because of the multi-billion dollar investment and drilling decisions that are
being made by the energy companies
regarding where to drill, where to frack
and how to frack. It calls for combining
disparate computational and scientific
disciplines to be able to interpret different types of data together. For example,
to algorithmically interpret images (such
as well logs), machine learning needs to
be combined with pattern recognition,
computer vision and image processing.
Mixing these different disciplines provides more holistic recommendations regarding where and how to drill and frack,
while reducing the chances of problems
that could emerge along the way.
For example, by developing detailed
analytical signatures – using data from
production, subsurface, completion and
other sources – one can better predict
performing and non-performing wells in
a field. This process is supported by the
prescriptive analytics technology’s ability to automatically digitize and interpret
well logs to create depositional maps
of the subsurface. With a better idea of
where to drill, companies save invaluable resources by skipping wells that
shouldn’t be drilled in the first place. At
the same time, they minimize damage
to that particular landscape.
Prescriptive analytics can be used in
other areas of oil and gas production.
A NA L Y T I C S
In both traditional and unconventional
wells, by using data from pumps, production, completion and subsurface
characteristics, one can predict failures
of electric submersible pumps and prescribe actions to mitigate production
loss. Apache Corp., for example, is using analytics to predict failures in pumps
that pull oil out from subsurface and
preempt the associated production loss
from these pump failures.
Another potential application of prescriptive analytics is that it can possibly
predict corrosion development or cracks
in pipelines and prescribe preventive
and preemptive actions by analyzing
video data from cameras along with
other data from robotic devices called
“smart pigs” inside these pipelines.
Smarter decisions equal fewer resources, lower environmental impact
and greater yields. Successful companies will be the ones that know how to
prioritize resources to extract, produce
and transport oil and gas in the most efficient and safest manner. Look for big
data and prescriptive analytics to play a
much bigger role in this space over the
coming years.
Atanu Basu is CEO of AYATA, a software
company headquartered in Austin, Texas. AYATA’s
prescriptive analytics software focuses on
improving oil and gas exploration and production.
Basu is a member of INFORMS. A version of this
article appeared in DataInformed.
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