Fracture prediction method and application based on multi-attribute fusion generative adversarial network - Zhang, Yongheng; Wang, Xingjian; Li, Yang; Jiang, Xudong; Zhang, Han (Frontiers in Earth Science, 2025)
In complex structural zones shaped by multi-phase tectonic movements, the coexistence of diverse structural origins and intricate hydrocarbon accumulation conditions makes fracture prediction a critical technical challenge in oil and gas exploration. Current methods face two key limitations: conventional single-attribute seismic analysis falls short of satisfying high-precision fracture detection requirements, while deep learning approaches, despite their progress, suffer from poor generalization due to limited training samples. To address these issues, this study proposes a multi-attribute fusion method that synergistically combines Wasserstein GAN (WGAN) and U-Net++. The proposed approach effectively enlarges the training dataset while maintaining geological fidelity, empowering the trained network to hierarchically extract fracture features across multiple scales. Field tests show our method achieves precise alignment with well-log interpretations and delivers superior performance to conventional attribute-based techniques in both major and micro-fracture identification, demonstrating superior noise resistance and generalizability for fracture prediction across different study areas.