Oil & Gas
DNV GL RP on Quality Assurance of Oil & Gas Digital Twins
DNV GL has published the oil and gas industry’s first recommended practice on how to build and quality-assure digital twins. Developed in collaboration with
DNV GL has published the oil and gas industry’s first recommended practice on how to build and quality-assure digital twins. Developed in collaboration with TechnipFMC, DNVGL-RP-A204 Qualification and assurance of digital twins sets a benchmark for the sector’s varying approaches to building and operating the technology.
It guides industry professionals through
Assessing whether a digital twin will deliver to stakeholders’ expectations from the inception of a project
Establishing confidence in the data and computational models that a digital twin runs on
Evaluating an organization’s readiness to work with and evolve alongside a digital twin.
Seventy-five percent of organizations implementing Internet of Things (IoT) already use digital twins or plan to within a year, according to Gartner. However, there has previously been no commonly agreed methodology for developing and operating the technology among global oil and gas operators and their supply chains.
DNV GL’s RP provides valuable guidance for digital twin developers, introduces a contractual reference between suppliers and users, and acts as a framework for verification and validation of the technology. It builds upon the principles of DNV GL’s Recommended Practices for the qualification of novel hardware technology and assurance of data and data-driven models.
The methodology behind DNV GL’s new RP has been piloted on 10 projects with companies including Aker BP, Kongsberg Digital and NOV Offshore Cranes. It has also been through an extensive external hearing process involving the industry at large. In addition, TechnipFMC’s deep domain knowledge and expertise in digital technologies and oil and gas infrastructures has made an essential contribution to jointly developing the RP.
The framework provides clarity on the definition of a digital twin; required data quality and algorithm performance; and requirements on the interaction between the digital twin and the operating system. It addresses three distinct parts: the physical asset, the virtual representation, and the connection between the two. This connection amounts to the data streams that flow between the physical asset to the digital twin and information that is available from the digital twin to the asset and the operator for decision making.