Application of Artificial Intelligence Fracture Detection in Hechuan Area

The development of strike slip faults in the central part of the Sichuan Basin is influenced by the structure, and the high yield wells reveal that strike slip faults have a close relationship with reservoir control. This article uses 3D seismic data from the HC125 work area in the Hechuan Tongnan area of the Sichuan Basin to carry out identification of strike slip faults based on artificial intelligence. Firstly, preprocess seismic data to improve the imaging characteristics of strike slip faults in seismic profiles. Secondly, developing method for edge coherence enhancement to highlight the faults boundaries. Finally, U-Net convolutional neural network machine learning method is used to identify main faults, and disorder detection technology is used to identify associated fractures and small-scale faults. According to this, a comprehensive detection technology suitable for strike-slip faults in the central Sichuan region will be formed. Compared with conventional fracture detection technology, artificial intelligence technology for fracture detection has a relatively high fault resolution, and the continuity and interpretability of deep fracture have been greatly improved; Results of multi-scale Fault Detection guarantees the research on the Mechanism of Controlling hydrocarbon accumulation through Strike-slip Faults in the Central Sichuan Basin. This technology effectively improves the interpretation accuracy and classification accuracy of strike slip faults in the central Sichuan region.