Carbonate reservoir characterization and permeability modeling using Machine Learning - a study from Ras Fanar field, Gulf of Suez, Egypt

Predicting facies and petrophysical properties along and between wells is challenging in carbonate reservoir modeling. In the Nullipore carbonate reservoir, Ras Fanar field, depositional and long-term diagenetic processes result in a high degree of heterogeneity and complex distribution of facies, which in turn affect the reservoir quality. This provides a significant obstacle to building accurate geological models. This study integrates thin sections, routine core analyses, and well logging data to overcome such difficulties and model the Nullipore carbonate facies and permeability. The detailed petrographic analysis revealed the existence of seven microfacies in the reservoir, which are summed up into three facies associations (FAs), each of which represents a specific reservoir rock type (RRT): (1) supratidal FA, (2) intertidal FA, and (3) shallow subtidal FA. The three FAs were correlated with the gamma-ray logs to create facies logs for the studied wells, which were further populated via the Truncated Gaussian Simulation method. Cross-validation was used to evaluate the model's accuracy. The analysis of the available core data infers that the three RRTs are prospective and have a wide permeability distribution. However, RRT3 constitutes the best reservoir quality. The sedimentological analysis revealed that the long-term diagenetic events, involving the dolomitization of limestone and the dissolution of allochems have a major role in improving the pore connectivity and permeability of the reservoir. Fracture characterization discloses that fractures play a significant role in fluid storage and migration. Three Machine Learning (ML) models, including Adaptive boosting (AdaBoost), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were developed to integrate the RRTs, porosity, and permeability to improve permeability prediction. Statistical analysis revealed that the XGB model outperforms other models and exhibits the highest prediction performance. The present study provides further insights into the characterization and modeling of facies and permeability of complex carbonate reservoirs. It can be applied in similar geological settings to better interpretation of depositional and diagenetic controls on reservoir quality assessment and aid in the field development plan.