The problem of rocks thermal conductivity determination from well-logging data is highly important for many branches of petroleum geology and geophysics (basin modelling, thermal EOR modelling, thermal logging interpretation, etc.). 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. Among considered machine-learning techniques, the 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 it can be concluded that thermal conductivity can be determined with a total relative uncertainty of 11.5%. The available data allows concluding that machine-learning algorithms are a promising framework for accurate well-log based predictions of rock thermal conductivity.