend on the thermal conductivity of the subsurface. Thus, the first step in BHE planning is often assisted by a map representing the thermal conductivity of a region created from existing data. Such estimates have high uncertainty, which is rarely quantified. In addition, different methods for estimating thermal conductivity are used, for example, by the German federal states, resulting in incomparable estimates. To enable a consistent thermal conductivity estimation across state or country borders, we present a workflow for automatically estimating the thermal conductivity and its uncertainty up to user-defined BHE lengths. Two methods, which assess the thermal conductivity on different scales, are developed. Both methods are (1) based on subsurface data types which are publicly available as open-web services, and (2) account for thermal conductivity uncertainty by estimating its lowest, mean, and maximum values. The first method uses raster data, e.g., of surface geology and depth to groundwater table, and provides a large-scale estimate of the thermal conductivity, with high uncertainty. The second method improves the estimation for a small, user-defined target area by calculating the thermal conductivity based on the available borehole data in that area. The presented approach’s novelty is a web-based geodata infrastructure that seamlessly connects data provision and calculation processes, with a geoportal as its central user interface. To demonstrate the approach, we use data from the federal state of Hamburg and compare the results of two target areas with the thermal conductivity estimation by the Geological Survey of Hamburg. Depending on the selected region, differences between the two estimates can be considerable (up to 1.2 W m−1 K−1 ). The differences are primarily due to the selection of the thermal property database and the consideration of wet and dry rock. The results emphasize the importance of considering and communicating uncertainty in geothermal potential estimates.