Oil and gas
PERSPECTIVES

New digital twin concept could show real-time status of safety risk and operations

digital twin

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Frank Børre Pedersen

Frank Børre Pedersen

Programme Director, Oil & Gas and Energy Systems, Group Technology and Innovation

Andreas Hafver

Andreas Hafver

Senior Research Scientist

  • DNV GL proposes new digital twin concept could show real-time status of safety risk and operations
  • The concept adds a risk-analysis layer to existing digital twins
  • It can capture uncertainty, and the effect of new knowledge and changes in conditions on operational performance and safety
  • New DNV GL research explains the model, and its use for more efficient safety management and value creation for the industry

Few technology developments in the oil and gas industry have captured the imagination more than the digital twin. As a virtual replica of a physical asset, the two are linked for life by data from sensors and other sources. Work by DNV GL and others has established that digital twins offer significant benefits to the industry for more informed decision making around optimizing production and maintenance. Twins are starting to provide consistent, accurate single sources of information for operators to manage the efficiency, safety, and environmental performance of their assets.

In an enhancement of the concept, DNV GL has developed the probabilistic digital twin. The idea is to tightly combine data-driven digital twins and risk-analysis modelling, which is still largely conducted manually using generic data from similar assets to make assumptions about what could happen.

Contact us:

Frank Børre Pedersen

Frank Børre Pedersen

Programme Director, Oil & Gas and Energy Systems, Group Technology and Innovation

Andreas Hafver

Andreas Hafver

Senior Research Scientist

”Our proposed probabilistic digital twin is designed to bring risk analysis into live use
Dr Frank Børre Pedersen,
  • programme director for Oil & Gas in DNV GL’s Group Technology and Research unit

“Our proposed probabilistic digital twin is designed to bring risk analysis into ‘live’ use,” said Dr Frank Børre Pedersen, programme director for Oil & Gas in DNV GL’s Group Technology and Research unit. “It would ‘bolt on’ a layer of probabilistic risk modelling to existing digital twins, capturing uncertainty, and the effect of new knowledge and changes in conditions on operational performance and safety.”

By providing an updated and asset-specific risk picture, such a twin would allow operators to adjust processes and procedures, or take preventive actions, to maintain an acceptable risk level at all times. This will enhance safety and may save money in an industry where a single, unscheduled downtime event can cost from USD2 million (m) to USD5m per day, Pedersen added.


Asset risk management can go live through probabilistic digital twins

Currently, risk models typically exist separately within engineering, operations, and health and safety disciplines, and are rarely brought together into operations. They are used mainly in desk studies, based on analysing historical data and offering only a static picture of potential risks.

“In reality, risk is dynamic, varying in time with operational conditions and the condition of the asset, but this is not captured by current risk models which are seldom updated and lack real-time and prediction capabilities
Dr Andreas Hafver,
  • senior research scientist
  • DNV GL’s Group Technology and Research unit

“In reality, risk is dynamic, varying in time with operational conditions and the condition of the asset, but this is not captured by current risk models which are seldom updated and lack real-time and prediction capabilities,” said Dr Andreas Hafver, senior research scientist in DNV GL’s Group Technology and Research unit.

The company proposed the probabilistic digital twin concept in September 2019 to address this gap. This evolution of the digital twin expands it into the risk analysis space as a new way to continuously, and in a digital format, add more value in day-to-day decision making (Figure 1).

QRA past, present and future
Figure 1: QRA past, present and future

Safety risk transformation

A probabilistic digital twin may include reliability and degradation models to predict the remaining lifetime of mechanical components. However, it is more than a predictive maintenance tool. Risk is not only about component failures, but also about exposure to hazards and how the asset is operated. A probabilistic digital twin can say something about the overall impact on safety by linking conditions to possible accident scenarios. It can do this by combining reliability models with models of the hazard exposure and the consequences if something goes wrong.

Figure 2 shows the relationship between physical assets, digital twins and probabilistic versions of them. Three main elements distinguish the probabilistic digital twin. First, probabilistic degradation and failure models, reflecting uncertainty and variability of conditions and processes that affect performance and lead to failures. Second, logic and relational models, relating performance variables to failures and loss events. Third, surrogate models, approximating heavier simulation models, allowing fast queries and enabling propagation of uncertainty and model coupling.

Here is a detailed description of a demonstration case involving a probabilistic digital twin monitoring the risk of a burst in a gas export pipeline from an oil and gas platform. A web app demonstration shows how this risk depends on how the pipeline is operated, offering decision support to operators.


Digital twins in the future

Having encouraged the use of digital twins in multiple industries, DNV GL is prepared for a future in which its clients will have them for all their assets. “Companies should not compete on safety, but instead learn and transfer knowledge that keeps the sector safe and secure. We are discussing with several clients how to make their digital twins probabilistic, so that they may be used for risk management,” said Pedersen.

Regulators worldwide require operators to know the status of safety barriers. The challenge is to know how detection of, for example, a degraded component affects the risk level. Operators also want to know if it is safe to continue operating, and for how long, if a problem is discovered. Understanding the safety integrity level can deliver cost savings through better timed and accurate maintenance planning.

DNV GL has developed risk models for several types of safety barriers, in which probabilistic reliability models are updated with information from tests and inspection, providing decision support to the operator. It is actively collaborating with Brazilian oil and gas company Petrobras on this to assess various safety barriers for wells and blowout preventers.

Pedersen said: “Many of our clients are building and maintaining digital twins of their assets. The probabilistic digital twin allows us and our clients to take advantage of all the information such twins contain to improve risk assessments. There is no digital twin that rules them all. Instead, there should be inter-communication between digital duplicates and the creation of a transparent data platform. We must keep pace with this rapidly evolving technology to improve risk assessments.”

Concept: Adding a layer of probabilistic risk models to digital twin
Figure 2: Elements of physical assets, digital twins and probabilistic digital twins (Source: DNV GL)

Disclaimer: 

DNV GL prides itself on providing accurate information but makes no claims or guarantees about the accuracy, completeness or adequacy of contents in this publication, and disclaims liability for any errors or omissions. The authors’ views here do not necessarily reflect DNV GL’s views.