A dual-branch fracture attribute fusion network based on prior knowledge

The Tarim Basin region harbors abundant carbonate reservoirs in which fractures throughout the structures play a crucial role in facilitating the transportation of oil and gas. The degree and distribution of fracture development play a crucial role in achieving a high yield and stable production. Seismic attribute analysis is a widely used and efficacious approach for examining fractures in reservoirs. However, different seismic attributes characterize fractures at different scales, and any single seismic attribute may be of poor quality, making it difficult to describe reservoir channel media comprehensively. In the present study, the seismic attribute volume was sliced from three perspectives to obtain complete geological spatial structural information of the seismic volume. This was accomplished using a shifted window (Swin) transformer module guided by a t-distribution before extracting and capturing the prior information and global contextual cues of the seismic attributes. Additionally, a dualbranch fusion (DBFusion) network that combined a convolutional neural network and a Swin transformer to extract the spatial geological structural features of seismic attributes was used to facilitate a comprehensive integration of local and global contextual information. Through the DBFusion network modeling, the network comprehensively extracted local information and integrated complementary global information. Taking threedimensional seismic data from a unit in the Tarim Basin as an example, this study explores the fusion of three attributes: coherence, ant tracking, and curvature. When compared to individual attributes, the fused attributes can comprehensively leverage the advantages of all three seismic attributes in reflecting fractures. This results in a more precise representation of fractures of varying sizes in the fused output. It was discovered through comparative experiments that the DBFusion network proposed in this paper exhibits the best performance compared to other models.