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GCSpectral
Decomposition for Seismic
Stratigraphic Patterns*
By
Kenny Laughlin1, Paul Garossino2, and Greg Partyka3
Search and Discovery Article #40096 (2003)
*Adapted for online presentation from the Geophysical Corner column in AAPG Explorer May, 2002, entitled “Spectral Decomp Applied to 3-D,” prepared by the authors. Appreciation is expressed to the authors, to R. Randy Ray, Chairman of the AAPG Geophysical Integration Committee, and to Larry Nation, AAPG Communications Director, for their support of this online version.
1Landmark Graphics, Denver; Col.orado
2Upstream Technology Group, BP, Houston, Texas
3Upstream Technology Group, BP, Sunbury, U.K.
While seismic
processors have long used spectral
decomposition, it is only in recent years that it has been applied directly to
aspects of 3-D
seismic
data
interpretation
. The method for doing this was first
published in “The Leading Edge” in 1999, in a paper by Greg Partyka et al.,
that illustrated the idea of using frequency to “tune-in” bed thickness.
Although spectral decomposition is a relatively new technique, some companies
are experiencing great success in many basins around the world. (Most of the
best examples are in clastic environments where depositional stratigraphy is a
key driver.) Companies using spectral decomposition observe significant detail
from these images at great depth – but have found that interpretation
and
integration with well
data
and models are critical to its success.
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Click to view sequence highlighting different parts of reservoir (thicker to thinner).
As shown by the channel system in
Figure 1,
spectral decomposition can extract detailed stratigraphic patterns that
help refine the geologic In other words, higher frequencies image thinner beds, and lower frequencies image thicker beds. This approach is similar to how remote sensing uses sub-bands of frequencies to map interference at the earth’s surface. Just like remote sensing, it is very important to dynamically observe the response of the reservoir to different frequency bands.
The key is to create a set of
Based on well-understood principals, typical
amplitude maps are dominated by the frequency content of What is needed is to see all the different stratigraphic thicknesses in a meaningful way. Spectral decomposition provides this by generating a series of maps or cubes that observe the response of the reservoir to different frequencies. These are then animated allowing the interpreter’s eye to catch subtle changes in the reservoir through motion. There are other good methods that can analyze tuning, but none are as easy to create or as routinely used as the method of animation called the “Tuning Cube.”
To use spectral decomposition, you would
interpret a
If you believe that amplitude is a meaningful
indicator for reservoir presence, then spectral decomposition is a new
step in the
Subtle changes in reservoir thickness or
internal heterogeneities can be observed when comparing these images.
Very quickly you will get a feel for areas with active stratigraphic
variation that need to be evaluated in more detail. Tracking between
these maps and the
In this example, there are actually 30 images
that need to be animated to allow the eye to catch all of the detail
available. Integration with well control is critical to determining the
accuracy of the geologic interpretations. As mentioned, spectral
decomposition is a relatively new technique that already has helped
bring great success in many basins around the world. As such, it is
poised to become an essential tool for the geologic
Partyka, G., J. Gridley, and J. Lopez, 1999, Interpretational applications of spectral decompositiion in reservoir characterization: The Leading Edge, v. 18, p. 353-360 |