Prediction of petrophysical properties from seismic quality factor measurements

McCann, C.; Sothcott, J.; Assefa, S.

doi:10.1144/gsl.sp.1997.122.01.09

Cited by 5 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimated Q function is shown in Figure 5, in which the Q function is plotted against the depth and the two-way traveltime. It is interesting to see that the Q function falls considerably when depth increases, typically, at the depth range 1700-1790 m. This Q anomaly can be used in conjunction with other petrophysical attributes as a lithological indicator for seismic interpretion (e.g., McCann et al, 1997).…”

Section: Robust Q Estimationmentioning

confidence: 97%

Quantifying the effectiveness of stabilized inverseQfiltering

Wang¹

2003

GEOPHYSICS

View full text Add to dashboard Cite

Applying inverse Q filtering to surface seismic data may minimize the effect of dispersion and attenuation and hence improve the seismic resolution. In this case study, a stabilized inverse Q filter is applied to a land seismic data set, for which the prerequisite reliable earth Q function is estimated from the vertical seismic profile (VSP) downgoing wavefield. The paper focuses on the robust estimate of Q values from VSP data and on the quantitative evaluation of the effectiveness of the stabilized inverse Q filtering approach. The quantitative evaluation shows that inverse Q filtering may flatten the amplitude spectrum, strengthen the time-variant amplitude, increase the spectral bandwidth, and improve the signalto-noise (S/N) ratio. A parameter measuring the resolution enhancement is defined as a function of the changes in the bandwidth and the S/N ratio. The stabilized inverse Q filtering algorithm, which may provide a stable solution for compensating the high-frequency wave components lost through attenuation, has positive changes in both the bandwidth and the S/N ratio, and thereby enhances the resolution of the final processed seismic data.

show abstract

Section: Robust Q Estimationmentioning

confidence: 97%

Quantifying the effectiveness of stabilized inverseQfiltering

Wang¹

2003

GEOPHYSICS

View full text Add to dashboard Cite

show abstract

“…If the goal is to predict velocity and attenuation spectra in rocks as a function of their petrophysical parameters (or visa versa), as is may be for an application within seismic exploration or reservoir monitoring (e.g., Samec and Blangy, 1992;McCann et al, 1997;Koesoemadinata and McMechan, 2001), one should probably focus on inclusion-based models. Hudson et al (1996), Pointer et al (2000 and Tod (2001Tod ( , 2002 have shown that progress along the inclusion-based line can be obtained if an extremely simple model of the porous microstructure is employed: namely, that where an anisotropic porous/ permeable medium is containing a dilute concentration of small-aspect ratio spheroidal cracks that are distributed in space in accordance with an isotropic correlation function.…”

Section: Introductionmentioning

confidence: 99%