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March 21, 2026Petroleum Science1 citationsOpen Access

Application of 2D full-waveform inversion for characterizing potential gas reservoirs

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KRKai RenSLShaoping LuLLLi Long

Key Points

  • The aim is to improve the identification of potential gas reservoirs using 2D full waveform inversion (FWI) integrated with amplitude variation with angle (AVA) analysis.
  • Applied 2D full waveform inversion on field data from the South China Sea.
  • Conducted FWI processing in the frequency range of 0–9 Hz to update the velocity model.
  • Performed comparative analyses to evaluate model improvements in data fitting and imaging.
  • Integrated FWI results with pre-stack depth migration images and well data for validation.
  • Executed AVA analysis to assess reservoir characteristics.
  • Demonstrated superiority of the updated FWI model over the initial model with improved data fitting.
  • Enhanced pre-stack depth migration imaging with better structural alignment.
  • Identified a distinct high-velocity anomaly, indicating potential gas-bearing sandstone.
  • Provided geological information surpassing that of traditional modeling methods.

Abstract

The application of full waveform inversion (FWI) on field data is currently a widely concerned challenge in seismic exploration. FWI has been proven on synthetic data to have the ability to obtain high-accuracy subsurface velocity and improve the imaging effect of the pre-stack depth migration (PSDM). However, its practical application remains constrained by challenges such as cycle-skipping. Meanwhile, the method’s capability to recover detailed subsurface properties retains considerable, yet underexploited, potential for advanced geological interpretation. In this study, we integrated FWI with amplitude variation with angle (AVA) analysis to identify potential gas reservoirs using 2D OBC data from the South China Sea. Our workflow began with 0–9 Hz FWI processing to obtain an updated velocity model. Comparative analysis demonstrated the FWI model’s superiority over the initial model through: (1) improved data fitting, (2) enhanced PSDM imaging with better structural alignment, and (3) higher-quality ADCIGs. The FWI results revealed a distinct high-velocity anomaly, which was validated through integration with PSDM images and well data. Subsequent AVA analysis suggested this anomaly may represent gas-bearing sandstone, though additional verification would be required to fully characterize the formation. Nonetheless, our workflow offers a reference for subsequent interpretations, which narrows down the scope for targeted objects. Our findings demonstrate that FWI and ADCIGs can obtain more geological information than traditional velocity modeling methods, thereby becoming a valuable tool for geological interpretation.

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Cite This Study

Ren et al. (2026) studied this question.

synapsesocial.com/papers/69be362d6e48c4981c674e2dhttps://doi.org/10.1016/j.petsci.2026.03.034
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