high voltage power lines affecting corrosion growth. In addition,
operational pressure histories let us calculate fatigue effects in
the presence of corrosion or other defects, including how this
changes over time.
Pipeline integrity should not be treated as an island within
an organisation, becoming disconnected from the rest of the
business. The fitness for service, reliability and risk levels of a
pipeline are critical in optimising and managing operational
performance and risk. This perspective illustrates how valuable
inline inspection data can be to a pipeline operator. Control
rooms and asset performance teams must provide on-time,
accurate and consistent information to other departments
within the organisation to set the stage for optimal and
time-critical decisions that impact safety and operational
performance. For example, asset condition
affects potential throughput and security
of supply. In order to ensure that this is
best managed, it must be integrated with
state-of-the-art network optimisation and
planning tools, such as DNV GL’s Synergi
Gas or Synergi Pipeline Simulator. Close
links must also be made to control room
applications, for example leak detection
solutions, such as that provided by Synergi
Pipeline Simulator. In this application we
view our probability of failure of a pipe
segment, as calculated in our integrity
analysis, as the probability of a leak in
that pipe segment before taking account
of any live operational SCADA data. Of
course time factors and failure probability
over time due to condition degradation
must be taken into account. Pigging data
can therefore be used to tune sensitivity
in leak detection relating to corrosion or
stress corrosion cracking factors. Other
data sources, for example call-before-you-
dig or tracking company field activities
from a work management system, could
provide the same input for potential
third party incidents. Combining these
has the potential to greatly improve leak
detection performance and as part of
an emergency response system can be
critical in determining effective responses
and follow-up actions in the case of
incidents.
Overlooked information
sources
One of the largely overlooked and
certainly underutilised information sources
is that described in big data terminology
as unstructured data. This is data that
is not stored in the typical relational or
spatial databases, such as field reports,
email communications or reports with
expert opinions and exchanges including
understanding from domain and
pipeline-specific experts. Think also of
those companies that use internal social media for business
purposes. On platforms such as Yammer, junior and senior
engineers alike exchange valuable information daily, on
operational and more general professional subjects. How can
we make any possible use of this? Well, think of Google, and
how they crawl through websites and pick up keywords and
expressions, making the empirical correlation with search terms
to return content of potential interest, including a priority rating.
Think of the power this could bring to help understand the
cause of specific conditions and possible problems with your
pipeline. Now combine this with input from more conventional
sources, such as the results of the advanced analytics described
earlier and you begin to see the future of pipeline integrity
analysis.
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Made in U.S.A.
Advanced model
The Measure of Quality
DeFelsko Corporation
• Ogdensburg, New York
+1 (315) 393-4450 •
1-800-448-3835
Simple.
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