‘Digital twin’ concept underpins
successful digitization strategy
Creating order from data overload is essential for the decision-making pro- cess. Imposing structure to this mas- sive amount of data is critical. This is the role of the digital twin.
The extended low oil price environment has
led to an increased focus on managing and
maintaining assets and improving efficiencies.
In this current economic environment, data
plays a prominent role. Oil and gas companies
are collecting and analyzing more operational
data than ever before in an effort to make better and smarter decisions, but the enormous
volume of data from disparate sources poses
a problem that continues to expand in com-
plexity with the addition of new data streams.
According to a publication by Northeastern
University, the total amount of data in the
world was 4. 4 zettabytes in 2013. To put that
in more digestible terms, one zettabyte equals
44 trillion gigabytes. This volume is expected
to grow to 44 zettabytes by 2020.
While individual companies are not wrangling
with zettabytes of data, they struggle to manage
a significant volume of information and find it
difficult to determine the hidden value of data.
Exploring the concept
Before explaining the value of the digital
twin, it is important to understand the concept.
Newly introduced to the oil and gas industry,
the concept of a digital twin has been around
since 2002. With the introduction of the Inter-
net of Things, the digital twin is now cost-effec-
tive to implement, and the concept is rapidly
becoming imperative to obtain a competitive
advantage in efficient business operations.
Gartner Inc., which has the largest base
of IT research analysts and consultants in
the world, includes the digital twin in its list
of Top 10 Strategic Technology Trends for
2017, listing it among technologies that ana-
lysts believe will have, “substantial disruptive
potential across industries.”
In simple terms, a digital twin is a virtual
model. In the case of oil and gas operations,
it is a model of any production and processing
asset, such as a semisubmersible or drill-
ship. Pairing the virtual and physical worlds
via a digital twin allows analysis of data and
system monitoring in a way that dramatically
improves operations, preventing downtime,
reducing maintenance costs, and providing
data that can be used to streamline operations
throughout the lifecycle of the asset.
The digital twin uses smart sensors to gath-
er and communicate real-time performance
data from an asset to both onsite and remotely
located teams that can leverage extensive data
sets to monitor operations, identify trends,
and more rapidly implement lessons learned
to improve operating efficiency.
A digital twin can be built with varied layers of
complexity – for example, separate models can
be developed for structural, machinery, control
systems, and process systems. A digital twin
also can plug into enterprise systems to gather
business performance data like maintenance
costs and contractor performance data and can
manage these data on the same platform.
Key benefits include the ability to:
• Analyze production rates and identify
• Analyze equipment failure rates and op-
timize maintenance programs
• Conduct root cause analysis of equip-
• Manage structural integrity
• Integrate knowledge management systems
• Optimize new designs based on historical
• Explore hypothetical “what if” scenarios allow-
ing operators to be ready for the unplanned
• Visualize asset risk and key performance
indicators on a single platform.
Analyzing business and equipment performance data holistically allows business decisions like field life extensions to be made on the
basis of accurate data and a clear understanding
of asset integrity.
The decision to extend the life of an existing
asset depends on a number of things, including
the future production rate from the asset, market
price of the product that the asset produces, cost
of modifications, refurbishment, repair, and the
cost of future investment in operating the asset.
A key concern in determining whether to extend
the life of an existing asset is finding a way to
minimize unplanned maintenance and repairs.
These can prove very costly, especially if there
is an associated loss of production, and trying
to get a handle on unplanned interruptions to
production is extremely difficult in the absence
of historical and real-time data.
A data-driven systems approach is an essen-
tial first step in prioritizing future investment
decisions and maximizing the performance
of an existing asset.
The digital twin is a valuable tool in the data-
driven asset management process because it is
a virtual representation of the asset, which in-
cludes multiple components that can be used to
manage the asset throughout its lifecycle. These
components can include drawings and reports,
inspection and sur vey results, numerical models
(such as finite element models), sensor data
and other information particular to the asset.
Currently, DNV GL is developing digital twins
that integrate data such as metocean conditions,
survey results, and sensor data with advanced
numerical structural modeling of the entire unit
for structural integrity management of offshore
assets. What sets this effort apart from others
is the work that has gone into streamlining and
fasttracking modeling capabilities.
The computational engine for the digital twin
is based on the Reduced Basis Finite Element
Analysis (RB-FEA) model developed at the
Massachusetts Institute of Technology and is
commercially available in the Akselos software.
Pairing the virtual and physical worlds via a digital twin
helps improve operations.
(Images courtesy DNV GL)