Scaling Smart: How Digital Twins Transform Operations and Workflows in Oil & Gas

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from Zuken

Oil and gas operations involve thousands of interconnected assets, remote locations, and strict safety and environmental regulations. In this complex environment, digital twins—virtual replicas of physical assets, systems, or entire facilities—are proving to be more than a novel technology. They're becoming foundational tools for both strategic planning and day-to-day operations.

While many companies have successfully piloted digital twin projects, scaling these efforts across the enterprise—and realizing their full value—remains a challenge. At the same time, for those that do scale smart, digital twins are reshaping workflows, breaking down silos, and empowering teams to make faster, more informed decisions.

From Pilot to Platform: The Scaling Challenge

Digital twin adoption often starts small: a model of a critical compressor, a digital replica of a pipeline segment, or a simulation tool for maintenance training. These projects offer tangible benefits like predictive maintenance, reduced downtime, and better planning.

But scaling a digital twin strategy enterprise-wide introduces new layers of complexity. Companies must:

  • Integrate data across siloed systems 

  • Standardize models and metadata to make twins interoperable

  • Adapt legacy infrastructure to support modern digital capabilities

  • Navigate internal resistance from teams used to traditional ways of working

Successful scaling requires more than technology—it requires a mindset shift. Digital twins must be treated as strategic platforms, not just engineering tools. Open data architectures, cloud and edge infrastructure, and strong governance practices are critical enablers of scalable digital twin ecosystems. So is the right internal buy-in, as Jessica Beavers of HTC VIVE and Todd Daniel of Shiny Box Interactive shared at this year’s Industrial IMMERSIVE.

Digital Twins as Workflow Engines

Once implemented across an organization, digital twins become more than visual dashboards—they become engines for workflow transformation. In oil and gas, this plays out across a range of operational functions:

1. Real-Time Collaboration Across Teams

Field engineers, offshore operators, and asset managers can access the same live model of a facility that’s updated with sensor data, equipment status, and maintenance history. Decisions that once required weeks of back-and-forth or physical site visits now happen in hours, remotely and collaboratively.

2. Proactive, Not Reactive Maintenance

Predictive analytics within a digital twin help detect anomalies before they escalate. Instead of reacting to alarms, maintenance teams are alerted to early signs of failure and can schedule interventions during planned downtime—saving time, money, and risk.

3. Streamlined Work Orders and Execution

When a digital twin is integrated with CMMS or ERP systems, it can trigger maintenance workflows automatically, assign tasks, and even simulate the impact of taking an asset offline. This reduces manual handoffs and errors.

4. Faster Commissioning and Decommissioning

Digital twins of new assets allow simulation and testing before physical deployment, while models of aging infrastructure support safe and efficient decommissioning planning.

5. Enhanced Training and Safety

Interactive, 3D digital twins give operators a realistic environment to rehearse emergency scenarios or complex procedures. This is especially valuable for remote offshore installations where live training carries logistical, cost, and safety constraints.

Real-World Results

Several major oil and gas companies are already seeing measurable results:

These examples underscore the importance of scaling digital twins with purpose: not just replicating assets, but reimagining how work gets done.

Scaling Smart By Empowering People

Ultimately, scaling digital twins is about more than tech—it's about enabling people. By providing context-rich, real-time information in an accessible format, digital twins change how operators, engineers, and executives do their jobs.

The result? Safer operations. More resilient systems. And faster, smarter decisions at every level.

For oil and gas companies facing mounting pressure to improve efficiency, reduce risk, and meet sustainability targets, digital twins offer a pathway forward.

Playbook for Scaling O&G Digital Twins

Digital twins can offer great ROI—but only if they’re deployed with intention and integrated into daily workflows. Take these practical steps to turn a pilot project into an enterprise-wide, operations-improving capability.

Define Your Strategic Objectives

Before scaling, make sure your team is aligned on why you’re using digital twins.

Checklist:

  • Get on the same page about what business outcomes you’re targeting (e.g., reduced downtime, emissions tracking, improved safety)

  • Identify your biggest bottlenecks and risks where digital twins could improve workflows

  • Outline digital twin stakeholders and champions in your organization

Tip: Start with a single value stream (e.g., maintenance) to prove the concept and build internal support.

Build a Strong Data Foundation

Digital twins are only as good as the data that powers them. Focus on making your data accessible, clean, and standardized.

Checklist:

  • Conduct a data audit across your systems

  • Break down silos between those systems

  • Implement data standards so data format is consistent

  • Ensure data storage and sharing protocols are secure

Tip: Edge computing is often required to process real-time data at remote or offshore sites.

Launch into Real-World Workflows

A digital twin isn’t useful if it’s sitting in a system no one uses. 

Checklist:

  • Embed the digital twin into actual workflows

  • Identify what works well and what could use improvement

  • Document potential use cases for future development

Tip: Focus on 2 or 3 key workflows to refine before expanding to additional areas.

Monitor, Measure, Improve

A digital twin isn’t a static model—it needs continuous validation and improvement.

Checklist:

  • Assign ownership for maintaining data accuracy

  • Establish KPIs for each twin and process (uptime, alerts triggered, tasks automated)

  • Incorporate user feedback to refine features

  • Watch for opportunities to integrate AI, XR, and other technologies

Tip: Schedule regular “twin audits” to make sure the digital models are aligned with real-world changes.

Scale Strategically

Once you’ve seen success in a focused area, scale deliberately and iteratively.

Checklist:

  • Replicate successful implementations across similar assets

  • Create reusable digital twin templates or libraries

  • Standardize across regions or business units

  • Evaluate enterprise tools to create and manage digital twins

Tip: Measure ROI (e.g., downtime avoided, labor hours saved) continuously to justify further investment.