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Precise knowledge of the subsurface thermal field plays a key role in the assessment of geothermal targets. Unfortunately, deep underground temperature data is generally scarce and a matter of research. To achieve first estimates for subsurface temperatures, steady-state conductive thermal modeling is commonly applied. Thereby the rock thermal conductivity is an essential parameter, which is usually determined under ambient laboratory conditions. To arrive with in situ thermal conductivity, the ambient values need to be corrected for in situ temperature and pressure. In this paper, we apply different conversion functions for the correction of thermal conductivity and study the impact on the resultant temperature and heat flow prognoses for a synthetic, upper crustal sedimentary and a magmatic scenario along 2-D geological cross sections. Application of the correction functions results in maximum temperature prognosis uncertainties of about 8 °C and 55 °C at 2 km depth and at 8 km depth, respectively. The effect positively correlates with the magnitude of the basal heat flow used in modeling. In contrast to the heat flow determined at depth, the resulting surface heat flow is only minor affected by the different correction functions applied. In addition, the modeled temperature at depth is strongly dependent on the type and sequence of application of the pressure and temperature correction equations.

Data on rock thermal conductivity (TC) are important for the quantification of the subsurface temperature regime and for the determination of heat flow. If drill core is not retrieved from boreholes and thus no laboratory measurement of TC can be made, other methods are desired to determine TC. One of these methods is the prediction of TC from well logs. We have examined the relationships between TC and standard well-log data (gamma ray, density, sonic interval transit time, hydrogen index and photoelectric factor) by a theoretical analysis and by using real subsurface data from four boreholes of the North German Basin. The theoretical approach comprised the calculation of TC from well-log response values for artificial sets of mineral assemblages consisting of variable contents of 15 rock-forming minerals typical for sedimentary rocks. The analysis shows different correlation trends between TC and the theoretical well-log response in dependence on the mineral content, affecting the rock matrix TC, and on porosity. The analysis suggests the development of empirical equations for the prediction of matrix TC separately for different groups of sedimentary rocks. The most valuable input parameters are the volume fraction of shale, the matrix hydrogen index and the matrix density. The error of matrix TC prediction is on the order of 4.2 ± 3.2 per cent (carbonates), 7.0 ± 5.6 per cent (evaporites), and 11.4 ± 9.1 per cent (clastic rocks). From the subsurface data, comprising measured TC values (n = 1755) and well-log data, four prediction equations for bulk TC were developed resembling different lithological compositions. The most valuable input parameters for these predictions are the volume fraction of shale, the hydrogen index and the sonic interval transit time. The equations predict TC with an average error between 5.5 ± 4.1 per cent (clean sandstones of low porosity; Middle Buntsandstein), 8.9 ± 5.4 per cent (interbedding of sandstone, silt- and claystones; Wealden), and 9.4 ± 11 per cent (shaly sandstones; Stuttgart Fm.). An equation including all clastic rock data yields an average error of 11 ± 10 per cent. The subsurface data set also was used to validate the prediction equation for matrix TC established for clastic rocks. Comparison of bulk TC, computed from the matrix TC values and well-log porosity according to the geometric-mean model, to measured bulk TC results in an accuracy