Fracture Modeling of a Carbonate Reservoir: A Case Study of the East Urikhtau Field

Background: Fracture Modeling of carbonate reservoirs plays a key role in predicting well productivity and enhancing field development efficiency. The East Urikhtau field, located in the eastern flank zone of the Pre-Caspian Depression, features a complex tectonic structure with an extensive system of faults and fractures. These geological features significantly impact the filtration and storage properties of carbonate reservoirs, making advanced geological modeling techniques necessary. A comprehensive fracture model allows a more precise evaluation of structural heterogeneities and their effect on hydrocarbon migration and accumulation. Aim: A three-dimensional fracture model of a carbonate reservoir was developed to identify highly fractured zones and evaluate their correlation with well productivity. This model is essential for improving the accuracy of reservoir filtration-capacity property predictions and designing effective strategies for the field’s further development. Materials and methods: Modern geological modeling techniques were applied in this study, including FMI data interpretation, core analysis, seismic attributes, and Discrete Fracture Network (DFN) modeling. Initial geological and geophysical data were processed using Petrel software, utilizing Ant Tracking and Distance to Object methods to determine fracture orientations and intensities. The developed trend model served as the foundation for discrete fracture modeling, enabling the quantitative assessment of fracture intensity and the identification of the most promising zones for further development. Results: The results of this study demonstrate that the developed fracture model facilitated the detailed identification of highly fractured zones and established their correlation with well productivity. It was found that the most intensely fractured zones are located near faults, as confirmed by fluid flow rate analysis. The application of Ant Tracking and DFN methods reduced uncertainties in the inter-well space and improved predictions of the reservoir’s filtration-capacity properties. Conclusion: The developed methodology allows for a more detailed characterization of the geological structure, enhances the accuracy of well productivity forecasting, and optimizes development planning. The obtained data can be used for designing new wells and adjusting field development strategies for reservoirs with dual porosity and permeability.