GE has developed a big data concept that they call
‘Unified Operations’. “The concept helps increase uptime
and efficiency for oilfield assets across an enterprise,” says
Ashley Haynes-Gaspar, the general manager for Software and
Services at GE Measurement and Control. “We are working with a
large oil company on a Unified Operations project involving gas
turbines. Gas turbines are very similar in terms of their physics,
but they tend to behave quite differently depending on how they
are operated. The project, which went live recently, will analyse
data from 160 turbines, both GE and non-GE, to optimise their
performance. Eventually, we will extend it to have 5000 pieces of
equipment under management.”
Not sofast
One of the major challenges of implementing new, data-centric
processes has been slow adoption. The digital oilfield (DOF), for
instance, has been around for the last two decades. The original
concept was relatively straightforward; create a suite of gadgets
and software that would allow oil companies to plan, drill,
complete and produce a well using data that was generated as
close as possible to real time. The goal was to reduce operating
costs, add reserves and increase overall efficiency. IHS CERA has
been documenting DOF performance. “We have been tracking
metrics for a while, and we see production increases of 2 - 8%,
operating expense reductions of 5 - 25% and Capex reductions
from 1 - 10%, depending on the project,” says Judson Jacobs,
research director of upstream technology for IHS CERA.
Yet, many companies are reluctant to engage the DOF. One
major reason is the technology does not follow a well-worn path
of innovation. “Traditionally, a service company approaches an
operator with a new technology or process and it is tested out on
20 wells before being widely adopted,” says John Elmer, executive
vice president of Endeavor Management, a Houston-based
consultancy to the oil and gas industry. “DOF has taken a different
approach. You can’t expect a sensor or a communications network
to produce results; organisation change has been the bigger
determinant of success. People have to work together in order to
capitalise on the new knowledge that is streaming in at a much
faster time scale.”
Getting swathes of information from a plethora of smart
gauges back to HQ where it can be crunched is also a barrier to
effective analysis. Unless there is pre-existing internet within
calling range, connectivity can be dicey; bandwidth on cell
networks is limited, and satellite links are expensive.
In addition, outdated corporate IT systems cannot handle
the load big data imposes. “The foundation for opportunities in
machine learning and analytics lie with better data management,
data governance and data quality processes,” says Jim Crompton,
Managing Director at Reflections Data Consulting. “These are the
missing or low-visibility elements in today’s DOF programmes, and
are often the reason for low returns and failed DOF projects.”
Finally, all innovation comes with a price tag. “The cost of a
pilot programme is in the low six figures, and deployment into
the entire infrastructure is in the 7-figure range,” says Bit Stew’s
Castaldini.
Lessons learned
Industry and providers gained valuable experience from
instituting the DOF, and are working to reduce the barriers
associated with data-centric processes. APM-related applications
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