Аңдатпа
ABSTRACT Background: 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. Aim: 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. Materials and methods: The study describes methods for automated determination of porosity and grain size distribution, as well as their integration with conventional research techniques. Results: 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. Conclusion: 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.