A scalable and broadly accepted methodology will benefit all parties
A methodology to qualify digital twins should adhere to the following three principles (Figure 1).
First, it must enable a modern agile approach to development of digital twins, while being systematic at the same time. Second, it should be scalable as digital twins mature and evolve. Finally, the qualification process must become broadly accepted in order to serve as terms of reference between supplier and buyer, enabling efficient development and procurement processes.
The methodology that TechnipFMC and DNV GL are developing aims to meet all three of these goals.
Data and algorithm quality is vital in digital twins
Advanced industrial operations depend on information systems for control and analysis. Data is increasingly considered to be of equal value to physical assets, and considerable costs are involved in collecting, storing, and acting upon data. An advanced digital twin is no exception.
The quality indicator component involves continuous assessment of the quality of data and algorithms, and of the twin’s output and automated recommendations. The indicator also includes periodic assessment of the functional elements and of changes to the asset.
The methodology and logic for how to ensure quality of data and algorithms is based on two recommended practices: DNVGL-RP-0497 Data quality assessment framework; and the forthcoming DNVGL-RP-0510 Framework for assurance of data-driven algorithms and models. DNVGL-RP-0497 includes a process for organizational data maturity assessment.
A recommended practice to assure digital twins
DNV GL’s Technology Outlook 2030 forecasts the emergence this decade of a full digital value chain in oil and gas, with the digital twin at its core, to reduce development times and costs in the energy transition.
As the TechnipFMC / DNV GL pilot project continues, the aim is to refine the methodology to publish by the end of 2020 a new DNV GL recommended practice for the quality assurance of digital twins to increase their efficiency.
While the methodology is a first for the oil and gas industry, it is being built on tried and tested foundations.
It is derived from Recommended Practice DNVGL-RP-A203 Technology qualification, and from other standards, adapting them for use on digital twins. DNVGL-RP-A203 was first published more than 20 years ago as a common framework for oil and gas industry players to gain acceptance for implementing unproven hardware technology. It has been used to demonstrate the trustworthiness of hundreds of technologies.
The digital twin qualification methodology being developed uses the definition of digital twins previously provided. It is applicable to alternative definitions, though this may require adjustments depending on the scope and application at hand.
“We invite other companies to come forward with digital-twin modules with which we can test and refine the assurance methodology before the recommended practice is published,” said Eriksson. “We aim to create a broadly accepted recommended practice that operators and technology providers can use as a key reference. Having a standardized approach will remove uncertainty and move the industry to a more efficient future powered by trustworthy digital twins.”