American Oil and Gas Reporter - September 2015 - 87

(amplitude volume), average energy, envelope time derivative, density (derived
through prestack inversion), spectral decomposition envelop sub-band at 67.3
hertz, and sweetness.
Figure 4B is the same cross-line 516,
showing the results of classifying the
seven attributes referenced. The red ellipse
shows the pattern in the data that best
represents the actual porosity zone encountered in the well, but could not be
identified readily by conventional attribute
analysis.
Figure 5 is a 3-D view of the cluster
of neurons that best represent porosity.
The ability to isolate specific neurons
enables one to more easily visualize specific stratigraphic events in the data.

Conclusions
Seismic attributes help identify numerous geologic features in conventional
seismic data. Applying principal component analysis can help interpreters identify
seismic attributes that show the most
variance in the data for a given geologic
setting, and help them determine which
attributes to use in a multiattribute analysis
using self-organizing maps. Applying
current computing technology, visuali-

ROCKY
RODEN

Rocky Roden runs his own consulting
company, Rocky Ridge Resources Inc.,
and works with oil companies around
the world on interpretation technical
issues, prospect generation, risk analysis
evaluations, and reserve/resource calculations. He is a senior consulting geophysicist with Houston-based Geophysical Insights, helping develop advanced
geophysical technology for interpretation.
He also is a principal in the Rose and
Associates DHI Risk Analysis Consortium, which is developing a seismic amplitude risk analysis program and worldwide prospect database. Roden also has
worked with Seismic Microtechnology
and Rock Solid Images on integrating
advanced geophysical software applications. He holds a B.S. in oceanographic
technology-geology from Lamar University and a M.S. in geological and
geophysical oceanography from Texas
A&M University.

zation techniques, and understanding of
appropriate parameters for SOM enables
interpreters to take multiple seismic attributes and identify the natural organizational patterns in the data.
Multiple-attribute analyses are beneficial when single attributes are indistinct.
These natural patterns or clusters represent
geologic information embedded in the
data, and can help identify geologic features that often cannot be interpreted by
any other means. Applying SOM to bring
out geologic features and anomalies of
significance may indicate this approach
represents the next generation of advanced
interpretation.
❒
Editor's Note: The authors wish to
thank the staff of Geophysical Insights
for researching and developing the applications used in this article. The seismic
data for the Gulf of Mexico case study
are courtesy of Petroleum Geo-Services.
Thanks to T. Englehart for insight into
the Gulf of Mexico case study. The
authors also would like to acknowledge
Glenn Winters and Dexter Harmon of
Fasken Ranch for the use of the Midland
Merge 3-D seismic survey in the West
Texas case study.

DEBORAH
KING SACREY

Deborah King Sacrey is a geologist/geophysicist with 39 years of oil
and gas exploration experience in the
Texas and Louisiana Gulf Coast, and
Mid-Continent areas. For the past three
years, she has been part of a Geophysical
Insights team working to bring the
power of multiattribute neural analysis
of seismic data to the geoscience public.
Sacrey received a degree in geology
from the University of Oklahoma in
1976, and immediately started working
for Gulf Oil. She started her own company, Auburn Energy, in 1990, and
built her first geophysical workstation
using Kingdom software in 1995. She
specializes in 2-D and 3-D interpretation
for clients in the United States and internationally. Sacrey is a DPA certified
petroleum geologist and DPA certified
petroleum geophysicist.

SEPTEMBER 2015 87

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American Oil and Gas Reporter - September 2015

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