Integrated Well Log and Petrographic Analysis for Reservoir Characterization and Hydrocarbon Potential Assessment in the Bornu Basin, Nigeria (Journal of Pure and Applied Sciences (Science Forum), 2025)
The Bornu Basin, an integral part of the West and Central African Rift System, has been the focus of increasing hydrocarbon exploration efforts. However, limited well data and challenges in direct field investigations have hindered a comprehensive understanding of its petroleum system. This study integrates well log analysis and petrographic examinations to assess reservoir quality, hydrocarbon potential, and depositional environments in the Bima, Gongila, and Fika Formations. Well log data, including Gamma Ray (GR), Resistivity, Density (RHOB), and Neutron (NPHI) logs, were analyzed to delineate lithofacies, identify hydrocarbon-bearing zones, and estimate porosity and fluid saturation. The results indicate that the Gongila Formation exhibits the most promising reservoir characteristics, with clean sand intervals displaying strong resistivity anomalies, suggesting hydrocarbon saturation. The Bima Formation, while containing hydrocarbon-bearing sandstones, demonstrates complex sand-shale intercalations that influence reservoir continuity and quality. The Fika Formation, primarily shale-dominated, shows evidence of secondary hydrocarbon migration with oilwet sand grains observed in specific intervals. Petrographic analysis of well cuttings further supports these findings, revealing the presence of organic matter, oil residues, and oil-coated grains, confirming hydrocarbon migration and entrapment. These observations provide critical insights into the basin’s petroleum potential, with implications for reservoir connectivity, compartmentalization, and trapping mechanisms. The integration of petrographic and petrophysical data reduces uncertainty in subsurface interpretations and enhances the accuracy of reservoir characterization. The study highlights the need for further exploration efforts, including seismic integration and advanced petrophysical modeling, to refine hydrocarbon prospectivity in the Bornu Basin. Additionally, advancements in machine learning and high-resolution logging technologies offer new opportunities for enhancing reservoir property predictions in complex geological settings. The findings from this research provide a valuable framework for future exploration strategies, contributing to a deeper understanding of the Bornu Basin’s hydrocarbon potential and guiding investment decisions in Nigeria’s inland basins.