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GCPhase
Residules Can Reveal Stratigraphic Features*
Oswaldo Davogustto1
Search and Discovery Article #41141 (2013)
Posted June 30, 2013
*Adapted from the Geophysical Corner column, prepared by the author, in AAPG Explorer, April, 2013, and entitled “It’s Just a Phase
(Residue)”.
Editor of Geophysical Corner is Satinder Chopra ([email protected]). Managing Editor of AAPG Explorer is Vern Stefanic
1University of Oklahoma, Norman, Oklahoma ([email protected])
In the March AAPG Explorer Geophysical Corner (Search and Discovery Article #41140) my colleagues Marcilio Matos and AAPG member Kurt Marfurt discussed the concept of phase
unwrapping and the computation of
phase
residues. Here, we go deeper: I elaborate on the application of
phase
residues to seismic data – and the resulting subsequent interpretation of geological features.
Some geologically induced spatial discontinuities, such as channels and faults, easily can be identified as seismic phase
shifts or amplitude anomalies when they are above seismic resolution – but
phase
shifts from condensed sections and erosional unconformities can be subtle and not as easily detected. Spectral decomposition is a proven, powerful means of identifying such discontinuities at specific frequencies that are otherwise buried in the seismic broadband response.
Although seismic acquisition and processing preserve seismic phase
very well, little has been published about interpreting the
phase
components resulting from spectral decomposition. Morlet complex wavelet transform
phase
residues can improve seismic spectral decomposition interpretation by detecting the
phase
discontinuities in the joint time-frequency spectral
phase
component – by evaluating the
phase
shifts that are derived from thickness changes in a wedge model.
We unwrap phase
the
phase
traversing a rectangular contour about each time-frequency sample. In almost all incidents, the contour closes. However, in some cases we have a +180 or -180 degree
phase
anomaly. We display the location of these
phase
residue anomalies and correlate them to stratigraphic discontinuities and inconsistencies in seismic data quality.
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We are able to map interference patterns between the wavelets that occur below seismic resolution. For example, here we apply the
Based on the response of the Figure 1 shows our well-to-seismic calibration for two wells, A and B. Locations of wells A and B are shown in Figure 2c. The correlation coefficient is 75 percent for both wells. We identify very distinctive patterns in the log response for each well – well A is located in the regional Red Fork facies and shows faster P-wave velocity, whereas well B is located in the incised valley system facies and displays lower P-wave velocities from the sonic log.
In Figure 2 we display the seismic data (a), the
In Figure 3 we show a chair diagram of the seismic data with a time slice at 1.8 s (a) and a 3-D view of the geo-cellular grid constructed from the combined well log and In conclusion, we demonstrated how the use of I would like to thank Mark Falk and Al Warner for their support and advice in this project. I also would like to thank Chesapeake Energy Corporation and CGG-Veritas for donating the data for this project, and to Schlumberger and CGGVeritas for facilitating the software used in these displays. |