Thermal conductivity is a decisive parameter in all geothermal applications. In addition to the influencing factors of density, saturation, porosity, temperature and pressure, it is, above all, the geochemical and mineralogical composition that determines the thermal conductivity in rocks and soils. This study focuses on selected rock samples from Southern and Central Germany regarding major element oxides and minerals as well as distributed thermal conductivity. We examined clastic and chemical sedimentary, as well as igneous and metamorphic rocks, ranging from the Paleozoic to Cenozoic age. Measurements were conducted by X-ray fluorescence analysis (XRF), X-ray diffraction (XRD) and optical scanning with a thermal conductivity scanner (TCS). The results show significant correlations between thermal and geochemical parameters. Chemical composition significantly impacts thermal conductivity. Higher quartz and SiO₂ contents generally lead to increased thermal conductivity, while aluminum silicates, common in clay minerals, correlate with lower conductivity. For carbonates, increased density or reduced porosity enhances conductivity. Structural differences and differing mineral concentrations influence the measurement variability along the sampling axis. This is especially visible in clastic sedimentary rock samples, where porosity decreases while cementation of the matrix increases thermal conductivity.

For large infrastructure projects, such as high-voltage underground cables or for evaluating the very shallow geothermal potential (vSGP) of small-scale horizontal geothermal systems, large-scale geothermal collector systems (LSCs), and fifth generation low temperature district heating and cooling networks (5GDHC), the thermal conductivity (λ) of the subsurface is a decisive soil parameter in terms of dimensioning and design. In the planning phase, when direct measurements of the thermal conductivity are not yet available or possible, λ must therefore often be estimated. Various empirical literature models can be used for this purpose, based on the knowledge of bulk density, moisture content, and grain size distribution. In this study, selected models were validated using 59 series of thermal conductivity measurements performed on soil samples taken from different sites in Germany. By considering different soil texture and moisture categories, a practicable guideline in the form of a decision tree, employed by empirical models to calculate the thermal conductivity of unconsolidated soils, was developed. The Hu et al. (2001) model showed the smallest deviations from the measured values for clayey and silty soils, with an RMSE value of 0.20 W/(m∙K). The Markert et al. (2017) model was determined to be the best-fitting model for sandy soils, with an RMSE value of 0.29 W/(m∙K).