Application of Machine Learning for Fatigue Prediction of Flexible Risers – Digital Twin Approach

Event:
Application of Machine Learning for Fatigue Prediction of Flexible Risers – Digital Twin Approach
Date:
November 2020
Author:
Nitin Repalle, Pedro Viana, Elizabeth Tellier, Ricky Thethi

Flexible pipes have a range of potential failure modes, however fatigue damage of the tensile, and eventually,
the pressure armour, is one of the most common problems affecting the longevity of service life and the
OPEX due to the common need for flexible riser replacement. With increasing utilisation of flexible pipe
for current and future field developments, compounded by the recurrent need for field life extension, it is
essential to monitor the riser fatigue regularly to maintain integrity, maximise asset life and to allow for
informed appraisal before extending its operational life.

This paper presents a novel method of using the refined finite element analysis (FEA) in combination
with Artificial Neural Network (ANN) to develop a riser digital twin that can be utilised as an operational
decision-making tool for integrity management and life extension. A digital twin model is trained on a subset
of available metocean and vessel motion data utilising advanced neural networks which can then be utilised
to predict fatigue under the full spectrum of metocean and internal pressure conditions. This approach allows
for a significant reduction in the estimation time of the fatigue damage compared to conventional FEA as
well as improved accuracy of prediction.

The methodology presented in the paper has been primarily developed with the view of deepwater riser
applications but is easily adaptable to shallow water application in combination with various floating vessels.
A case study is presented to demonstrate how this technology is being deployed offshore. A comparison of
FEA and digital twin approach is also presented to highlight the speed and efficiency of digital twin model
whereby real-time insights on fatigue life can be evaluated for informed operational decisions.

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