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Continuous evaluation of integrity risks for high-value, safety-critical and environmentally-sensitive assets is a complex challenge.
Asset integrity is typically determined by referring to evidence compiled over the lifetime of an asset; including original design specifications, as-built and installation records, inspection and monitoring data, maintenance and repairs, and continued engineering analysis.
For many installations, these records exist as standalone documents providing a discrete snapshot of a moment in time. These records require assimilation into a holistic view on asset fitness for purpose by a uniquely competent organisation, taking into account their interconnected nature to build a ‘narrative’ as to the condition of the asset.
The rise of digital twinning is turning this accepted practice onto its head. A ‘digital twin’ is a virtual copy of an asset; used to bring volumes of discrete information into a single, integrated and digitised model of the asset. As a living replica of a physical structure, it is continually updated to reflect its real-life counterpart’s present condition, while also acting as a historical record.
At its most basic level, the digital twin provides a means of organizing data so that it’s easily accessible. At the most useful level, digital twins can be used to support real-time fitness for purpose analysis, enhance predictive and preventative maintenance, simulate performance under adverse events, identify risk and optimise asset integrity best practices.
In addition to physical assets, digital twins can also be relied upon to represent processes, systems and services. A recent MarketsandMarkets report estimates the total digital twinning market to grow from $3.1 billion USD in 2020 to $48.2 billion in 2026.
For offshore energy infrastructure, digital twins may start from as-built CAD models, but require regular or even real-time input of real-world data to ensure the digital twin remains representative of the asset. This requires environmental data, asset response data, and quantitative knowledge of physical condition. Providing best available data to optimise digital twins is challenging, and when it comes to underwater infrastructure, the challenge is magnified further.
OPTIMISED DIGITAL TWINNING
Best-in-class digital twinning specialists apply hydrodynamic analysis and finite element methods to digital twins for real-time condition monitoring. While best available data is crucial, more data is not necessarily beneficial. Hypothetically, a digital twin of an offshore structure could be built entirely on in-situ 3D scans and system response monitoring on every structural member. However, this would result in excessive detail in areas with low criticality, leading to slower computation speeds and increased costs to maintain and update the digital twin.
Instead of using 3D scans of all structural components, the digital twins can use estimated geometries based on 2D measurement outputs, such as calliper gauging or plate thickness measurement. When measurements approach minimum strength criteria or accumulated fatigue damage becomes critical, higher resolution 3D data can be integrated.
Correctly-optimised digital twins use a risk-based approach, using input data with resolution equivalent to the criticality of individual components. This approach of using variable levels of data resolution will provide the greatest cost-benefit to drive the digital twin. The digital twin itself can then be used to identify critical components to determine the level of data resolution required.
Digital twins provide much-needed recordkeeping continuity between the design to construction, operation to decommissioning lifecycle of an asset, which often involves personnel handover and loss of knowledge as assets age and operators change.
Despite the advantages of the technology, predictive models based even on the most reliable data will still have blind spots for so-called ‘black swan’ events. Over-reliance on digital twins comes with the risk of marginalizing existing integrity management practices, including visual inspection and conventional NDT which often uncover unexpected results.
However, digital twinning provides significant advances in reliable, data-based decision-making, lowering the reliance on personal competency and continuity over the lifetime of an asset. Adoption of digital twinning will result in lower operating costs, increased asset life, and more reliable management of risk.