Rock thermal conductivity is an essential input parameter for enhanced oil recovery methods design and optimization and for basin and petroleum system modelling. Absence of any effective technique for direct in situ measurements of rock thermal conductivity makes the development of well-log based methods for rock thermal conductivity determination highly desirable. A major part of the existing problem solutions is regression model-based approaches. Literature review revealed that there are only several studies performed to assess the applicability of neural network-based algorithms to predict rock thermal conductivity from well-logging data. In this research, we aim to define the most effective machine-learning algorithms for well-log based determination of rock thermal conductivity. Well-logging data acquired at a heavy oil reservoir together with results of thermal logging on cores extracted from two wells were the basis for our research. Eight different regression models were developed and tested to predict vertical variations of rock conductivity from well-logging data. Additionally, rock thermal conductivity was determined based on Lichtenecker-Asaad model. Comparison study of regression-based and theoretical-based approaches was performed. Among considered machine learning techniques Random Forest algorithm was found to be the most accurate at well-log based determination of rock thermal conductivity. From a comparison of the thermal conductivity - depth profile predicted from well-logging data with the experimental data, and it can be concluded that thermal conductivity can be determined with a total relative error of 12.54 per cent. The obtained results prove that rock thermal conductivity can be inferred from well-logging data for wells that are drilled in a similar geological setting based on the Random Forest algorithm with an accuracy sufficient for industrial needs.
- Downhole methods
- Heat flow
- Heat generation and transport
- Neural networks
- Sedimentary basin processes