“Digital twin” defined for the oil field

The “digital twin” is one of many common colloquialisms used by data wizards during technical presentations on the virtues of the E&P industry’s digital transformation. Such terms also include “the internet of things” (IoT) and “machine learning,” among others. Confusingly, these terms can often be used gratuitously and interchangeably, without much explanation. While this ambiguous usage can render understanding of these terms difficult, the functions that the phrases refer to have the potential to make upstream operations in the oil patch much more efficient.

One of the early adopters of the term, digital twin—Dr. Michael Grieves, executive director for the Florida Institute of Technology’s Center for Advanced Manufacturing and Innovative Design—gives a good definition of the concept. “We can now strip the information from the physical space, in order to create the virtual space,” he said during the Landmark Innovation Forum and Expo in Houston on Aug. 23. “The whole idea of (the digital twin) is connecting the two models together, with the physical model and the digital model, so I’m going to collect the data from the physical space and populate the virtual space, and I’m going to use that information to make better decisions.” Although the digital twin’s use cases are many, such as predictive analytics on existing technology, and 3D printing, his ultimate vision is that it could enable engineers with the ability to design, assess and construct new technology completely, from within a virtual environment, before it is manufactured.

The digital twin is an important asset for NASA, due to the complications of creating equipment for space exploration, he said. It also can be applied to other industries, such as the marine, automotive sectors, and of course, the oil and gas business. “Take any task—exploring a field to find oil, building an oil rig, production, or building a rocket ship—all of these things can be separated into two areas, which are the most efficient ways a task can be completed, and the inefficient ways to complete a task that waste time, energy and resources,” Greives said. He goes on to say that, using a digital twin, the inefficiencies of a task can be replaced using information, or data, synthesized to automate the task.

Grieves outlined three different types of digital twins, including the prototype, the instance and the aggregate. A digital twin prototype is a single digital version of something that exists before something is made, while a digital twin instance is the paired digital version of an object connected with the physical object. A digital twin aggregate pairs multiple digital twins in existence, with all known configurations, to help remotely monitor equipment, predict how technology will react to future conditions it is placed in, and to learn how to make products better in the future. Prototypes exist in the creation phase of technology, while instances exist in the build phase, and aggregates exist in the operational phase, according to Grieves.

The concept of the digital twin has many possible use cases in the upstream oil and gas industry, from replicating a complicated well design to allow the well to be the most productive it can be, to refining a well control system to sharply decrease the risk of a blowout. It’s just one more concept that is helping to thrust the oil and gas industry through the digital transformation.