Predicting Coal Quality Using Electrical Resistivity and Chemical Techniques for Enhanced Resource Evaluation in Parts of the Northern Anambra Basin, Nigeria
Keywords:
2D Electrical Resistivity Imaging (ERI), Coal seam, Proximate Analysis, Ultimate Analysis, and Sulfur DistributionAbstract
The purpose of the study was to correlate resistivity signatures with coal quality parameters for possible enhanced resource evaluation. The method used vertical electrical sounding (VES) and 2D electrical resistivity imaging (ERI) alongside coal samples, which were collected and subjected to chemical analysis to determine their percentage composition regarding proximate and ultimate analyses. The resistivity results, along with the borehole data, reveal five to nine lithological layers, including coal seams embedded within alternating sandstone and shale beds. Coal seam thickness ranges from 0.5 m to 6.1 m, with the thickest seams and mineable overburden observed in the western part of the study area. The geoelectric results also depict that the overburden thickness varies between 5 m and 140 m across the study area. A strong correlation was established between high resistivity values (>16,000 Ω-m) and high coal quality, characterized by low moisture (2.5-9.5%), low ash (2-16%), high fixed carbon (36-58%), and high calorific values (4400-6800 kcal/kg). The results depict that the lower resistivity zones were associated with lower-grade coal. The integrated results show that the study area possesses a total estimate of coal resources at 23.86 million metric tonnes, with an overall strip ratio of 22.63. The results show that low-sulfur zones (< 0.6) correspond to slightly high resistivity, implying minimal conductive sulfur-bearing compounds, while areas of higher sulfur (0.6-0.8%), higher oxygen content (11.4% - 14.4%), and higher ash content (16% - 34%) align with lower resistivity, highlighting zones requiring environmental monitoring. The study concludes that integrating electrical resistivity and coal sample analysis can serve as a reliable proxy for predicting coal quality and optimizing resource extraction strategies across the study area and the world at large.