Abstract
This study focuses on the characterization of the subsurface faults and fractures that influence hydrocarbon drilling and production in El Feel field, Murzuq Basin, Libya. The Poor quality of the seismic data posed a significant challenge to the traditional manual fault detection methods. Two signal processes (AGC and Median filter) were used to enhance the clarity of seismic data. Seismic attributes such as variance, curvature, and ant tracking were used to facilitate faults detection. A GIS-based statistical method was used to interpret and evaluate variances in trends and density of extracted faults in the study area. SRTM (30-m) shaded relief maps were used to investigate the spatial correlation and direction similarity between surface and subsurface structures in the study area. The results of the seismic attribute analysis indicate a moderate degree of faulting and fracturing, with five primary fault patterns. The density maps showed four high-density regions distributed over the entire study area. The approach implemented in this study can improve well placement decision-making, optimize the drilling process and contribute to a more comprehensive understanding of subsurface geological features.