American Oil and Gas Reporter - July 2017 - 74

SpecialReport: Seismic & Geophysics
and wavelet frequencies. Below the quarter-wave length, the wavelets start to
overlap in time and interfere with one another, making it impossible by conventional
means to resolve separate events.
The third step is correlating seismic
reflection data-including compressional
wave energy, shear wave energy and
density-to quantitative rock property and
geomechanical information from geology
and petrophysics. Connecting seismic
data to the variety of very detailed information available at the borehole lowers
risk and provides a clearer picture of the
subsurface between wells, which is fundamentally the purpose of acquiring a 3D survey.
The final step is conducting a broad,
multiscaled analysis that fully integrates
all available data into a single rock volume
encompassing geophysical, geologic and
petrophysical features. Whether an unconventional shale or a conventional carbonate, bringing all the data together in a
unified rock volume resolves issues in
subsurface modeling and enables more
realistic interpretations of geological characteristics.

efficient business only ratchet up at lower
commodity prices. The business challenges
also relate to the personnel side of the
equation, and that should never be dismissed. Companies are trying to bridge
the gap between older geoscientists who
seemingly know everything and the ones
entering the business who have little experience, but benefit through mentoring,
education and training.
One potential solution is using information technology to capture best practices
across a business unit, and then keeping
a scorecard of those practices in a database
that can offer expert recommendations
based on past experience. Keylogger applications can help by tracking how experienced geoscientists use data and tools
in their day-to-day workflows.
However, there is no good substitute
for a seasoned interpreter. Technologies
such as machine learning and pattern
recognition have game-changing possibilities in statistical analysis, but as petroleum geologist Wallace Pratt pointed out
in the 1950s, oil is first found in the human
mind. The role of computing technology
is to augment, not replace, the interpreter's
creativity and intuitive reasoning (i.e., the
"geopsychology" of interpretation).
Machine learning techniques apply
statistics-based algorithms that learn it-

The Role Of Technology

Every company faces pressures to
economize, and the pressures to run an
Well #6

100ms Stratigraphy above Buda

Pilot &
1H
Austin Ch

alk

MSD 2d
EF S

Bud

EF Shale Objective 14ms (108')
108' EF Formation 28ms (260')
EF "inﬂuence" 42ms (~350')

Basal Clay-rich Sh

In the Eagle Ford Shale, multiattribute analysis has matched 24 classes of attributes
within a 150-foot vertical section throughout 200 square miles of a 3-D survey. In this
example, a conventional amplitude seismic display from a northwest-to-southeast seismic section across a well location is contrasted with SOM results using multiple instantaneous attributes. The SOM run used a seismic interval from 10 milliseconds below the
Buda to 100 milliseconds above the Buda, near the top of the Austin Chalk. The SOM results reveal nonlayer cake facies bands that include details in the Eagle Ford's basal
clay-rich shale, multiple high- and low-resistivity Eagle Ford objectives, low-resistivity
ashy beds in the Eagle Ford, and the Upper Eagle Ford Marl.

74 THE AMERICAN OIL & GAS REPORTER

eratively from the data and adapt independently to produce repeatable results.
The goal is to address the big data problem
of interpreting massive volumes of data
while helping the interpreter better understand the interrelated relationships of
different types of attributes contained
within 3-D data. The technology classifies
attributes by breaking data into what
computer scientists call "objects" to accelerate the evaluation of large datasets
and allow the interpreter to reach conclusions much faster.
Some computer scientists believe "deep
learning" concepts can be applied directly
to 3-D prestack seismic data volumes,
with an algorithm figuring out the relations
between seismic amplitude data patterns
and the desired property of interest. While
Amazon, Alphabet and others are successfully using deep learning in marketing
and other functions, those applications
have access to millions of data interactions
a day. Given the significantly fewer number of seismic interpreters in the world,
and the much greater sensitivity of 3-D
data volumes, there may never be sufficient
access to training data to develop deep
learning algorithms for 3-D interpretation.
The concept of "shallow learning"
mitigates this problem. First, 3-D seismic
data volumes are converted to well-established relations that represent waveform
shape, continuity, orientation and response
with offsets and azimuths that have proven
relations ("attributes") to porosity, thickness, brittleness, fractures and/or the presence of hydrocarbons. This greatly simplifies the problem, with the machine
learning algorithms only needing to find
simpler (i.e., shallower) relations between
the attributes and properties of interest.
In resource plays, seismic data interpretations increasingly are based on statistical rather than deterministic predictions. In development projects with hundreds of wells within a 3-D seismic survey
area, operators rely on the interpreter to
identify where to drill and predict how a
well will complete and produce. Given
the many known and unknown variables
that can impact drilling, completion and

American Oil and Gas Reporter - July 2017

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