| Abstract |
Thermal conductivity is one of the key properties of geothermal studies and other applications, like petroleum geology, applications to geothermal energy, civil engineering applications and hydro-geological studies. Due to difficult measurements of the thermal conductivity in boreholes, in most cases only laboratory values are available. Therefore the knowledge of correlations between the thermal conductivity with other petrophysical properties measurable in wells or from the surface (seismic wave velocity, electrical resistivity) could deliver indirectly thermal conductivity. Our approach was to correlate thermal conductivity with compressional wave velocity for magmatic rocks and sandstone as well as with electrical resistivity for carbonates. Compressional wave velocity, thermal conductivity, electrical resistivity and porosity were therefore determined in our laboratory at different rock types. Two models are applied in order to formulate the basic structure of a correlation between compressional wave velocity and thermal conductivity: a defect and an inclusions model. The solid mineral composition values are taken from the literature. The types of rock are divided into: granite and gneiss, gneiss respectively granite with higher content of quartz, basalt/gabbro/diorite and sandstone. Groups indicate a petrographic code as a property which controls the correlation. Both models give a good fit to measured data. With the derived equations a calculation of the thermal conductivity log out of a sonic log was possible. Next step was to use the inclusions model and the Archie equation for the calculation of thermal conductivity and electrical resistivity for carbonates, where the other correlations did not work. The derived equations for dolomite and limestone were further applied on resistivity logs, which worked well. Summarized it can be said that the developed petrographic coded correlations out of the laboratory values worked well for the selected rocks. They show the two important factors that influence the thermal conductivity: the effect of mineral composition and cracks/fractures/pores. The prediction of the thermal conductivity log out of sonic and resistivity log worked well. |