Digital twin: Guiding asset owners to overcome common pitfalls
Wood is partnering with clients across energy and the built environment to adopt digital solutions that drive innovation and shape the future of our industries. We are exploring new solutions in technology to create greater operational value and accelerate delivery at every stage of the asset lifecycle. There are three technology product lines we continue to invest in to drive performance, reduce risk and lower costs – connected worker, digital asset and autonomous operations.
The Wood Connected Worker product line is designed to enable clients to maximise value from safer and more effective operations and enable connectivity among our field employees to drive higher productivity and efficiencies.
Connected Worker brings together a core suite of solutions and applications to digitalise time writing, promote real time workforce engagement, enable remote collaboration and put Wood’s lifesaving rules in the palms of our people’s hands. We can also advance things like data logging and field service tools via digital solutions tailored to the job.
Digital asset is an immersive Digital Twin platform created by Wood and our technology partners that makes critical information accessible to anyone working for or in support of an asset. The platform integrates existing digital data sources into a 3D visualisation of the physical asset, creating a digital platform for accessing the information operations need to carry out their work.
It removes the need to go offshore to see the asset and visualises information in a more intuitive way to improve the planning and preparation of work, removing delays, errors, inefficiencies and uncertainty by making information readily available to those who need it.
For data collection we are running a live pilot at a facility using robots and drones whereby 3D models of assets are generated from visual data capture. We can then use a variety of sensors mounted on the robots or drones to attribute operating parameters such as visual, thermal, infrared, acoustic and methane emissions to the 3D model. Regular rounds autonomously detect any variations of these operating parameters and generate an anomaly report. This technology allows us to effectively develop a Digital Twin without having to retrofit Internet of Things (IoT) or build engineering models.
Data analysis takes the anomalies detected during the collection phase, ranking, and prioritizing them using Artificial Intelligence (AI) and machine learning. Our operations teams partner with consulting teams where they have already developed technology around systems optimisation and digital integration.
The execution phase will utilise tools from the Connected Worker toolbox to resolve an identified problem. In effect, it means when a human is engaged in the workflow for the first time, they receive a repair notification on a device telling them where to go, what they need to do and everything they require to be able to do it.
The learning phase is intended to capture real time results from the execution phase to keep developing the analysis AI.
We see a tremendous opportunity in potential use cases for this technology and are homing in on methane management as a valuable application. While we continue refining this technology and working to bring this commercial model to market, you can watch the following video, which further illustrates the possibilities of Autonomous Operations.