How Shell is Evolving its Digital Twin to Drive Business Value

Want to get more value out of your digital twin implementation? Maybe you need to think more broadly about how you apply it, argues Romesh Mahajan, Product Line Manager for Digital Twin at Shell. Enter what Mahajan calls the “Mega Use Case.”

Many oil and gas companies have been using digital twins, the bits and bytes version of a physical asset or process, for years for a variety of reasons: improving asset management, increasing visibility into the supply chain and its risks, modelling and visualizing structures and products, visualizing processes.

Shell began its own digital twin journey in 2017, recounts Mahajan during a presentation at Oil and Gas IQ’s Digital Twins for Oil and Gas Online event.

Then, the focus was on creating a virtual representation of assets and associated asset behavior over time, and collecting data from the company’s physical assets to help workers make better decisions (such as around maintenance, for instance).

“The basic premise of bringing the digital twin was to bring those efficiencies where the data are integrated and available in a contextualized, visualized form so that the human layer [...] can spend more quality time and effort in decision making rather than spending time in collecting the data,” he explains.

However, the past few years have seen rapid developments in machine learning and artificial intelligence. Mahajan says that the company realized that with these new technologies comes capabilities such as predictive maintenance, modeling, and simulation. It is these new capabilities that can drive enormous business value.

“Suddenly what we realized is that the digital twin actually offers much more value than when we just look at it as a virtual representation of physical elements behavior, because the technology provides a platform and an opportunity,” he says.

For instance, in the initial phase of its digital twin, Shell would look at specific uses cases such as reducing the number of helicopter trips required to an offshore facility or reducing the number of visits from an EPC vendor. The savings of those uses cases amount to millions of dollars but they only scratch the surface of what is possible, according to Mahajan.

Instead, the business value of a digital twin goes up exponentially if you look at the mega use cases of digital.

He defines mega use case as “an end-to-end view of the task in each work process or cross work process.”

“The benefit of it is when you do [look at the work process end-to-end] the digital twin converts its efficiency and productivity into the strategic value,” says Mahajan.

“We started looking at digital twin as a tool for the work process integrator,” he says. “It suddenly gives you a completely different perspective of how digital twin can fit into the larger value conversation.”

Another key part to driving business value with a digital twin is ensuring accuracy of data. The quality of the data that informs a digital twin will inform the value of the decisions and actions that derive from that data. The challenge is that data, processes and procedures change over time and some of it may have been low quality to start with.

Shell wanted to address the data problem by “democratizing” the solution. They developed a process that they call “Pin my data” where users can tag outdated or incorrect information that they see in a digital twin (for instance, if a particular document is outdated or a piece of equipment is incorrectly tagged.) Once an item has been tagged, the company has a workflow where people in charge of making corrections to the data are alerted and can evaluate and authorize changes in the source system.

“That helps in democratizing the data and also ensuring that the data which matters most gets corrected the fastest,” explains Mahajan.

Watch the full presentation on demand here: Digital Twins in Oil and Gas

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