MultiURNet for 3D seismic fault attributes fusion detection combined with PCA

Identifying faults and fractures in complex block reservoirs is critical for recovering residual oil-gas. It has been found that improving the identification accuracy of neural networks and enhancing their ability to learn multiple fault features are key to accurately identifying complex faults, including low-order and hidden faults. In this study, we developed a U-shaped residual network (URNet) that intelligently identifies seismic faults using principal component analysis (PCA). Numerous synthetic seismic records were generated using convolution method. Multiple seismic attributes were generated based on amplitude attributes and preferred attribute bodies. PCA is used to pre-process the features of multi-attribute bodies, downscale and preserve the original data information and reduce data complexity and storage space. A residual block was added to the deep coding and decoding network structure to improve the expression capability of the neural network while preventing gradient vanishing. Naming the network structure as MultiURNet and the network model was applied to the generated data and actual data, and the identification of the high-order and low-order faults were both significantly improved with the clarity of the fault lines. The results confirm the feasibility and efficiency of the proposed method, which has significant potential for future exploration in shallow and medium-shallow oil and gas fields.