Petrophysical analysis of regional-scale thermal properties for improved simulations of geothermal installations and basin-scale heat and fluid flow

TL;DR

This study analyzes petrophysical properties of rocks in the Molasse Basin to improve geothermal and basin-scale heat flow models by developing a multi-step data analysis procedure that refines property estimates and reduces uncertainties.

Contribution

It introduces a three-step analysis method combining statistics, petrophysical relationships, and inversion techniques to better estimate rock properties and their uncertainties.

Findings

Uncertainty in thermal conductivity can vary from 0.1 to 0.8 W/(m K).

Refined mineral property estimates achieve an accuracy of 0.3 W/(m K).

Predictive measurement errors are 0.07 W/(m K), 70 m/s, and 8 kg/m^3.

Abstract

Development of geothermal energy and basin-scale simulations of fluid and heat flow both suffer from uncertain physical rock properties at depth. Therefore, building better prognostic models are required. We analysed 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. Here, we propose a three-step procedure with increasing complexity for analysis of the data set: First, univariate descriptive statistics provides 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) or as low as 0.1 W/(m K). 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). The predictive errors for the measurements are 0.07 W/(m K), 70 m/s, and 8 kg/m^3 for thermal conductivity, sonic velocity, and bulk density, respectively.