From Novelty to Necessity: Digital Twins Deliver Real-Time ROI

_ The long history of “twinning” as a means to represent and understand a physical object includes sketches, blueprints, physical models, and computer-aided design (CAD). As digital technology has matured, so have twinning capabilities. Once a novel concept, the digital twin has become an essential tool for managing and operating the oil and gas industry’s infrastructure. It has been less than a decade since the term “digital twin” first appeared in the OnePetro database, but the industry’s uptake on these virtual replicas has been rapid. And why not? They replace outdated tools with dynamic, real-time models that provide a continuous, comprehensive view of an asset’s health and performance. The industry has learned a great deal about what it takes for a digital twin to be useful—such as current and accurate models and data—and the different ways the tool can be used to benefit the bottom line, such as through risk mitigation or as an operations twin. Is It the Right System? As digital twins are created for real-world objects, the question of trustworthiness is paramount. While presenting OTC 35756 at the 2025 Offshore Technology Conference in Houston, Eric VanDerHorn, manager of technology-digital research at American Bureau of Shipping (ABS), said a digital twin’s credibility should be predicated on its planned use. “The first aspect of considering credibility is really to understand that it’s all based on content. It’s based on the use case. So, we might identify that the digital twin that is established as credible for one use case, may not be credible for another, ” he said. Establishing credibility for a digital twin requires both verifying the twin is built to relevant technical specifications and validating the twin fulfills its purpose and goal. The paper aims to establish a framework for verifying and validating digital twins related to the problem they are trying to solve (Fig. 1). The key questions, VanDerHorn said, are “Did we build the digital twin system right? ” and “Did we build the right digital twin system? ” Verification of the twin revolves around how compliant it is with the requirements and specifications detailed in conceptual models, mathematical models, and other constructs, he said. Verification categories include code, solution, and stored representation. Code verification focuses on reliability, robustness, and security of the software, as well as the correctness of the numerical algorithms in the code, the paper’s authors wrote. Solution verification focuses on the numerical accuracy of the solution to governing equations, while stored representation verification is focused on the accurate representation of the design or the real-world asset. The ideal digital twin would see a virtual representation perfectly mirroring the physical item, but this is not likely to be technologically feasible, the authors note, with the scope, fidelity, and update frequency of the twin being dictated by its intended use case.