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

Abstract

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 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) -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 2 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.

Evaluation of common mixing models for calculating bulk thermal conductivity of sedimentary rocks: Correction charts and new conversion equations

“…This method was applied successfully to crystalline rocks (e.g., He et al, 2008;Popov et. al., 1999) as well as to sedimentary rocks (e.g., Clauser, 2006;Fuchs and Förster, 2010;Hartmann et al, 2005Hartmann et al, , 2008Homoth et al, 2008;Liu et al, 2011;Majorowicz et al, 2008;Mottaghy et al, 2005;Norden and Förster, 2006;Orilski et al, 2010;Popov et al, 1995Popov et al, , 2003Popov et al, , 2010Popov et al, , 2011Schütz et al, 2012). It involved the measurement of TC under ambient temperature and pressure, which is in contrast to the other widely used method, the divided-bar technique (DB).…”

Influence of depth, temperature, and structure of a crustal heat source on the geothermal reservoirs of Tuscany: numerical modelling and sensitivity study

“…Here, we use the geometric mixing law which has been proven to be a good theoretical description of the effective thermal conductivity of a rock/water mixture (Hartmann et al 2005(Hartmann et al , 2008. The thermal conductivity profiles derived from the logs are calibrated with the laboratory measurements (Fig.…”

Past climate changes and permafrost depth at the Lake El'gygytgyn site: implications from data and thermal modeling

“…Porosity was determined by using a defined volume and weighing the saturated and dry sample. This allows determination of the matrix thermal conductivity using the geometric mixing law, which proved to be a good theoretical description of the effective thermal conductivity of rocks with different components (e.g., Hartmann et al, 2008).…”