GC Resolving Thin Beds and Geologic Features by Spectral Inversion*
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.
. Calgary, Canada
2University of Houston/Fusion Petroleum Technologies Inc.
If the frequency spectrum of a seismic wavelet is centered around 30 Hz, which is usually achievable, and the seismic interval velocity is greater than 3000 m/s, reservoirs having a thickness less than 25 meters may not be resolved. “Not resolved” means there is no distinct reflection peak or trough centered on the top and bottom interfaces of the reservoir unit.
This interval thickness, where seismic data can no longer position a distinct reflection peak or trough at the top and base of the interval, is called “tuning thickness.” Because numerous stratigraphic targets have thicknesses of 10 meters or less – which is thinner than tuning thickness for most seismic profiles – frequency enhancement procedures need to be applied to seismic data to study reservoir targets in this “thinner than tuning thickness” domain.
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
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 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.
Portniaguine, O. and John P. Castagna, 2005, Spectral inversion - lessons from modeling and Boonesville case study: 75th SEG Meeting, p. 1638-1641.
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