Petrophysical analysis of regional-scale thermal properties for improved simulations of geothermal installations and basin-scale heat and fluid flow
The development of geothermal energy and basin-scale simulations of fluid and heat flow both suffer from uncertain physical rock properties at depth. For the production of geothermal energy, a high risk of failure is associated with this uncertainty. Invoking the usual conservative assumptions as a remedy results in unnecessarily large drilling depths and increased exploration costs. Therefore, building better prognostic models for geothermal installations in the planning stage requires improvement of this situation. To this end we analysed systematically the hydraulic and thermal properties of the major rock types in the Molasse Basin in Southern Germany. On about 400 samples, thermal conductivity, density, porosity, and sonic velocity were measured in the laboratory. The size of both the study area and the this data set require special attention with respect to the analysis and the reporting of data, in particular in view of making it useful and available for practitioners in the field. Here, we propose a three-step procedure with increasing complexity, accuracy, and insight into petrophysical relationships: first, univariate descriptive statistics provide a general understanding of the data structure, possibly still with large uncertainty. Examples show that the remaining uncertainty can be as high as 0.8 W (m K)−1 or as low as 0.1 W (m K)−1. This depends on the possibility to subdivide the geologic units into data sets that are also petrophysically similar. Then, based on all measurements, cross-plot and quick-look methods are used to gain more insight into petrophysical relationships and to refine the analysis. Because these measures usually imply an exactly determined system they do not provide strict error bounds. The final, most complex step comprises a full inversion of select subsets of the data comprising both laboratory and borehole measurements. The example presented shows the possibility to refine the used mixing laws for petrophysical properties and the estimation of mineral properties. These can be estimated to an accuracy of 0.3 W (m K)−1. The predictive errors for the measurements are 0.07 W (m K)−1, 70 m s−1, and 8 kg m−3 for thermal conductivity, sonic velocity, and bulk density, respectively. The combination of these three approaches provides a comprehensive understanding of petrophysical properties and their interrelations, allowing to select an optimum approach with respect to both the desired data accuracy and the required effort.