One of the basic units of the geological model of the field is a classification of rocks (lithotypes or petrotypes) determined by logging data. At the same time, in complex formations in case of a wide variety of classes, logging data not always can help to distinguish them by properties. In practice, some classes have fairly close physical properties despite differences in genesis, structure and texture. In this case, it is necessary to form an optimal set of classes for their further prediction and using in a geological modeling. Under this article an attempt of solving this problem for the acid Permian-Triassic igneous rocks of Western Siberia was made using a combination of random forest machine learning algorithm and hierarchical clustering. While hierarchical clustering, the basic matrix of features was built taking into account a volumetric model that was calculated using hybrid technology (simultaneous linear algebraic equations and genetic algorithm). As a result, the integration of technologies made it possible to solve the problem of reducing the class space for its further prediction using logging data. As a confirmation of the reliability of the applied approaches, a comprehensive analysis of the results was carried out. Thus, the results are approved by the petrographic analysis of the core data that shows the similar material composition and similar mechanism of formation of united classes. In the article conclusions about the possibility of using machine learning algorithms to solve similar problems are stated.
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