## 1 Introduction  
Deep water in the passive continental margin basin has been the focus of global oil and gas exploration over the last decade [1; 2; 3]. In Basin A, the marine hydrocarbon source rocks in the drift period are widely distributed and of good quality, with abundant oil sources. The basin mainly develops Cretaceous-Tertiary sandstone reservoirs and some carbonate reservoirs [4]. It is found that the hydrocarbon source rocks near the target reservoir are immature, and the vertical distance between the target layer and the mature hydrocarbon source rocks is more than 1 km due to the tectonic flatness of the period of drifting and the limited development of fracture. Nevertheless, research has discovered the presence of steep faults that, along with their corresponding fracture systems, can serve as vertical channels for the transportation of hydrocarbons [5]. Therefore, studying the distribution characteristics of reservoir structural fractures and the main controlling factors is of great theoretical and practical significance for the rational development of passive continental margin basin. Currently, the mainstream fracture prediction and modelling methods include the binary, fractal and dimensional, seismic attribute analysis, maximum curvature method, and numerical simulation of tectonic stress field [6]. The binary method integrates the fracturing method with the energy method to predict reservoir fractures. It develops a mathematical model to quantitatively predict fractures by analysing the relationship between single-well values, energy values, and fracture densities [7; 8]. However, it does not consider the impact of the stress field on structural fractures, resulting in the ability only to determine the distribution of fracture line density. Fractal theory suggests that fractures formed by rock rupture have fractal characteristics [9]. The fractal dimension can quantitatively characterise the degree of reservoir fracture development. [PERSON] et al. [10] By studying Tarim carbonate reservoir fractures, it is concluded that the degree of reservoir fracture development is positively correlated with the fractal dimension, and the greater the fracture density, the greater the fractal dimension. Still, this method is only used in areas with a high degree of fracture development and is limited by the accuracy of the tectonic interpretation of seismic data. Geophysical methods can indirectly reflect fractures' development through changes in fractured reservoirs' physical characteristics. Fractures can be detected by varying seismic wave propagation time, velocity, amplitude, and frequency at different azimuths in an anisotropic medium. Seismic techniques for fractured reservoir prediction include transverse wave splitting, multi-wave multi-component detection, seismic attribute analysis, etc. [11] [PERSON] et al. [12] summarised the fracture indication parameters and fracture identification methods based on conventional logging. [PERSON] [13] used the coherent body technique to predict the distribution of fracture development zones and fractures in the study area. [PERSON] et al. [14] combined multivariate statistical analysis, seismic attributes, and adaptive fuzzy neural networks to comprehensively predict fractured reservoir distribution concerning reservoir cohomogeneity and anisotropy characteristics. [PERSON] et al. [15] post-stack seismic attribute analysis techniques are utilised to forecast fractures in fractured reservoirs, and a multi-attribute approach has been developed for predicting reservoir fractures. While the 3D seismic data encompass a broad scope of regions, their effectiveness is constrained by the quality and precision of the seismic measurements. Consequently, they can solely identify fracture development zones that are of significant scales and sizes. The fracture prediction method based on the tectonic stress field relies on geomechanical theory [14]. It begins by examining the connection between the tectonic stress field and the mechanical properties of the reservoir rock.