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<p>Xihu depression in the East China Sea is characterized by a high proportion of low-permeability reserves in its deep low-permeability reservoirs. Hence, this region holds significant potential for oil and gas resources and is crucial for the future strategic development of oil and gas resources in China. However, complex geological conditions, ambiguous seismic data imaging, and challenging reservoir characterization make it challenging to apply conventional seismic exploration techniques in this region. In this study, we succinctly describe the effective application of seismic technology and its outcomes for deep, low-permeability reservoirs in the Xihu depression of the East China Sea, focusing on three key aspects: seismic data optimization, reservoir prediction, and sweet spot prediction. We applied the seismic-geologic target-oriented fidelity and amplitude preservation technique for the purpose. This technique applies structure-oriented progressive residual multi-wave suppression, artificial intelligence-driven multiscale fault identification and enhancement, and differentiation based on multi-signal-to-noise ratio components for extending frequency while preserving the amplitude in the image-gathering domain. Thus, it obtains information on seismic data and enhances the imaging accuracy of deep, complex faults and diverse sand bodies. We also used the multidimensional time–frequency reservoir prediction technique employing seismic advantage information. The technique uses restack amplitude-<i>versus</i>-offset (AVO)-preserved time–frequency seismic data for coal weakening and sand strengthening, bidirectional spectral extension for the staged characterization of overlapping channels, and phase-controlled frequency-divided and iterative prestack inversion for the prediction of thick reservoirs. This approach substantially improved the accuracy of reservoir prediction in tidal-river-controlled and river-lake interactive channel sand bodies. We performed sweet spot prediction using favorable lithofacies optimization under low-permeability conditions. For this purpose, we adopted petrophysical experiment-driven favorable-lithofacies-sensitive parameter optimization and physical property prediction using the density factor, based on the separation of coupled P-and S-waves and direct seismic prediction of oil and gas <i>via</i> solid-liquid phase stripping. This effectively enhanced the prediction accuracy of high-porosity and high-gas-saturation sweet spots in low-permeability reservoirs. The application of advanced seismic description technologies for deep, low-permeability reservoirs was highly effective, leading to a significant breakthrough in tapping the deep oil and gas reserves in the East China Sea.</p>

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Seismic Description Technology for Deep, Low-Permeability Reservoirs in the Xihu Depression in the East China Sea

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Jian Li,
  • Dewen Qin,
  • Yan Zhang,
  • Wei Hu,
  • Weizhe Yu,
  • Qin Li,
  • Lide Peng

摘要

Xihu depression in the East China Sea is characterized by a high proportion of low-permeability reserves in its deep low-permeability reservoirs. Hence, this region holds significant potential for oil and gas resources and is crucial for the future strategic development of oil and gas resources in China. However, complex geological conditions, ambiguous seismic data imaging, and challenging reservoir characterization make it challenging to apply conventional seismic exploration techniques in this region. In this study, we succinctly describe the effective application of seismic technology and its outcomes for deep, low-permeability reservoirs in the Xihu depression of the East China Sea, focusing on three key aspects: seismic data optimization, reservoir prediction, and sweet spot prediction. We applied the seismic-geologic target-oriented fidelity and amplitude preservation technique for the purpose. This technique applies structure-oriented progressive residual multi-wave suppression, artificial intelligence-driven multiscale fault identification and enhancement, and differentiation based on multi-signal-to-noise ratio components for extending frequency while preserving the amplitude in the image-gathering domain. Thus, it obtains information on seismic data and enhances the imaging accuracy of deep, complex faults and diverse sand bodies. We also used the multidimensional time–frequency reservoir prediction technique employing seismic advantage information. The technique uses restack amplitude-versus-offset (AVO)-preserved time–frequency seismic data for coal weakening and sand strengthening, bidirectional spectral extension for the staged characterization of overlapping channels, and phase-controlled frequency-divided and iterative prestack inversion for the prediction of thick reservoirs. This approach substantially improved the accuracy of reservoir prediction in tidal-river-controlled and river-lake interactive channel sand bodies. We performed sweet spot prediction using favorable lithofacies optimization under low-permeability conditions. For this purpose, we adopted petrophysical experiment-driven favorable-lithofacies-sensitive parameter optimization and physical property prediction using the density factor, based on the separation of coupled P-and S-waves and direct seismic prediction of oil and gas via solid-liquid phase stripping. This effectively enhanced the prediction accuracy of high-porosity and high-gas-saturation sweet spots in low-permeability reservoirs. The application of advanced seismic description technologies for deep, low-permeability reservoirs was highly effective, leading to a significant breakthrough in tapping the deep oil and gas reserves in the East China Sea.