Kazakhstan journal for oil & gas industryKazakhstan journal for oil & gas industry2707-42262957-806XKMG Engineering10880310.54859/kjogi108803Research ArticleExpanding the horizons of core analysis. Panoramic images of thin rock sectionsDoyevaZarema M.Z.Doyeva@kmge.kzhttps://orcid.org/0009-0004-4145-6933JarassovaTolganay S.<p>PhD</p>t.jarassova@kmge.kzhttps://orcid.org/0000-0002-2900-9872SaudabayevRenat K.R.Saudabayev@kmge.kzhttps://orcid.org/0009-0001-7610-1305MerbaevRinat B.R.Merbaev@kmge.kzhttps://orcid.org/0009-0003-3483-330XProninNikita A.N.Pronin@kmge.kzhttps://orcid.org/0009-0008-8686-3523Atyrau branch of KMG Engineering26092025731161262911202424072025Copyright © 2025, Doyeva Z.M., Jarassova T.S., Saudabayev R.K., Merbaev R.B., Pronin N.A.2025<p><strong><em>ABSTRACT</em></strong></p> <p><strong>Background</strong><strong>:</strong> Core analysis is a key method for directly evaluating the properties of promising or existing reservoirs. Core data can be used to determine the sedimentological and diagenetic characteristics of rocks, which are critical for assessing their filtration and storage properties. This article presents the findings of a core digitization project, including the application of advanced technologies for analysing high-resolution panoramic images of thin rock sections.</p> <p><strong>Aim</strong><strong>:</strong> Development and implementation of digital technologies for automated core analysis, including determining porosity and grain size composition from panoramic images of thin rock sections, aim to enhance research accuracy and efficiency over traditional methods, aligning with academic standards.</p> <p><strong>Materials</strong><strong> and methods:</strong> The study describes methods for automated determination of porosity and grain size distribution, as well as their integration with conventional research techniques.</p> <p><strong>Results</strong><strong>: </strong>The results demonstrate a significant improvement in analysis accuracy and efficiency compared to manual methods, as supported by statistical data from 147 thin rock sections.</p> <p><strong>Conclusion</strong><strong>: </strong>The analysis of 147 thin rock sections from eight wells confirmed the effectiveness of digital analysis techniques, which significantly enhanced the accuracy of determining porosity and grain size composition of rocks. The data obtained served as the basis for developing detailed petrophysical models. This is critical for geological and hydrodynamic modelling. Future work includes the further expansion of digital core databases and the implementation of machine learning algorithms to predict reservoir properties.</p>core digitalizationpanoramic imagesgrain size analysisporositypetrophysical modelsкернді цифрландырупанорамалық кескіндергранулометриялық талдаукеуектілікпетрофизикалық модельдерцифровизация кернапанорамные изображениягранулометрический анализпористостьпетрофизические модели1.Ponomareva YA. Digitizing core testing laboratory equipment. Vesti gazovoy nauki. 2021;1(46):125–128. (In Russ).2.Bukharev AY, Budennyy SA, Pachezhertsev AA, et al. Automatic analysis of petrographic thin section images of sandstone. ECMOR XVI – 16th European Conference on the Mathematics of Oil Recovery; 2018 Sept 3–6; Barcelona, Spain. Available from: earthdoc.org/content/papers/10.3997/2214-4609.201802177.3.Liu H, Ren Y-L, Li X, et al. Rock thin-section analysis and identification based on artificial intelligent technique. Petroleum Science. 2022;19(4):1605–1621. doi: 10.1016/j.petsci.2022.03.011.4.Rubo RA, de Carvalho Carneiro C, Michelon MF, Gioria RDS. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images. Journal of Petroleum Science and Engineering. 2019;183:106382. doi: 10.1016/j.petrol.2019.106382.5.Zinkov AV, Makishin VN. Tsifrovizatsiya kerna: uchebnoye posobiye dlya vuzov. Vladivostok: FEFU; 2023. 73 p. (In Russ).6.Idrisova SA, Tugarova MA, Stremichev EV, Belozerov BV. Digital core. Integration of carbonate rocks thin section studies with results of routine core tests. PROneft. Professionally about Oil. 2018;(2):36-41. (In Russ).7.Lazeev AN, Timashev EO, Vakhrusheva IA, et al. Digital Core technology development in Rosneft Oil Company. Oil Industry. 2018;11. doi: 10.24887/0028-2448-2018-11-18-22. (In Russ).8.Zhukovskaya YA, Lokhanova OD. K voprosu o potentsiale tsifrovizatsii petrografii osadochnykh terrigennykh porod. Exolith – 2020. Lithological schools of Russia. Annual meeting (scientific readings) dedicated to the 215-th anniversary of the Moscow Society of Naturalists; 25–26 May, 2020; Moscow, Russia. Available from: geokniga.org/bookfiles/geokniga-2021-02-11-lythology.pdf. (In Russ).