Thermal Conductivity from Core and Well log Data

TL;DR

This study develops and validates equations to predict rock thermal conductivity from wireline logging data with high accuracy, enabling thermal property estimation in boreholes lacking core samples.

Contribution

It introduces a regression-based method to estimate thermal conductivity from logging data, validated against laboratory measurements, applicable even with incomplete historical data.

Findings

Thermal conductivity can be predicted with an accuracy better than 0.2 W/(m K).

The method works well with old and incomplete logging data.

Predicted thermal conductivity aligns closely with laboratory measurements.

Abstract

The relationships between thermal conductivity and other petrophysical properties have been analysed for a borehole drilled in a Tertiary Flysch sequence. We establish equations that permit us to predict rock thermal conductivity from logging data. A regression analysis of thermal conductivity, bulk density, and sonic velocity yields thermal conductivity with an average accuracy of better than 0.2 W/(m K). As a second step, logging data is used to compute a lithological depth profile, which in turn is used to calculate a thermal conductivity profile. From a comparison of the conductivity-depth profile and the laboratory data it can be concluded that thermal conductivity can be computed with an accuracy of less than 0.3 W/(m K)from conventional wireline data. The comparison of two different models shows that this approach can be practical even if old and incomplete logging data is used. The results can be used to infer thermal conductivity for boreholes without appropriate core data that are drilled in a similar geological setting.