4.4 Article

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

期刊

JOURNAL OF APPLIED GEOPHYSICS
卷 120, 期 -, 页码 81-95

出版社

ELSEVIER
DOI: 10.1016/j.jappgeo.2015.07.001

关键词

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

资金

  1. National Science & Technology Major Project [2011ZX05019-008]
  2. National Natural Science Foundation of China [U1262208]
  3. Science Foundation of China University of Petroleum-Beijing [KYJJ20150090]
  4. Natural Environment Research Council [bgs05015] Funding Source: researchfish
  5. NERC [bgs05015] Funding Source: UKRI

向作者/读者索取更多资源

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. (C) 2015 Elsevier B.V. All rights reserved.

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