Datensatz

Zusammenfassung

[...] The approach followed in this study is based on the detailed analyses of the relationships between thermal conductivity of rock-forming minerals, which are most abundant in sedimentary rocks, and the properties measured by standard logging tools (i.e., gamma ray, density, sonic interval transit time, hydrogen index, and photoelectric factor). By using multivariate statistics separately for clastic, carbonate and evaporite rocks, the findings from these analyses allow the development of prediction equations from large artificial data sets that predict matrix thermal conductivity within an error of 4 to 11%, without being affected by the limitations mentioned above.