Estimating thermal conductivity of the Main Dolomite on the basis of mineral composition and porosity

Thermal conductivity is one of the parameters essential to determine thermal conditions of a geothermal reservoir. The value of this parameter depends on other rock properties, such as mineral composition and porosity. Therefore, mathematical models that take into account both the volumetric content and thermal conductivity of individual minerals and pore solutions as well as the rock structure, can be used to calculate the value of thermal conductivity. In this study, different types of mathematical models were used, from the simplest layer models to more complex non-spherical inclusion models. The calculated values were verified by laboratory measurements of thermal conductivity. The tests were carried out on dolomite samples characterized by similar mineral composition and varied porosity (from 3 to 27%). The porosity parameter has an apparent influence on particular mathematical models. The best fitting of measured and calculated values was obtained for samples with high porosity (above 25%), with the use of the arithmetic mean model. For samples with lower porosity, the best fit was obtained for harmonic mean, spherical inclusions (the case of a rock consisting of spherical grains dispersed in a pore solution), and non-spherical inclusions (the case of pores in the form of disc-shaped fractures) models. All models reflect well the variation trends obtained for the measured values. The correlations obtained are characterized by high determination coefficients R-2, exceeding 0.86. Based on high quality correlations, a correction allowing to approximate the modelled values to the laboratory measured ones was introduced. The results made it possible to select optimum models for estimating the value of thermal conductivity of dolomites based on their mineral composition. For rocks of very high porosity, exceeding 25%, the arithmetic mean model fitted best. For the other samples, the best fit was obtained after applying a correction; the results are very similar for all models used.