Kazakhstan journal for oil & gas industryKazakhstan journal for oil & gas industry2707-42262957-806XKMG Engineering10872010.54859/kjogi108720Research ArticleClustering of lithotypes based on visual features of cores using convolutional neural networks and K-MeansAbdimanapGalymzhan S.g.abdimanap@kmge.kzhttps://orcid.org/0000-0003-1676-4075BostanbekovKairat A.<p>PhD</p>k.bostanbekov@kmge.kzhttps://orcid.org/0000-0003-2869-772XAlimovaAnel N.<p>PhD</p>a.alimova@kmge.kzhttps://orcid.org/0000-0002-5155-2417SalievNurlan B.saliyevnurlan@gmail.comhttps://orcid.org/0009-0001-6537-6960NurseitovDaniyar B.<p>Cand. Sc. (Physics and Mathematics),&nbsp;professor (associate)</p>d.nurseitov@kmge.kzhttps://orcid.org/0000-0003-1073-4254KMG EngineeringSatbayev University120720246225380502202412062024Copyright © 2024, Abdimanap G.S., Bostanbekov K.A., Alimova A.N., Saliev N.B., Nurseitov D.B.2024<p><strong>Background:</strong> Lithology is a vital field of study in both geology and the oil and gas sector that focuses on the properties of geological rocks. The primary objectives of lithology to classify rocks, determine their origin, and investigate the conditions of their formation and changes over time. Lithological core examination employ various methods, encompassing both conventional techniques (e.g., visual inspection of the rock samples or microscopic analysis of slides) and modern technologies. Conventional methods of examination require high qualifications and experience, and can be labour-intensive, especially in visual analysis (description of core material). The application of machine learning methods and automated technologies can enhance the efficiency and accuracy of analysis, save time, and provide quick access to information.</p> <p><strong>Aim: </strong>To develop lithotypes clustering model on core images using machine learning methods.</p> <p><strong>Materials and methods:</strong> The paper discusses an algorithm for clustering lithotypes using K-Means method combined with VGG16, VGG19 and ResNet50 convolutional neural networks to identify key features (similarities and distinctions as determined from photos).</p> <p><strong>Results: </strong>The algorithm for clustering lithotypes using K-Means method and convolutional neural networks is developed. The advantages and limitations of the algorithm when working with core images are determined. Results from experiments conducted using an actual dataset are presented.</p> <p><strong>Conclusion:</strong> The findings of the study offer important practical insights that can be applied to deep learning methods for core analysis as well as geological research. The application of this approach in geology can be broadened and the analysis of alternative machine learning models and techniques can be strengthened with more investigation.</p>lithologycore analysisclustering lithologymachine learningconvolutional neural networksлитологиякернді зерттеулитологияны кластерлеумашиналық оқытутүйіндемелі нейрондық желілерлитологияисследование кернакластеризация литологиимашинное обучениесвёрточные нейронные сети1.Gandhi SM, Sarkar BC. Essentials of mineral exploration and evaluation. Elsevier; 2016. 410 p.2.Corina AN, Hovda S. Automatic lithology prediction from well logging using kernel density estimation. 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