Analysis of applying machine learning methods in rock classification problems on core samples

Relevance. Core studies in the oil and gas industry allow us to obtain some filtration and capacity characteristics of rocks, as well as to give an idea of the composition and structure of the subsoil. This information is extremely important in the early stages of field development, as it allows us to formulate a primary version of the development project, which is then refined during the course of field drilling. However, core analysis and description are an extremely labor-intensive and human-influenced works that require automation. Thus, core image research is a popular task in the oil and gas industry that requires high accuracy and care during work; especially considering the volume of images that have to be analyzed. Aim. To review and analyze existing algorithms for classifying rocks from core images based on machine learning methods; as well as to use the information obtained to formulate recommendations for the development of these algorithms. Methods. Machine learning methods, including neural networks. Results. The work analyzed existing approaches to the study of core samples. The main advantages and disadvantages of each of them were noted and, based on the findings, a plan and requirements for conducting further research on core samples using machine learning were developed. As a result, using a convolutional neural network on the U-Net architecture, the authors have trained a model to solve the problem of segmentation of core samples in daytime images and presented the results of the model operation.