Maintworld 1/2017

ASSET MANAGEMENT
sight into the performance and efficiency
of any operation. What is often forgotten
is the information on the assets themselves.
Asset lifecycle information management
provides structured control of
asset information and managed change
from cradle to grave, ensuring engineers,
maintenance and operations always have
accurate information.
Knowing what and where assets are
located exactly, what condition they are
in, how they are performing and what is
their remaining life is critical to knowing
you have control over your operation
with line of sight. Maintaining a
seamless information stream between
asset data, documents, organizations,
requirements, people, and processes;
asset lifecycle information management
assures information integrity. For example,
BP sees asset information becoming
increasingly important and valuable
within the business. By using cuttingedge
technology solutions BP expects to
provide new understanding of how to derive
value from data. This will help drive
standardisation and project execution
efficiency that will be driven out across
the enterprise rather than each project
working on their own.
Incorporate asset design data with designs to help risk based inspections.
Display all of your operational data and more on a single dashboard and explore deeper.
the most advantageous outcome. With
machine learning, this becomes a reality.
Machine learning involves doing the
tasks engineers perform but with the
ability to make the right decision from
a variety of options. By using historical
condition data from assets (corrosion,
vibration, and so on) as well as current
conditions (for example, temperature,
pressure, turbidity), machine learning
can sort through large data sets and
identify patterns or connections, predict
outcomes based on knowledge, and
make predictions and recommendations
to decision makers on the best course of
action to take.
Asset Lifecycle Information
Management Leads to Agility
and Value
With so many assets producing so much
data around the world, this information
can be used to provide much more in-
The Digital Transformation
Has Already Started
Advances in analytics, generated data,
and hardware can lead to significant
advantages across all areas of the oil and
gas spectrum when it comes to leading
the digital innovation charge, culminating
in the ‘digital oilfield.’ Key to this is
the convergence of operational technology
(OT) and information technology
(IT) for improved decision-making.
While this is a step forward, converging
engineering technology (ET) will provide
more significant improvements to asset
performance. With asset-related information
linked to the digital engineering
model, it facilitates efficient modifications
and renovations. Before and during
design, functional definitions and requirements
define expected asset behaviour.
With model data being used more
often in the oil and gas industry, often
in separate locations, it makes sense to
incorporate them into the whole system
for improved visibility.
Engineering data (models that are in
the form of networks, schematics, catalogues,
3D designs, and so on) are not
a static view, but can be a continuously
evolving living thing, as assets change
over time in terms of functions and repairs.
Engineering modelling data is the
‘digital twin’ of the physical asset. These
digital representations of the physical
asset allow producers to understand,
predict and optimize the performance of
their assets and their business. With real-time
information from the OT laid on
top of the models, they can then navigate
and display all information relating to
that asset or process, only recommending
an engineer for inspections purposes
if necessary, and all done remotely. The
true value of convergence lies in reduced
asset downtime and maintenance costs,
which will only become more accelerated
with the inclusion of machinelearning
technology.
You don’t have to wait for an upturn in
demand and oil prices. Change can only
take place if you embrace the technology
that is already out there and available.
10 maintworld 1/2017