TY - THES

T1 - Petrographic Coded Modelling of Thermal Conductivity

AU - Kienler, Markus

N1 - embargoed until null

PY - 2016

Y1 - 2016

N2 - In this Master thesis the relationship between the thermal conductivity and different petrophysical parameters (electrical resistivity, compressional wave velocity)are analyzed. As a basis for the various model calculations, laboratory measurements from the Department of Applied Geophysics of the Montanuniversitaet Leoben are used. In most drilling projects, core data is not available and an indirect determination of the thermal conductivity of the various layers would lead to large cost savings. For this purpose, the laboratory measurements are separated according to different lithologies. Thereafter, a subdivision of the samples, on the basis of the pore space, is done using electrical properties. In the next step, the thermal conductivity of the rock matrix is determined under consideration of cementation factor and pore shape (aspect ratio). Now model calculations (inclusion model) can be applied to correlate the relationship between electrical properties and the compressional wave velocity with the thermal conductivity. These calculated regression lines represent the basis for modeling a "thermal conductivity log" in the borehole. Using the example of the continental deep drilling project in Germany the model calculations for the lithologies granite/gneiss and basalt are applied and compared with the measured data of the thermal conductivity. Correlation of the compressional wave velocity model with the thermal conductivity shows a very good agreement. The electrical resistivity model only can describe the lithology granite/gneiss adequately. For Basalt, the regression leads to increased results. For comparison of the calculated regressions (from Sonic- and Resistivity log) a "Multiple Linear Regression" is used, in which the thermal conductivity is determined using various logs. In another chapter, the thermal conductivity is calculated from the geometric-mean model. Here, the pore space is determined with the neutron log and a very jagged curve is the result. The best fit for thermal conductivity calculation give the compression wave velocity model and the multiple linear regression. A problem for all models, which has to be kept in mind, is the anisotropy of rocks. This effect leads to a wide range of the measured thermal conductivity data.

AB - In this Master thesis the relationship between the thermal conductivity and different petrophysical parameters (electrical resistivity, compressional wave velocity)are analyzed. As a basis for the various model calculations, laboratory measurements from the Department of Applied Geophysics of the Montanuniversitaet Leoben are used. In most drilling projects, core data is not available and an indirect determination of the thermal conductivity of the various layers would lead to large cost savings. For this purpose, the laboratory measurements are separated according to different lithologies. Thereafter, a subdivision of the samples, on the basis of the pore space, is done using electrical properties. In the next step, the thermal conductivity of the rock matrix is determined under consideration of cementation factor and pore shape (aspect ratio). Now model calculations (inclusion model) can be applied to correlate the relationship between electrical properties and the compressional wave velocity with the thermal conductivity. These calculated regression lines represent the basis for modeling a "thermal conductivity log" in the borehole. Using the example of the continental deep drilling project in Germany the model calculations for the lithologies granite/gneiss and basalt are applied and compared with the measured data of the thermal conductivity. Correlation of the compressional wave velocity model with the thermal conductivity shows a very good agreement. The electrical resistivity model only can describe the lithology granite/gneiss adequately. For Basalt, the regression leads to increased results. For comparison of the calculated regressions (from Sonic- and Resistivity log) a "Multiple Linear Regression" is used, in which the thermal conductivity is determined using various logs. In another chapter, the thermal conductivity is calculated from the geometric-mean model. Here, the pore space is determined with the neutron log and a very jagged curve is the result. The best fit for thermal conductivity calculation give the compression wave velocity model and the multiple linear regression. A problem for all models, which has to be kept in mind, is the anisotropy of rocks. This effect leads to a wide range of the measured thermal conductivity data.

KW - Thermal Conductivity

KW - model calculations

KW - Wärmeleitfähigkeit

KW - Modellberechnungen

M3 - Master's Thesis

ER -