Application of post-stack multi-attributes in the description of submerged mountain fault and fracture development

The Baiyun Depression area, located in the northern deep-water region of the South China Sea, is situated adjacent to a hydrocarbon-generating overpressure zone. This region not only exhibits exceptional conditions for hydrocarbon reservoir formation but also has immense potential for exploration. At the same time, the development of deep buried-hill traps has given rise to structural closures, with the fracture zones within these hills serving as conduits for the migration of oil and gas. Consequently, predicting favorable traps associated with these potential closures becomes imperative in the oil and gas exploration endeavors within this region. In response to the challenges posed by tectonic forces and weathering effects, the seismic response characteristics within these hills often present as chaotic reflections, weak reflections, and blank reflections. These characteristics undeniably amplify the complexity of reservoir prediction. To address these challenges, we adopted a comprehensive approach, extensively utilizing poststack seismic data to conduct a thorough study involving multi-attribute analysis. Subsequently, a sophisticated set of multi-scale post-stack seismic buried-hill reservoir prediction schemes and technical workflows are established. We classified fractures in nature into large-scale faults (faults), sub-fractures, and micro-fractures based on the relative size relationship between fault scale and seismic data resolution. Different post-stack seismic attributes are used to describe the classification. Initially, coherent attributes of poststack seismic data are utilized for characterizing sub-faults through manual structure interpretation. Additionally, filter processing is applied, deploying dip enhancement, fault enhancement, and linear enhancement techniques to finely characterize large-scale faults.Subsequently, through a comparative analysis of curvature attribute volumes and maximum likelihood attributes, the maximum likelihood attribute is chosen to characterize small and medium-scale micro-fractures, facilitating the determination of areas rich in fractures. A hierarchical characterization is then meticulously conducted, comprehensively describing micro-fault development zones through multi-attribute superposition. The practical application of this approach is centered on the east buried-hill exploration area of the Baiyun Depression in the deep-water region of the northern South China Sea. By articulating various attributes and integrating multiple attributes of fault bodies of different scales, the precision of delineating the development zone of micro-faults within the buried hill is significantly enhanced. This, in turn, aids in determining the target area of beneficial reservoirs within the buried hill. The application results using real field data affirm that the proposed post-stack multi-attribute fault system description technique effectively characterizes fractured reservoirs and determines high-quality target areas. This approach holds promise for broader application in the exploration and development of other similar buried hill oil and gas reservoirs.