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<p>The Tarim Basin hosts numerous fault-controlled fracture-cavity reservoirs, which represent a distinctive type of reservoir with substantial hydrocarbon potential. However, due to their deep burial depth, conventional methods often fail to accurately characterize these reservoirs, falling short of the precision required for effective exploration and development. To address this challenge, this study proposes a novel method for identifying the core zones of fracture-cavity reservoirs. The proposed approach begins by verifying the advantages of angle-domain seismic data as a superior data source for delineating fracture-cavity systems. A deep learning model incorporating both spatial and channel attention mechanisms is then employed to extract the contours of the fracture-cavity bodies. Through waveform separation processing, a refined dataset devoid of stratigraphic interference is obtained. Subsequently, the first eigenvalue of the structure tensor is extracted to enhance the energy response associated with the fracture-cavity features. Finally, energy tracking is applied to identify the core zones, resulting in a comprehensive characterization of the core–belt architecture of the fracture-cavity system. Field applications demonstrate that the proposed method significantly outperforms conventional techniques in accurately identifying the core zones of fracture-cavity reservoirs. This approach provides an effective and reliable tool that can be widely applied in other regions with similar geological conditions.</p>

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Research on waveform separation for identifying the core of fracture-cavity reservoirs based on angle-domain seismic data

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Gui Zhipeng,
  • Yin Pengbo,
  • Zhang Junhua,
  • Deng Xingliang,
  • Zhang Yintao,
  • Sun Chong

摘要

The Tarim Basin hosts numerous fault-controlled fracture-cavity reservoirs, which represent a distinctive type of reservoir with substantial hydrocarbon potential. However, due to their deep burial depth, conventional methods often fail to accurately characterize these reservoirs, falling short of the precision required for effective exploration and development. To address this challenge, this study proposes a novel method for identifying the core zones of fracture-cavity reservoirs. The proposed approach begins by verifying the advantages of angle-domain seismic data as a superior data source for delineating fracture-cavity systems. A deep learning model incorporating both spatial and channel attention mechanisms is then employed to extract the contours of the fracture-cavity bodies. Through waveform separation processing, a refined dataset devoid of stratigraphic interference is obtained. Subsequently, the first eigenvalue of the structure tensor is extracted to enhance the energy response associated with the fracture-cavity features. Finally, energy tracking is applied to identify the core zones, resulting in a comprehensive characterization of the core–belt architecture of the fracture-cavity system. Field applications demonstrate that the proposed method significantly outperforms conventional techniques in accurately identifying the core zones of fracture-cavity reservoirs. This approach provides an effective and reliable tool that can be widely applied in other regions with similar geological conditions.