American Oil and Gas Reporter - November 2015 - 63

SpecialReport: Oil & Gas Computing

Geophysical Insights' Paradise® multiattribute analysis software uses learning machine
technology to extract more information from seismic data than is possible using traditional interpretation tools because it learns the data at full seismic resolution.

generated using the graphics processing
unit and previewed in real time to let interpreters key in on exactly the attribute
of interest. The volume can be generated
immediately," he elaborates. "Instead of
taking two or three days to generate 1215 volumes for review, only one volume
is created and the process completes in
an hour or sooner."
The processing power in this scenario
comes from gaming technology. Highend visualization is cheaper than ever,
commoditized by the gaming industry.
"Thanks to the power of the GPU, processing and visualizing complex subsurface geology is very fast," Oelfke states.
To illustrate the sheer volume of data
that interpreters must contend with, consider the typical number of wells in a
project. "Twenty years ago, 500 wells in
a project was a lot of wells, but 500,000
wells are not uncommon today," says
Oelfke. "The scale of these plays is creating huge volumes of data."
Geosteering is another area benefiting
from emerging web technologies such as
HTML5 (the fifth revision of the hypertext
markup language standard), and the opensource Angular Web application framework, Oelfke points out. "Moving
geosteering to the Web lets operators
steer wells anywhere, anytime, 24 hours
a day, seven days a week," he says. "A
Web-based tool gives geoscientists the
flexibility to get their work done in the
office, at home or on the road. It gives
these folks their lives back."
Internet Of Things
Various technologies are converging
in ways that result in massive quantities
of data being generated in most industries

today, but the oil and gas industry has a
unique challenge with the types of data
being collected as well as the quantity of
data, says Felix Balderas, director of
technology and product development at
Geophysical Insights.
"We need to have the tools to analyze
multivariate data because traditional tools
were not designed for what is happening
with data today," he remarks. "From upstream to downstream, we are seeing an
increased use of data-generating sensors
and other devices."
These devices often are equipped with
flash drives, making them more rugged
and giving them more storage capability,
and faster acquisition and transmission
rates, and they are interconnected, Balderas
points out.
"This and other increased capacities
have produced larger data volumes than
we have seen in the past," he says, adding
that the emerging Internet of Things
(IOT) opens the possibility for tracking
data from all aspects of an operation in
real time. "This could provide valuable
insights, if the proper tools are available
to exploit this information."
In the seismic acquisition sector, massive volumes of data are generated to
create datasets with sizes in terms of terabytes and petabytes, Balderas notes.
"These must be analyzed by interpreters,
but many of the tools interpreters use
were developed when a dataset measured
in gigabytes was considered big," he says.
"Fortunately, desktop workstations are
keeping pace with performance requirements in most cases, but the challenge
continues of how to extract knowledge in
a manner that is efficient and effective,
given the quantity of data now available."

Among the potential solutions are analytical and statistical techniques that
cross-correlate apparently disparate data
types to find previously unseen relationships that can help optimize dataset selections, such as seismic attributes, and
find patterns that reduce the time to
identify strategically important geological
areas of interest.
"Traditionally, interpreters looked for
geological patterns as much visually as
numerically, manually picking points to
identify geological features. This was a
slow and error-prone technique that introduced human bias. The solutions we
are developing are based on learning machine (LM) technology," Balderas says.
"Paradise®, the multiattribute analysis
software that applies LM technology, extracts more information from seismic
data than is possible using traditional interpretation tools because it learns the
data at full seismic resolution. And, unlike
human interpreters, Paradise is not limited
to viewing only two or three attributes at
a time."
What makes LM algorithms different
from imperative programming algorithms
is that LM can learn from the data, rather
than following a set of predefined instructions. Driverless cars, for example,
must be able to recognize any stoplight
encountered on a route. "There is no way
to describe, using instructions, every possible intersection and stoplight configuration," Balderas explains. "Sooner or
later, the car will encounter a stoplight it
has not seen before. With LM algorithms,
the car will recognize a pattern and adjust
what it knows about stoplights for future
reference."
A similar process of pattern recognition
and machine learning techniques can
shorten the time for extracting knowledge
from geophysical data, he contends. "Applied to a volume of geophysical data,
the algorithm looks for patterns that
reveal geological features, which is essentially what interpreters do," notes
Balderas.
He adds that the speed of pattern
recognition is crucial to generating value.
"Learning machines can quickly locate
faults, horizons and other geological features for the interpreter to review,"
Balderas states. "There is no technological
substitute for an experienced interpreter,
but this 'candidate feature' finding approach helps the interpreter focus his
work on areas with the greatest potential."
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NOVEMBER 2015 63

American Oil and Gas Reporter - November 2015

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