Enhancing and evolving the digital twin

By Kjell Eriksson and Francois-Xavier Sireta, DNV GL

The digital twin platform brings all the experts together, providing powerful analysis, insight and diagnostics
The digital twin platform brings all the experts together, providing powerful analysis, insight and diagnostics (illustration: DNV GL)

In the second of our two-part series on trusting and realising value from digital twins, DNV GL’s Kjell Eriksson, VP digital partnering and Francois-Xavier Sireta, Principal Engineer, discuss the acceleration and adaption of virtual replicas to improve performance and mitigate risk…

With fluctuating oil price and the impact of COVID-19 on travel, the delivery of a mirror image of an asset from the safety of shore needs to be trusted, of value, and fit for purpose. The digital twin can be developed for varying purposes.

The probabilistic digital twin (PDT) for instance, is intended to close the gap between digital twins and risk analysis, which is still largely conducted manually before assets enter service. By adding a layer of probabilistic risk modelling to existing digital twins, the concept aims to capture uncertainty, the effect of new knowledge and actual conditions on operational performance and safety. It will therefore allow operators to adjust operations or take preventive actions to maintain an acceptable risk level at all times, thereby reducing expensive downtime.

It delivers more than just predictive maintenance. By including reliability and degradation models to forecast the remaining lifetime of mechanical components, it can also be used to display the overall impact on safety.

DNV GL and FPSO specialist Bluewater are undertaking a pilot project to use hybrid digital twin technology to predict and analyse fatigue in the hull of an FPSO in the North Sea. The project aims to validate and quantify the benefits of creating a virtual replica of the FPSO to optimise the structural safety of the vessel and enhance risk-based inspection (RBI), a decision-making methodology for inspection regimes. Bluewater’s Aoka Mizu FPSO, currently in operation in the Lancaster field, west of Shetland, will be used. To date, the pilot test has shown encouraging results.

‘Aoka Mizu’ while operating at the Lancaster field
‘Aoka Mizu’ while operating at the Lancaster field (photo: Bluewater)

DNV GL’s unique combination of domain experience, inspection capabilities and digital analytics and modelling, enables the monitoring of the asset’s hull structure during operation without dependence on costly routine inspection regimes. Termed “Nerves of Steel”, the underlying concept permits the use of various data sets (external environmental data or local sensor data) combined with digital models of the asset, to develop a hybrid replica model of the vessel’s structure. This can be used in real-time to monitor the asset’s condition, identify and monitor high risk locations, and plan targeted and cost-efficient maintenance and inspection activities.

This is DNV GL’s third pilot project evaluating the performance of hybrid digital twin technology. With global support from the advisor’s experts in Singapore, the UK and Norway, the first involved defining a repair procedure for a FPSO flare tower. Another trial, which is still ongoing, is being performed on a fixed offshore platform.

On announcing the pilot, Peter van Sloten, Department Head Technology Management with Bluewater, says, “We [Bluewater] decided to extend our digital twin programme to include our FPSO Aoka Mizu. Our ambition for the structures largely matched with the novel digitalisation services of DNV GL. We are therefore pleased to team up with DNV GL to develop a tool to monitor the structural integrity of this most versatile FPSO, designed and proven to operate in harsh environments with high uptimes and a maintained, strict regulatory and safety regime. This will enhance the safety and enables an optimised inspection regime.”

Realising true value from virtual reality
The major challenge when implementing new digital technologies is the same as when novel hardware technologies were introduced two decades ago. How can you trust that it works when the technology hasn’t been used before? Building trust in the quality and integrity of digital twins is key to extracting maximum value and, subsequently, to its adoption.

Companies are used to doing asset management and condition monitoring on physical assets and using this metaphor, we can employ them for condition monitoring of the data, the data science and the digital twin. Developing a digital duplicate is challenging, but success does not necessarily mean making value from it.

The use of twins, and importantly, trust in their accuracy can be significantly increased by ensuring that data and information reflect the most up-to-date condition of the physical asset. In order to ensure that the performance of digital twins matches expectations, the organisations involved require a structured, systematic approach.

To support the industry achieving its goals of high quality trustworthy digital twins, DNV GL has published two frameworks as important building blocks. In 2017, the data quality assessment framework DNVGL-RP-0497 was published to perform quality assurance across three areas:

1.         an organisation’s capabilities to create and maintain high quality data;

2.         measuring the quality of data; and

3.         assessing the risk of using the data.

Three years later, DNVGL-RP-0510 was published. This methodology assures the process of making, testing, deploying, maintaining and monitoring data science solutions based on data-driven methodologies like machine learning and artificial intelligence.

Solving the digital trust challenge will be key to its adoption, acceleration to use at greater scale, and acceptance as an accurate, valuable and trusted technology. The new RP seeks to remedy this issue as the technology begins a path of significant scaling across the sector. It is time to prove that twins can be trusted, and that the investments made in them give the right return.

Read part one here.

Kjell Eriksson, DNV GL

Kjell Eriksson has more than 30 years of international experience from the oil, gas and energy sector. He has managed DNV GL’s operations in the Middle East, Asia and Norway. He is currently responsible for establishing new partnerships and ways of collaborating with partners and customers enabled by digital solutions in DNVGL. He also holds the position as Chairman of the Faculty of Engineering at the Norwegian National University of Technology and Science (NTNU).

Francois-Xavier Sireta, DNV GL

Francois-Xavier Sireta has more than 13 years of international experience in R&D and consultancy for naval architecture for the Oil and Gas and Maritime industries. He has worked with DNV GL in Singapore and Paris and is currently responsible for the development of the DNV GL hybrid twin technology.