Fuels & Lubricants Magazine

Technology Corner
PHOTO: Bigstock
Process units and
equipment are being
increasingly instrumented,
so big data
analytics has become
a key part of this drive
for continuous
improvement.
Both tactical and strategic decisions
can be made with confidence and
speed.
With the correct advanced reliability
modeling tool, refiners are able
to use big data analytics to improve
process monitoring, control, and
to optimize operation while helping
to target important problems
such as product and/or quality loss,
energy loss, byproduct generation,
efficiency improvement and safety
problems. With the explosion of
available data, much of this data
relates directly back to equipment
performance and reliability. Refiners
are as well able to make better
CAPEX decisions, to allocate redundant
systems and spares where they
will have the biggest financial impact
and to optimize buffering with the
process design and the logistics.
Royal Dutch Shell as one of the
largest oil and gas companies - one
of the “supermajors” which also
include BP, Chevron, Total and
ExxonMobil - and the world’s fourth
largest company by revenue, for
some time has been developing the
idea of the “data-driven oilfield” in
an attempt to bring down the cost
of drilling for oil. Shell is also known
to widely use big data for monitoring
equipment. Sensors are used for
collecting equipment data to evaluate
its performance and comparing
it to aggregated data. This “big” data
is then used to determine whether
parts need to be replaced and when.
Shell does the same thing with its
exploration equipment which minimizes
the time equipment spends
offline due to breakdowns. Consequentially,
overheads are reduced.
Big data are also used to increase
the efficiency of the transport, distribution
and retail of oil and gas.
However, as in any industry, application
of big data analytics in process
industry has its challenges that are
mostly related to the varying quality
of industrial data. Common factors
affecting the quality of process data
are measurement noise, missing
values, outlying observations, multirate
data, measurement delay, and
drifting data are the common factors
affecting the quality of process data.
The satisfactory performance of
these models can be achieved only
if such challenging issues are addressed.
Therefore, big data analytics can
be summarized into three dimensions
in oil and gas industry: surveying,
forecasting and maintaining
which helps producer understand
the “bigger picture” of the business.
Big data analytics allow for the
close examination and monitoring
of the separate aspects of this bigger
picture. Models can be built and
analyzed to determine how minor
modifications in one area can make a
big impact in another. The more data
an organization has about its business
components the more realistic
a portrait of reality it can create, and
thus, the better-backed decisions it
can make.