Integrated Well Log and Petrographic Analysis for Reservoir Characterization and Hydrocarbon Potential Assessment in the Bornu Basin, Nigeria

Abstract

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 oil
wet 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.