The application of quantitative gas saturation estimation based on the seismic wave dispersion inversion

Shuangquan Chen*, Mark Chapman, Xiaoyang Wu, Xiang Yang Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Trace volumes of pore gas result in a drastic reduction of P-wave velocity, making it hard to determine the degree of gas saturation in a reservoir from P-wave velocity or related seismic attributes. An analysis of the seismic data suggests that the presence of hydrocarbons in reservoir units results in higher relative degrees of seismic wave dispersion and attenuation. These effects are generally ignored during a conventional seismic data analysis and inversion. In this paper, we applied a crossplot inversion method to estimate gas saturation. Based on the modeling results, a useful method for the quantitative determination of gas saturation is established based on the poststack seismic dataset. Gas saturation is a frequency-dependent attribute, which allows a crossplot inversion by way of a time-frequency decomposition of the seismic data. The modeling of reflections from the interface between a medium that disperses seismic waves and its elastic overburden indicates that the reflection coefficient is frequency-dependent and varies with gas saturation. This relationship may in turn vary with the numerical model used to describe the reservoir medium (e.g., sand) and with the depositional environment, which affect the amplitude-versus-offset behavior at the interface. Applying this method to field seismic data shows frequency-dependent anomalies similar to those predicted by the model. The gas saturation predicted by these methods has also been verified by well data.

Original languageEnglish
Pages (from-to)81-95
Number of pages15
JournalJournal of Applied Geophysics
Publication statusPublished - 1 Sept 2015

Keywords / Materials (for Non-textual outputs)

  • Attenuation
  • Dispersion
  • Frequency-dependent
  • Gas saturation
  • Quantitative estimation


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