DI G ITAL TR AN S FOR MATION
MIT-led project incorporates
hard-to-measure data to develop
digital twin for drilling risers
ABS becomes project’s newest member as
consortium begins to build demo twins that will
improve motion prediction, extend asset life
BY STEPHEN WHITFIELD, ASSOCIATE EDITOR
Vortices, or closed circular flows of air or
water, are the primary cause of motion
for marine drilling risers. Understanding
when these vortices may occur, as well
as the potential force of vortex-induced
vibrations (VIVs), is important in predict-
ing potential fatigue damage to the riser.

However, VIVs are still difficult to antici-
pate due to the geometric complexity of the
riser configuration, as well as the sheared
and unsteady nature of ocean currents.

Over the past five years, research-
ers from the Massachusetts Institute of
Technology (MIT) and Brown University
have been working with a consortium of
operators, drilling contractors and ser-
vice companies to create a digital twin
applicable to marine risers, DigiMaR. The
twin was trained using a variety of data
like field sensor data, computational flow
dynamic simulations and existing data-
bases, and has semi-empirical codes that
incorporate various assumptions about the
data to predict VIVs. The result is an accu-
rate reconstruction of riser motion and
prediction of fatigue damage, which can
help to optimize the placement of sensors
on the risers and extend their field life.

“Artificial intelligence has become an
essential tool for the design and opera-
tion of complex systems, and digital
twins provide an unprecedented capa-
bility to explore the parametric space,”
said Michael Triantafyllou, Professor of
Mechanical and Ocean Engineering at
MIT. The digital twin utilizes a parametric
representation of the data characterizing
VIV behavior in a series of databases,
which Dr Triantafyllou said would provide
a comprehensive view of how VIVs affect
riser motion.

The twin takes streaming data from
physical marine risers and their environ-
ment and feeds it into AI predictive mod-
els and neural networks. Once the data is
assessed and interpreted, the twin outputs
information characterizing the magnitude
of parameters like the riser’s displacement,
velocity and acceleration. Those outputs
can then be used to predict the potential
fatigue damage of the structure. Additional
machine learning algorithms within the
digital twin also predict optimal times for
servicing of the riser.

During the 2023 Offshore Technology
Conference (OTC) in Houston on 2 May,
the American Bureau of Shipping (ABS)
announced it had joined the project to
provide third-party classification services
for the design of the digital twin, which
is now being incorporated into the work-
flows of major operators. “As a safety-
focused organization, we’re able to share
the insight we have learned with MIT to
help support the adoption of new tools and
systems that can improve safety and per-
formance,” said Patrick Ryan, ABS Senior
VP and Chief Technology Officer.

The consortium includes operators
Petrobras, ExxonMobil and Shell, as well
as drilling contractor Saipem.

Digital twin design process
Algorithms used in the digital twin
were developed under a three-pronged
approach. First, active learning algorithms
were used to enhance the capabilities of
the codes typically used to build a model
of a marine riser. Digital twins of marine
risers usually rely on the data captured
from the field that measure the motion of
fluids and the forces, such as VIVs, acting
on solid bodies immersed in fluid, also
known as hydrodynamic data. This data
is stored into a database, while a separate
database houses data modeling the geom-
etry of the riser.

Because this method is not comprehen-
sive enough to adequately measure riser
motion, Dr Triantafyllou said, other param-
eters such as the Reynolds number (a ratio
that helps predict fluid flow patterns in
different situations), surface roughness of
the riser and external turbulence were
included into the database.

Additionally, long-term issues that can
affect riser motion, such as biofouling and
the impact of equipment age on structural
integrity, are impossible to measure in
a hydrodynamic database, making long-
term prediction and monitoring even more
challenging. To handle this challenge of incorpo-
rating hard-to-measure data in the digi-
tal twin, the MIT-led team developed an
active learning neural network that uses
algorithms that can interact with a user
to sort unlabeled data. By interacting with
the user, the network learned how to asso-
ciate certain unlabeled data with certain
parameter labels, eventually taking over
the task by itself.

MIT also developed the Intelligent
Towing Tank (ITT), a robot that is capable
of learning about complex fluid-struc-
ture phenomena, to test the validity of
the active learning network. The robot
designed, performed and analyzed experi-
ments, using field data, to explore and map
the complex forces that govern VIVs. An
“explore-and-exploit” methodology also
helped to reduce the number of experi-
ments needed, improving the efficiency of
the active learning network.

The second prong of the algorithm devel-
opment involved creating a neural network
(LSTM-ModNet) that could use sensor
measurements to construct and analyze
the motion of a riser in deepwater. The net-
work allowed the researchers to combine
different types of sensor measurements,
D R I L L I N G C O N T R AC T O R • J U LY/AU G U ST 2023
“Digital twin”
continued on page 29
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