American Oil and Gas Reporter - September 2015 - 81

SpecialReport: Reservoir Characterization

Approach Aids Multiattribute Analysis
By Rocky Roden
and Deborah King Sacrey

HOUSTON-Seismic attributes, which
are any measureable properties of seismic
data, aid interpreters in identifying geologic
features that are not understood clearly
in the original data. However, the enormous amount of information generated
from seismic attributes and the difficulty
in understanding how these attributes-
when combined-define geology, requires
another approach in the interpretation
workflow.
To address these issues, "machine learning" to evaluate seismic attributes has
evolved over the last few years. Machine
learning uses computer algorithms that
learn iteratively from the data and adapt
independently to produce reliable, repeatable results. Applying current computing
technology and visualization techniques,
machine learning addresses two significant
issues in seismic interpretation:
* The big data problem of trying to
interpret dozens, if not hundreds, of volumes of data; and
* The fact that humans cannot understand the relationship of several types
of data all at once.
Principal component analysis (PCA)
and self-organizing maps (SOMs) are
machine learning approaches that when
applied to seismic multiattribute analysis
are producing results that reveal geologic
features not previously identified or easily
interpreted. Applying principal component
analysis can help interpreters identify
seismic attributes that show the most
variance in the data for a given geologic
setting, which helps determine which attributes to use in a multiattribute analysis
using self-organizing maps.
SOM analysis enables interpreters to
identify the natural organizational patterns
in the data from multiple seismic attributes.
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, geobodies, and aspects of geology
that often cannot be interpreted by any
other means. SOM evaluations have
proven to be beneficial in essentially all
geologic settings, including unconventional
resource plays, moderately compacted

onshore regions, and offshore unconsolidated sediments.
This indicates the appropriate seismic
attributes to employ in any SOM evaluation should be based on the interpretation
problem to be solved and the associated
geologic setting. Applying PCA and SOM
can not only identify geologic patterns
not seen previously in the seismic data,
it also can increase or decrease confidence
in features already interpreted. In other
words, this multiattribute approach provides a methodology to produce a more
accurate risk assessment of a geoscientist's
interpretation, and may represent the next
generation of advanced interpretation.

Seismic Attributes
A seismic attribute can be defined as
any measure of the data that helps to visually enhance or quantify features of
interpretation interest. There are hundreds
of types of attributes, but Table 1 shows
a composite list of seismic attributes and
associated categories routinely employed
in seismic interpretation. Interpreters
wrestle continuously with evaluating the
numerous seismic attribute volumes, including visually co-blending two or three
attributes and even generating attributes

from other attributes in an effort to better
interpret their data.
This is where machine learning approaches such as PCA and SOM can
help interpreters evaluate their data more
efficiently, and help them understand the
relationships between numerous seismic
attributes to produce more accurate results.

Principal Component Analysis
Principal component analysis is a
linear mathematical technique for reducing
a large set of seismic attributes to a small
set that still contains most of the variation
in the large set. In other words, PCA is a
good approach for identifying the combination of the most meaningful seismic
attributes generated from an original volume.
The first principal component accounts
for as much of the variability in the data
as possible, and each succeeding component (orthogonal to each preceding component) accounts for as much of the remaining variability. Given a set of seismic
attributes generated from the same original
volume, PCA can identify the attributes
producing the largest variability in the
data, suggesting these combinations of

TABLE 1
Seismic Attribute Categories
CATEGORY

TYPE

INTERPRETIVE USE

Instantaneous
Attributes

Reflection Strength, Instantaneous
Phase, Instantaneous Frequency,
Quadrature, Instantaneous Q

Lithology Contrasts, Bedding
Continuity, Porosity, Direct
Hydrocarbon Indicators,
Stratigraphy, Thickness

Geometric
Attributes

Semblance and Eigen-Based
Coherency/Similarity, Curvature
(Maximum, Minimum, Most
Positive, Most Negative, Strike, Dip)

Faults, Fractures, Folds,
Anisotropy, Regional Stress Fields

Amplitude
Accentuating
Attributes

RMS Amplitude, Relative Acoustic
Impedance, Sweetness, Average
Energy

Porosity, Stratigraphic and
Lithologic Variations, Direct
Hydrocarbon Indicators

AVO Attributes

Intercept, Gradient,
Intercept/Gradient Derivatives, Fluid
Factor, Lambda-Mu-Rho, Far-Near,
(Far-Near) Far

Pore Fluid, Lithology, Direct
Hydrocarbon Indicators

Seismic Inversion
Attributes

Colored Inversion, Sparse Spike,
Elastic Impedance, Extended
Elastic Impedance, Prestack
Simultaneous Inversion, Stochastic
Inversion

Lithology, Porosity, Fluid Effects

Spectral
Decomposition

Continuous Wavelet Transform,
Matching Pursuit, Exponential
Pursuit

Layer Thicknesses, Stratigraphic
Variations

SEPTEMBER 2015 81
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