American Oil and Gas Reporter - September 2015 - 83

SpecialReport: Reservoir Characterization
bility volumes. SOM is a powerful nonlinear cluster analysis and pattern recognition approach that helps interpreters
identify patterns in their data that can
relate to desired geologic characteristics
such as those listed in Table 1.
Seismic data contain huge amounts
of data samples, and are highly continuous,
greatly redundant and significantly noisy.
The tremendous amount of samples from
numerous seismic attributes exhibit significant organizational structure in the
midst of noise. SOM analysis identifies
these natural organizational structures in
the form of clusters. These clusters reveal
significant information about the classification structure of natural groups that
is difficult to view any other way. The
natural groups and patterns in the data
identified by clusters reveal the geology
and aspects of the data that are difficult
to interpret otherwise.

ance there is in its associated eigenvector, and an eigen-vector is the direction
showing the spread in the data. The red
bars in Figure 1A specifically denote the
in-lines that cover the areal extent of the
amplitude feature, and the average of
their eigenvalue results are displayed in
Figures 1B and 1C.
Figure 1B displays the principal components from the selected in-lines over
the anomalous feature with the highest

eigenvalue (first principal component),
indicating the percentage of seismic attributes contributing to this largest variation
in the data. In this first principal component, the top seismic attributes include
trace envelope, envelope modulated phase,
envelope second derivative, sweetness
and average energy, all of which account
for more than 63 percent of the variance
of all the instantaneous attributes in this
PCA evaluation.

Offshore Case Study
A case study is provided by a lease
located in the Gulf of Mexico offshore
Louisiana in 470 feet of water. This shallow field (approximately 3,900 feet) has
two producing wells that were drilled on
the upthrown side of an east-west trending
normal fault and into an amplitude anomaly identified on the available 3-D seismic
data. The normally pressured reservoir is
approximately 100 feet thick, and is
located in a typical "bright spot" setting,
i.e. a Class 3 AVO geologic setting
(Rutherford and Williams, 1989).
The goal of this multiattribute analysis
is to more clearly identify possible direct
hydrocarbon indicator characteristics such
as flat spots (hydrocarbon contacts) and
attenuation effects, and to better understand
the reservoir and provide important approaches for decreasing the risk of future
exploration in the area.
Initially, 18 instantaneous seismic attributes were generated from the 3-D
data in the area. These were put into a
PCA evaluation to determine which produced the largest variation in the data
and the most meaningful attributes for
SOM analysis.
The PCA was computed in a window
20 milliseconds above and 150 milliseconds below the mapped top of the reservoir
over the entire survey, which encompassed
approximately 10 square miles. Each bar
in Figure 1A represents the highest eigenvalue on its associated in-line over the
portion of the survey displayed.
An eigenvalue shows how much variSEPTEMBER 2015 83

American Oil and Gas Reporter - September 2015

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