The thickness imaging of channels using multiple-frequency components analysis

Abstract

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.

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

“…In such cases, direct estimation of thickness variations from spectral decomposition and SOM analysis would be possible (Ye et al . ).…”

“…Generally, the presented workflow could be extended if more wells would be available within the seismic study area. In such cases, direct estimation of thickness variations from spectral decomposition and SOM analysis would be possible (Ye et al 2019).…”