GC
Resolving
Thin
Beds and Geologic Features by Spectral Inversion*
By
Satinder
Chopra1, John P. Castagna2
and Yong Xu1
Search and Discovery Article #40326 (2008)
Posted May 31, 2008
*Adapted from the Geophysical Corner column, prepared by
the authors, in AAPG Explorer, May, 2008, and entitled “When Thin is
In, Enhancement Helps”. Editor of Geophysical Corner is Bob A.
Hardage3. Managing Editor of AAPG Explorer is Vern Stefanic; Larry
Nation is Communications Director.
1Arcis
Corp., Calgary, Canada.
2University
of Houston/Fusion Petroleum Technologies Inc.
3Bureau of Economic Geology,
The University of Texas at Austin ([email protected])
uGeneral statement
uFigure captions
uMethod
uThin-bed reflectivity
u Attribute extraction
uConclusion
uReferences
uGeneral
statement
uFigure captions
uMethod
uThin-bed
reflectivity
u Attribute extraction
uConclusion
uReferences
uGeneral statement
uFigure captions
uMethod
uThin-bed
reflectivity
u Attribute extraction
uConclusion
uReferences
|
Figure Captions
One
post-stack spectral
inversion method that resolves thin layers having a thickness less than
tuning
thickness was described by Portniaguine and Castagna (2005) and then by
Chopra
et al. (2006). This method is driven by geological principles rather
than by
mathematical assumptions and uses spectral decomposition to enhance the
frequency spectrum local to a thin-bed unit.
This
spectral, or thin-bed
reflectivity, inversion outputs a reflectivity series, and the apparent
resolution of the inversion product is superior to the resolution of
the input
seismic data used to generate the reflectivity response. Applications
of this
method in deconvolving complex seismic interference patterns are
changing the
mindset of many seismic interpreters because the technique shows
stratigraphic
patterns with such remarkable detail.
The
method consists of the
following steps:
1.
A set of time-varying and
space-varying wavelets is estimated from the seismic data. For this
purpose, it
is good to have well control data to aid in selecting optimal space and
time
dependencies that should be expressed by these wavelets. In the absence
of well
control, a statistical method of wavelet estimation can be adopted.
2.
The wavelets estimated in
step 1 are removed from the seismic data using an inversion procedure
in which
spectral constraints are derived on the basis of spectral decomposition
procedures. It is important to note that no Earth model or any
assumption about
stratigraphic layering is used in this inversion procedure – the
trace-by-trace
inversion procedure requires no starting geologic model and has no
lateral
continuity constraints.
Figure 1 shows a
comparison of a segment of a seismic section from Alberta, Canada,
before and after reflectivity inversion. After reflectivity inversion,
more reflection detail can be seen, and faults are shown with improved
clarity.
Once thin-bed reflectivity is derived from an input seismic volume –
using, for example, a wavelet derived from an existing well – an
interpreter can determine the amount of uncertainty involved in the
inversion process by using a blind-well test. Our experience with such
exercises suggests that thin-bed spectral inversion creates data that
tie favorably with other wells positioned in the same 3-D seismic
volume.
Figure 2 shows a comparison between a
segment of an input seismic
section (Figure 2a) and an equivalent segment of thin-bed reflectivity
that has been convolved with a bandpass wavelet that extends the high
end of the frequency spectrum to 120 Hz (Figure 2b). Enhanced
resolution of the reflectivity section is indicated by the extra
reflection cycles. More individual reflection cycles can now be
tracked, leading to more detailed interpretation of the data.
Attribute
Extraction
Seismic attributes are a great help
in extracting geologic information and are widely used to map geologic
features at many scales. Geologic information not revealed by
conventional displays of seismic data can often be seen on displays of
one or more attributes derived from the data. As a result, there has
been an explosive growth in the development and application of seismic
attributes. Attribute computation done on data with enhanced resolution
proves to be particularly useful for mapping onlap and offlap patterns
or other stratigraphic features, which facilitates the mapping of
parasequences and the direction of sediment transport.
Figure 3 shows a comparison of a
stratal slice through a
coherence- attribute volume generated for both input seismic data and
for enhanced-resolution data. Notice the significant impact that
enhanced resolution has on the coherence attribute , as evidenced by the
increased lateral resolution of the channel system and by the improved
faulting picture seen in Figure 3b.
The thin-bed spectral inversion
method discussed here is a novel way of removing wavelet effects from
seismic data to create a pure reflectivity sequence. For data with a
high signal-to-noise ratio, units with thicknesses less than the tuning
thickness of the input data can be resolved.
The improved-resolution seismic data retrieved in the form of
reflectivity data are not only important for more accurate geologic
interpretations but prove to be advantageous for:
1) Convolving the extracted reflectivity with a wider bandpass wavelet
(say 5-120 Hz) to provide a high-frequency section.
2) Providing high-frequency attributes that enhance lateral resolution
of geologic features.
Chopra, Satinder, John P. Castagna, O. Portniaguine, 2006,
Seismic
resolution and thin-bed reflectivity inversion: CSEG Recorder, v. 31,
no. 1, p. 19-25.
Portniaguine, O. and John P. Castagna, 2005, Spectral inversion -
lessons from modeling and Boonesville case study: 75th SEG Meeting, p.
1638-1641.
Return
to top.
|