Estimation of sub‐tuned reservoir thickness from amplitudes at different seismic bandwidths — a time‐domain approach

Khare, Vijay; Martinez, Alex

doi:10.1190/1.3063962

Cited by 2 publications

(1 citation statement)

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“…Liu and Marfurt (2006) show that the peak frequency can give a reasonably good estimate of thickness if the bed thickness is less than the tuning thickness when given adequate well control. Khare and Martinez (2008) demonstrate that amplitude ratios of frequencies can be used as an indicator of thickness variations. Oyem and Castagna (2013) test the accuracy of short time window Fourier transform and constrain least-squares spectral analysis (CLSSA) spectra in determining the time thicknesses of thin beds.…”

Section: Introductionmentioning

confidence: 91%

The thickness imaging of channels using multiple-frequency components analysis

Yan

Zhang

Niu³

et al. 2019

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Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green (RGB) color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components (formations with the largest thickness value) with a red color, medium-frequency components (formations with a medium thickness value) with a green color, and high-frequency components (formations with the smallest thickness value) with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same result of RGB blending may come from different sets of three frequency components. As a result, the same blended color may correspond to several different time thicknesses. It is also very difficult to interpret the corresponding thickness of the blended colors such as white and yellow. To avoid the ambiguity of time-thickness estimation using RGB blending, we have estimated the time thickness of the thin beds using all of the frequency components in a user-defined frequency band instead of only three frequency components. Our workflow begins with the normal seismic spectral decomposition. Considering that the different reflectivity pairs with a different time thickness have a different amplitude spectrum, we then use the self-organizing map to cluster the decomposed amplitude spectra of seismic traces. We finally assign each cluster with a relative thickness by comparing the clustered results with well logs.

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Section: Introductionmentioning

confidence: 91%