American Oil and Gas Reporter - January 2017 - 101

Tech Trends

Technology Reveals Eagle Ford Insights
By Patricia Santogrossi
HOUSTON-Spectral decomposition
is a useful tool for below-resolution seismic interpretation, reservoir thickness interpretation, and depositional structure
enhancement. However, seismic components that use normalized instantaneous
attributes help quantify thickness variability more reliably. Phase components
detect lateral discontinuities, both stratigraphic and structural, and also contribute
to segregating various facies tracts.
However, simultaneous analysis of
multiple seismic attributes using machine
learning goes beyond the visualization
of one, two, or even three attributes at a
time. Multi-attribute analysis enables interpreters to extract more information
from the seismic response, even below
seismic resolution.
When combined with machine learning
methods, the technology simultaneously
analyzes multiple attributes to give geoscientists deeper and more comprehensive
insights into seismic data to increase interpretation effectiveness, more accurately
geosteer wells, locate fracture trends and
sweet spots in shale plays, discriminate
direct hydrocarbon indicators in conventional reservoirs, identify changes in facies-related pore pressure, reveal geologic
and stratigraphic features, and visualize

thin beds and facies below seismic resolution, etc.
State-of-the-art multi-attribute, machine-learning software was applied to a
large 3-D seismic volume from two counties in Texas focused on the eastern end
of the Eagle Ford Shale trend and its
bounding formations: the underlying Buda
and overlying Austin Chalk. The results
characterize remarkable resolution of
stratigraphic and structural details in all
three formations. By taking full advantage
of multiple attributes simultaneously, the
technology successfully imaged features
below seismic resolution. The full range
of the findings enabled visualization and
characterization of explicit stratigraphic
and structural details.
The multi-attribute classification results
provided an effective basis for more accurate and detailed mapping and calibrations of the basal clay-rich shale (BCS),
the Eagle Ford Shale interval, and the
Upper Eagle Ford Marl using unique
neuron classification and unconformity
detection.
While the study was conducted in the
Eagle Ford, it is important to note that
the same technology and principles are
applicable to both conventional and unconventional geologic settings. However,

FIGURE 1A
Conventional Amplitude
Seismic Display across
Well 6 Location

FIGURE 1B
Self-Organizing Map of Multiple
Instantaneous Attributes
(Colored 64 Classes of Neurons)

Pilot &
1H

Austin Ch

alk

EF S

h

BU
DA

EF Shale Objective 14 ms (108')
108' EF Formation 24 ms (260')
EF "influence" 42 ms (~350')

the following conditions must be met for
effective application of these methods:
* High quality seismic data, preferably
sampled at two milliseconds;
* Some number of wells with edited
log curves, and carefully computed and
applied time/depth corrections; and
* Adequate core data that corroborate
the results visually.
Instantaneous Attributes
The Eagle Ford analysis began by applying principal component analysis
(PCA) on 16 instantaneous attributes. Instantaneous attributes calculate a value
at each sample and inherently return
higher-frequency information. From PCA,
nine attributes were run in self-organizing
maps (SOMs), and five of the nine types
of instantaneous attributes were found to
be most common in the Eagle Ford results:
* Instantaneous phase, which is useful
for enhancing continuity/discontinuity;
* Normalized amplitude (cosine of
instantaneous phase), which returns the
energy distinctly from each sample in
the full trace;
* Relative acoustic impedance, which
helps resolve geobodies;
* Envelope or total energy of the
entire reflected waveform; and
* Trace envelope, which was found
to occur only in two encapsulated geobody
facies tracts.
The original PCA and SOMs were
run over a 1.5- to 3.2-second interval
and a specific range of in-lines and crosslines to capture the Eagle Ford's complete
updip-to-downdip occurrence. Results
were viewed first by means of a default
interactive 2-D color map. The Eagle
Ford was resolved, but not uniquely distinguished, until a few colors that were
not specific to the Eagle Ford were
changed in the color map. The results
helped confirm the near uniqueness of
the Eagle Ford facies in the stratigraphy
of the area.
The transparency function of the interactive 2-D color map then was used to
remove all neuron colors except those
JANUARY 2017 101
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