3 lessons from deploying digital twin technology in high-risk industries

Digital twin technology is reshaping high-risk industries like oil and gas, chemical processing, and energy by enabling smarter, safer, and more efficient operations. However, many still misunderstand digital twins as standalone solutions rather than comprehensive enterprise initiatives that integrate multiple technologies.

In a recent interview with Chemical Industry Journal, our COO, Thomas Grand, shared his expertise on how digital twins, particularly our Shared Reality solution, bridge the gap between static 3D scans and continuously evolving, intelligent models. He highlighted three crucial lessons for successfully deploying digital twin initiatives in industrial environments:

  • Transforming 3D reality capture into dynamic 3D models
    Traditional CAD-based models fail to reflect real-world site conditions in real time. By leveraging AI and advanced reality capture, companies can transform raw 3D scans into structured, interactive models that adapt as facilities evolve—without the time-consuming “scan-to-BIM” process.

  • Enhancing HSE risk visibility through virtual models
    Safety is a top priority in industries handling hazardous materials. Combining 3D reality models with critical technical data (e.g., Piping & Instrumentation Diagrams) allows teams to simulate safety scenarios, assess risks, and optimize processes like Lockout Tagout before stepping on-site.
  • Ensuring data transparency across systems
    Outdated documentation and siloed systems create inefficiencies and operational risks. A well-integrated digital twin connects 3D reality models with enterprise asset management (EAM) and maintenance systems (CMMS), improving data accuracy, collaboration, and decision-making across teams.

As digital twin solutions become essential for industrial resilience, companies that embrace a structured, real-time approach will unlock new levels of efficiency, safety, and sustainability.

Read the full article in Chemical Industry Journal

More to explore

Automated Spatial Tagging

Through automated spatial tagging—powered by in-scan Optical Character Recognition (OCR) embedded within Shared Reality and its Assetizer AI engine—operators can now extract physical label data directly from 360° panoramic imagery embedded in 3D scans. By anchoring physical plant labels directly to CMMS tags and 3D geometries without requiring an existing, validated P&ID as an intermediary, industrial operators can rapidly turn monolithic scans into interactive digital twins, streamline turnaround (TAR) preparations, and unlock comprehensive line-by-line P&ID validation via automated pipe crawling.

A Reality-Based Evolution for RBI and Process Safety Management

In brownfield refining, petrochemical, chemical, and offshore processing facilities, maintaining dedicated “inspection isometrics” represents one of the most resource-draining, error-prone overheads in Asset Integrity Management (AIM). For decades, plant operators have treated 2D Computer-Aided Design (CAD) piping isometric drawings as the mandatory medium for mapping Condition Monitoring Locations (CMLs), defining corrosion loops, and executing Risk-Based Inspection (RBI) workflows under API 570 and API 580/581.

Webinar: Automated Spatial Tagging

Discover how Shared Reality helps you visually isolate piping circuits and corrosion loops instantly, bridging the gap between engineering data and field execution.

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