Seeing Double: How the pandemic has influenced a revival of digital twin technology

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How has market demand and technological progress shaped the digital twins of today from those of the past. And what is its future?

“Digital twin is not something revolutionary”, states the Innovation Lead at one of the world’s largest international oil storage operators. “It’s a technology – and a concept – that has been around for years”.

He’s right, and in an era driven by digitalization buzzwords, and an industry increasingly reliant on innovation for survival, it can be easy to forget that some of the new technologies shaping the next normal are not as new as they seem.

How has market demand and technological progress shaped the digital twins of today from those of the past. And what is its future?

Space Origins: a brief history of digital twin technologies

While typically associated with twenty first century technology, the use of digital twin – a virtual replica to run simulations before physical devices are built and deployed –  has been around since the 1970’s. NASA conducted a digital twin model of Apollo 13 which allowed engineers to test possible solutions from earth.

Today, they’re a strong commercial asset: it is estimated that approximately half of global industrial companies use digital twin technology, with over 21 billion digitally connected sensors, saving billions in maintenance, repair and operations.

In the downstream oil and gas sector, the process started gaining attention a few years ago, with reports from market leaders such as AVEVA and Westwood Global Energy Group noting that “the most advanced refineries are beginning the process of creating a Digital Twin…”. 

Then the pandemic hit, and in its economic fallout, the demand for real time risk mitigation and responsiveness accelerated. The health crisis has combined with the fluctuation in oil prices to create a “perfect storm”, in which digital twin technology can thrive, and evolve.

Perfect Storm, perfect opportunity

This new generation of digital twin needs to align with the current market’s specific needs. As Colin Parris, CTO of GE Digital, explains:

If the first essential ingredient of today’s digital twin is that it represent the current state of an asset, the second is that it must be capable of learning and predicting how that asset will fare over time under varying conditions.”

According to Parris, what sets the new digital twin models apart from those of the past is agility: their ability to react and respond in real-time to the vast amounts of data used by today’s operators.

We have always built models, but after we have built a model we stop(ped) using it,” he explains. New technologies have meant that digital twin models can now be updated to reflect reality, and match the dynamic climate they operate in.

As the recent over-production of oil, and scrutiny from stakeholders, regulators and consumers pushes sustainability to the front of downstream operators’ agendas, digital twin can also help provide clearer information on emissions waste and help set sustainable goals. Through a better monitoring of consumption and waste production across the supply chain, implementing digital twin allows downstream players to maximise sustainability, while maintaining profit, at a time where these are more critical than ever.

Protecting your digital assets

If data is digital twin’s most valuable asset, it can also be its greatest risk. While incorporating AI learning and IoT sensors into digital twin technology can help cut both costs and downtime, it also increases an operator’s virtual – and physical – attack surface.

“Cybersecurity is one of our top priorities”, confirms Oliver Wotton, Head of IT Manufacturing at Shell. “It’s not just about having the latest shiny box”. It is about having the existing infrastructure in the real world to support the next virtual leap.

Part of building this infrastructure is understanding the business case behind your digital twin decision. In a recent article, Peter Reynolds, Contributing Analyst at ARC Advisory, noted that

The Oil & Gas and Chemical industries spend almost USD 1 billion annually on process simulation and optimization software. However, much of this money is wasted on models which are abandoned, inaccurate (or) replicated in other parts of the organization…”

His claim is echoed by industry media, who believe that “while some oil and gas companies are taking advantage of digital twin technology in some form – most are not capturing the significant value it offers.

As digital twin evolves to meet current market demands, it is equally important for the current market to understand the how and why of implementing the technology. This involves the right partnerships, a clear understanding of your current digital assets, and making sure your workforce is trained and equipped to adapt to the new processes and technology.

Self-healing supply chains for the road to recovery

Since before the pandemic, the idea of a “self-healing supply chain” – a business strategy that allows infrastructure to automatically update, and predict and prepare for unexpected events – has captured operators’ attention. Digital twin is an integral of this concept, and the current health crisis has simply increased the importance of supply chain resilience on the road to recovery. Savvy operators understand this, and are willing to invest.  The $3.1 billion digital twin market is expected to grow at a CAGR of 58% before 2026.

In Asia, Shell recently announced the launch of a four-year pilot project at its Pulau Bukom refinery to implement Digital Twin. The initiative is part of a wider plan to integrate virtualisation throughout the company infrastructure: by 2025, all critical field operations at the Pulau Bukom site will be performed through tablets

In the age of the socially distanced workforce, production and personnel operations need to adapt to keep employees safe, and business healthy. Digital twin can help monitor both physical distancing for essential employees on site, and manage plant and refinery equipment restraints to accommodate alternating lines and shifts. Algorithms can collate and analyse scheduling, supply and logistics data to create multiple supply chain “game plans” on how to best respond to changing circumstances of an uncertain future. And, where the pandemic has exposed vulnerabilities in existing frameworks, forecasting digital twins can use predictive analytics to anticipate changes in consumer demand and explore process and design alternatives to build a more resilient business model.

These are new demands, for a new era, and the digital twin models of the past will need to adapt and evolve at unprecedented speed to step up to what’s at stake in the present. And with an anticipated global value of $48.2 billion over the next six years, this predictive technology is foreseeing a strong future

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