How oil & gas and chemicals companies can drive value with digital twins

One of the biggest issues energy companies face today is successfully marrying technology and the people who must use it. Because most companies manage their digital twin implementation through the technology or innovation function, projects often are focused on selecting flashy technology rather than on the day-to-day needs of the workforce.

Project leaders who embrace a “build it and they will come” mentality believe that the latest, most sophisticated tech will win over the masses. But that thinking can be a project killer.

Every digital twin project should begin with value in mind, not technology. What drives value for this particular asset, and how can we help employees maximize that value? What are the biggest challenges employees face in operating and managing this asset? What information do they need to make better decisions?

Too often, development teams design digital twins to maximize the capabilities of the tools they want to implement — but it’s not what the employees who manage the asset want or need. Without alignment, the company winds up with an expensive toy that no one uses. It’s often too difficult or time-consuming to learn or it doesn’t deliver data that the asset team needs. In some cases, the tech is too robust or has too many capabilities, overwhelming employees who need a simpler tool.

In fact, when a solution is designed correctly for the people charged with its daily use, substantial upskilling is often unnecessary, and employees embrace it because it makes their jobs easier and helps them be more successful.

Because digital twin technology is evolving at a rapid pace — for example, AI integration is now a core component rather than an add-on, and interoperability standards continue to mature — it’s even more important that it is fit-for-purpose and supports real-world processes.

Companies should approach digital twin development in three phases:

1. Gain a deep understanding of the asset, its unique role in the company’s business strategy, the key challenges the asset team faces and how the asset’s value can be maximized.
2. Identify the asset team’s needs and match those with available digital capabilities. How can a digital twin assist team members in making better, faster decisions? Where are the data gaps that keep that from happening?
3. And finally, select the appropriate technology and designing and implementing a platform that fits the sweet spot between “too much” and “not enough.” 

Starting with technology — without identifying the value proposition — is a recipe for failure.

Success with digital twins requires clear use cases with strictly defined parameters. There is no “one size fits all” digital twin platform because every asset has different needs.

Because digital twin development is about unlocking value, asset teams should drive the use case identification process, not tech engineers. Where are the team’s pain points? Where do team members see value leakage or opportunity for efficiencies? Companies should prioritize those use cases that can deliver a solid return on investment; for example, improving well yields or reducing emissions. 

Oil and gas companies who have adopted and prioritized digital twin use cases have seen a significant reduction in windshield, in-field or on-site time, and enhanced safety measures. Digital twins can decrease the risk of inaccurate planning for turnarounds in both upstream and downstream and are crucial to streamlining operational handovers, enhancing business decisions and maximizing value.

A key requirement for successful use cases is access to accurate, timely data. Legacy systems that don’t communicate or that don’t share common naming conventions can make it impossible to collect and collate the data needed to make a digital twin viable. That obstacle is exacerbated at many energy companies, which tend to operate in a siloed fashion, making access to data across business units a challenge.

Securing data properly is also an issue. The fear of cyber threats or data loss is real. Building proper safeguards takes time and expertise; without those efforts, even promising use cases will be untenable.

Creating valuable digital twins is a challenging process that requires significant investment in data infrastructure and modeling and simulation capabilities. Trying to do too much too soon can lead to mistakes and failed projects.

Companies in the initial stages of digital transformation may not have the necessary foundation in place, both in terms of their data capabilities, as mentioned earlier, or their workforce. Having access to data scientists, engineers with domain expertise, industry 4.0 practitioners and software developers — either in house or as consultants or contingent workers — is crucial for digital twin success.

Energy companies face significant challenges in finding and retaining talent with these skills as other emerging technologies are tapping into the same pool of skilled workers. The Future of Energy survey found that just 46% of respondents say their company’s workforce has the skills it needs to realize value from its current digital twinning efforts, and 33% say they have the skills for their digital twin plans.

Another major challenge is that digital twin development and implementation is time-consuming, requiring the installation of remote sensors, creation of a data pipeline, implementation of cybersecurity protection and more. Although many digital twins utilize AI in their operation, stand-alone AI is often given priority at energy companies, because tools can be developed and implemented more quickly and deliver value faster. That means foundation building for digital twin development may fall behind other tools.

One way to speed adoption and buy-in is the use of low-code tools in combination with large language models, or LLMs, empowering engineers and operators to build their own digital twins quickly and easily.

Companies that are currently realizing value from their digital twin investments typically share five key attributes:

1. They begin with the proper strategy, using a value-driven approach to identify appropriate use cases.
2. They design digital twins based on the needs and challenges of individual asset teams. No top-down approach is used; each digital twin is unique and structured to deliver asset-specific value.
3. They focus on unlocking their data, across disparate systems, to enable digital twin development. By creating a gold standard for data across the organization, they make it possible for every digital twin to utilize consistent, accurate data.
4. They ensure planned users are appropriately trained and supported throughout the adoption process.
5. They communicate wins — with a focus on how the technology is helping people — across the organization so that everyone sees tangible progress. By doing so, digital twins become embedded in “how we work” rather than an anomaly.

Finally, smart companies will work to develop a platform mindset — building the internal capabilities to create and utilize digital twins across the enterprise using the latest platform technologies. By connecting those digital twins to support end-to-end business processes — for example, subsurface and surface workflows — companies will unlock even more value.

By approaching digital twin technology with a thoughtful, value-centered strategy, oil & gas and chemicals companies can maximize their technology investments and drive value across the enterprise.

EY teams can help by creating a successful digital twin implementation roadmap that focuses on helping asset teams identify value opportunities and creating tools to optimize operational performance, increase efficiency and improve safety.