The Triassic Baikouquan Formation in MH1 area developed faults of different scales. The formation mechanism of these fractures is complex, the subsequent stimulation is frequent, and there are various forms, which seriously restrict the development process of horizontal wells. In order to quantitatively describe the fracture development density and trend of MH1 area, establish the small-scale hidden fault identification process and model, and guide horizontal well drilling, this paper is based on improving the signal-to-noise ratio and resolution of seismic data. Various methods are combined, such as optimal azimuth superposition scheme, continuous wavelet transform seismic data frequency boosting, matrix SVD decomposition for denoising, iterative processing of structure-guided filtering, fracture enhancement filtering, multi-attribute fusion and AI intelligent fault recognition. By using this method, the plane distribution mode and spatial distribution pattern of the fracture in MH1 area are clarified. The application of this method effectively solves the complex engineering problems such as horizontal well leakage and pressure channeling jamming in MH1 area.