Prestack seismic inversion using prestack seismic gather data has become a commonly used technical method for reservoir prediction and fluid detection. In particular, for the exploration and development of unconventional oil and gas reservoirs such as tight reservoirs, shale reservoirs, the prediction and description of geophysical "sweet spot" reservoirs by using the prestack seismic inversion is an important topic and a difficult problem in research. The commonly used prestack seismic inversion method is a linearized approximation based on the exact Zoeppritz equations, and mainly uses the reflected compressional wave data. Considering that the approximate equation has the condition limitation of small incident angle in the prestack seismic inversion, it is not applicable to the far offset data, while the far offset data is conducive to improving the inversion accuracy of the elastic parameters. There is an error between the approximate equations and the exact Zoeppritz equations during forward modelling, and the accumulated error during the inversion process will reduce the inversion accuracy, so the use of the exact Zoeppritz equations can effectively improve the inversion accuracy. In order to improve the suitability of prestack seismic inversion, an inversion method using the exact Zoeppritz analytical expression is proposed, avoiding the introduction of condition constraints which are approximated by the exact Zoeppritz equations and greatly improving the accuracy of prestack seismic inversion. Moreover, the reflected compressional wave data and the converted wave data are used in the prestack seismic inversion. The inversion of reflected compressional wave data and converted wave data is combined to improve the accuracy of the inversion result by increasing the input information in the prestack seismic inversion. Besides, the joint inversion of the reflected compressional wave data and the converted wave data based on the exact Zoeppritz equations can obtain more accurate parameters of compressional wave velocity, shear wave velocity and density, which can better describe the reservoir, detect the fluid, and determine the favorable "sweet spot" area of the oil and gas reservoirs. Furthermore, the Iteratively Regularized Levenberg Marquardt method（IRLM）is used in the inversion calculation process to solve the strongly nonlinear and ill-posed problems of multi-parameter inversion before stacking, and improve the robustness of the IRLM inversion method. Finally, through the application of synthetic data and actual data, the inversion results verify the applicability of the IRLM inversion method, and the method has strong robustness and can be applied to the actual data.

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.