E.N. Ramazanova1

1 Financial University under the Government of the Russian Federation (Moscow, Russia)

1 enramazanova@fa.ru

Estimation of thermal conductivity of rocks is important in various fields such as geology, geothermal energy, mining and others. Accurate and reliable prediction of rock thermal conductivity requires the use of complex mathematical models and methods. Traditional methods for assessing thermal conductivity, although effective, have certain disadvantages, such as high cost and time-consuming, as well as limited ability to take into account all the factors affecting the final result. The possibility of using machine learning methods to estimate the thermal conductivity of rocks is of interest to researchers because such methods can be more accurate, faster and more efficient in data analysis. Within the framework of this article, the task is to study the possibility and prospects of using machine learning methods in assessing the thermal conductivity of rocks, as well as identifying the advantages and disadvantages of this approach compared to traditional methods.

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