Digital Twins Offer Value to Energy, Oil and Gas Companies

Digital Twin Use Cases for Energy, Oil and Gas

Digital twin technology offers meaningful benefits to energy companies, especially in the realm of proactively optimizing performance, identifying efficiencies and preventing possible causes of downtime. These are just a few of the top uses for digital twins among energy, oil and gas companies.

Powering Predictive Maintenance

IDC Research Director Gaurav Verma  says one of the biggest near-term benefits is predictive maintenance.

“By modeling how equipment behaves under normal and stressed conditions, digital twins can identify early signs of wear or failure, helping companies schedule maintenance before breakdowns occur,” he explains.

The benefits include reduction of unplanned downtime, extension of asset life and lower repair costs.

Verma says predictive maintenance works by feeding time-series data from supervisory control and data acquisition (SCADA) systems and industrial historians such as AVEVA’s PI System into a digital twin environment, where artificial intelligence (AI) and machine learning models analyze equipment behavior to detect anomalies and forecast failures.

EXPLORE: Learn how to manage the convergence of IT and OT safely. 

He adds that companies are increasingly moving toward hybrid modeling approaches that combine physics-based models with data-driven machine learning models to improve accuracy and reliability.

“The common wisdom here is to use the hybrid approach to generate more reliable maintenance insights,” Verma says.

The result is earlier fault detection, better maintenance planning, and improved reliability for equipment that is central to production and safety.

Electric Grid Optimization

Digital twins are increasingly being applied to support grid optimization, planning and resilience, with utilities and grid operators using digital replicas of grid infrastructure to monitor conditions, simulate scenarios and improve decision-making in real time.

Verma explains that digital twins in grid operations often overlap with virtual power plant (VPP) concepts, which aggregate and coordinate distributed energy resources, and are used by distribution system operators (DSOs) to manage increasingly complex networks.

“These systems ingest real-time data from sensors, OEM devices and weather feeds to forecast asset behavior and operational risks,” he says.

By combining operational and environmental data with AI-based forecasting, grid operators can simulate how assets will perform under different load and climate scenarios and adjust operations accordingly.

Energy Efficiency and Management

The technology is playing a growing role in design and planning, allowing engineers to test new grid configurations, equipment layouts and operating strategies in a virtual environment before committing capital in the field.

Christian Salazar, director of portfolio management at Honeywell, says digital twins help companies model real-world operations and evaluate the impact of potential changes before investing.

He explains that companies take theoretical engineering and process models and make them flexible enough to reflect real operating conditions, allowing teams to test different scenarios and understand how operational or efficiency projects would perform in practice.

That approach gives operators clearer visibility into expected outcomes, including energy savings and financial returns, and helps reduce risk in project planning, particularly in complex environments such as refining.

“Digital twins are used to provide that clarity in project planning,” Salazar says.

FIND OUT: How artificial intelligence is helping modernize energy grids.

Digital Twins Aid Training

Beyond operations, digital twins are increasingly used for training and simulation, giving technicians and operators a safe environment to practice procedures and respond to emergency scenarios.

Verma says the approach is especially valuable for onboarding new employees and subcontractors who may have little familiarity with offshore platforms; liquefied natural gas  facilities; or floating production and storage units with complex layouts, equipment and safety systems.

“Many workers are not familiar with their work setting, from layout and equipment to safety,” Verma says.

Digital replicas of these facilities allow companies to simulate real working conditions and expose trainees to site-specific layouts, workflows and hazards in a controlled environment.

Salazar says this allows workers to safely rehearse abnormal and hazardous scenarios and practice recovery procedures without real-world risk.

The next step, he says, is keeping those training twins continuously synchronized with live operations so they don’t drift out of date and lose credibility with frontline staff. This approach makes operators more likely to trust and use the system.

DISCOVER: Artificial intelligence is helping utilities defend against cyberthreats.

Guidance for Implementing Real-Time Digital Twins

Salazar says technology leaders should approach digital twin investments by starting with clear, high-impact use cases and ensuring the technology can remain accurate as operating conditions change.

He argues that safety is the most practical entry point, particularly for organizations that have not yet deployed digital twins, because it delivers immediate value by helping operators train for adverse conditions.

From there, companies can expand into performance and process optimization by identifying bottlenecks and using digital twins to surface operational constraints across complex systems.

He says organizations should look for partners with a long-term, end-to-end strategy for maintaining and updating digital twins, not just deploying them.

That includes service capabilities; back-office resources; and technical support to keep models synchronized with such real-world conditions as equipment, terrain, weather and workflows evolve.

“If the digital twin doesn’t stay up to date, it’s not a useful twin,” Salazar says.