American Oil and Gas Reporter - July 2017 - 77

SpecialReport: Seismic & Geophysics
incorporate information from different
disciplines and allow all of it to be investigated within the same multidimensional
space.
That leads to the second concept:
Using machine learning to organize and
evaluate natural clusters of attribute
classes. If an interpreter is analyzing
eight attributes in an eight-dimensional
space, the attributes can be grouped into
natural clusters that populate that space.
The third component is delivering the
information found in the clusters in highdimensionality space in a form that quantifies the relative contribution of the attributes to the class of data, such as
simple geobodies displayed with a 2-D
color index map.
This approach allows multiple attributes
to be mapped over large areas to obtain a
much more complete picture of the subsurface, and has demonstrated the ability
to achieve resolution below conventional
seismic tuning thickness. For example,
in an application in the Eagle Ford Shale
in South Texas, multiattribute analysis
was able to match 24 classes of attributes
within a 150-foot vertical section across
200 square miles of a 3-D survey. Using
these results, a stratigraphic diagram of
the seismic facies has been developed
over the entire survey area to improve
geologic predictions between boreholes,
and ultimately, correlate seismic facies
with rock properties measured at the
boreholes.
Importantly, the mathematical foundation now exists to demonstrate the relationships of the different attributes and
how they tie with pixel components in
geobody form using machine learning.
Understanding how the attribute data
mathematically relate to one another and
to geological properties gives geoscientists
confidence in the interpretation results.
Leveraging Integration

The term "exploration geophysics" is
becoming almost a misnomer in North
America, given the focus on unconventional
reservoirs, and how seismic methods are
being used in these plays to develop rather
than find reservoirs. With seismic reflection

data being applied across the board in a
variety of ways and at different resolutions
in unconventional development programs,
operators are combining 3-D seismic with
data from other disciplines into a single
integrated subsurface model.
Fully leveraging the new sets of statistical and analytical tools to make better
predictions from integrated multidisciplinary datasets is crucial to reducing
drilling and completion risk and improving
operational decision making. Multidimensional classifiers and attribute selection
lists using principal component analysis
and independent component analysis can
be used with geophysical, geological, engineering, petrophysical and other attributes to create general-purpose multidisciplinary tools of benefit to all oil and
gas company departments and disciplines.
As noted, the integrated models used
in resource plays increasingly are based
on statistics, so any evaluation to develop
the models also needs to be statistical. In

THOMAS A.
SMITH

Thomas A. Smith is president and
chief executive officer of Geophysical
Insights, which he founded in 2008 to
develop machine learning processes for
multiattribute seismic analysis. Smith
founded Seismic Micro-Technology in
1984, focused on personal computerbased seismic interpretation. He began
his career in 1971 as a processing geophysicist at Chevron Geophysical. Smith
is a recipient of the Society of Exploration
Geophysicists' Enterprise Award, Iowa
State University's Distinguished Alumni
Award and University of Houston Distinguished Alumni Award for Natural
Sciences and Mathematics. He holds a
B.S. and an M.S. in geology from Iowa
State, and a Ph.D. in geophysics from
the University of Houston.

the future, a basic part of conducting a
successful analysis will be the ability to
understand statistical data and how the
data can be organized to build more
tightly integrated models. And if oil and
gas companies require more integrated
interpretations, it follows that interpreters
will have to possess more integrated skills
and knowledge. The geoscientist of tomorrow may need to be more of a multidisciplinary professional with the blended
capabilities of a geologist, geophysicist,
engineer and applied statistician.
But whether a geoscientist is exploring,
appraising or developing reservoirs, he
or she only can be as good as the prediction
of the final model. By applying technologies such as machine learning and
multiattribute analysis during the workup,
interpreters can use their creative energies
to extract more knowledge from their
data and make more knowledgeable predictions about undrilled locations.
r

KURT J.
MARFURT

Kurt J. Marfurt is the Frank and
Henrietta Schultz Chair and Professor
of Geophysics in the ConocoPhillips
School of Geology & Geophysics at the
University of Oklahoma. He has devoted
his career to seismic processing, seismic
interpretation and reservoir characterization, including attribute analysis,
multicomponent 3-D, coherence and
spectral decomposition. Marfurt began
his career at Amoco in 1981. After 18
years of service in geophysical research,
he became director of the University of
Houston's Center for Applied Geosciences & Energy. He joined the University of Oklahoma in 2007. Marfurt
holds an M.S. and a Ph.D. in applied
geophysics from Columbia University.
JULY 2017 77

American Oil and Gas Reporter - July 2017

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