Overview
The oil and gas sector is undergoing a significant transformation, driven by the need to enhance operational efficiency, ensure safety, and meet environmental regulations. Among emerging tools and techniques, digital twins have emerged as a pivotal innovation. AVEVA, a global leader in industrial software, defines a digital twin as a dynamic, virtual representation of a physical asset, system, or process. By integrating real-time and historical data, digital twins provide a comprehensive view of operations, facilitating informed decision-making and proactive maintenance strategies. As OSIsoft PI Experts, MetaFactor’s team is equipped to help your organization navigate this transformation. As the industry continues to embrace digitalization, the role of digital twins is expected to grow, offering profound insights into operational assets and enabling more informed and precise decisions.Understanding Digital Twins
A digital twin is a dynamic, virtual representation of a physical asset, process, or system that mirrors its real-world counterpart in real time. Unlike static models, digital twins continuously integrate data from various sources. This enables organizations to monitor performance, predict outcomes, and optimize operations throughout the asset’s lifecycle.
The functionality of a digital twin relies on the continuous aggregation of data from multiple sources, including sensors, control systems, and external databases. This data is processed and integrated into a dynamic model that accurately reflects the physical asset or process. Advanced analytics and artificial intelligence are then applied to this model. This helps to analyze behavior, performance, and potential issues, enabling predictive maintenance and informed decision-making.
How Does a Digital Twin Work? (Well Production Example)
To build a digital twin of a production well, the asset is equipped with a network of sensors that monitor key operational parameters. These sensors collect real-time data on variables such as pressure, temperature, flow rates, and fluid composition. The information is continuously transmitted to a processing system, where it updates the digital replica of the well. Once populated with accurate, real-time data, the digital twin can be used to run simulations, predict performance issues, and evaluate optimization strategies. This enables engineers and operators to gain valuable insights, detect anomalies early, and apply improvements directly to the physical well, ultimately enhancing production efficiency, safety, and asset longevity.Key Features of Digital Twins
Beyond the operational benefits, digital twins offer powerful technical features that enhance how oil and gas assets are designed, monitored, and optimized. These core features include:
Real-Time Data Integration
Digital twins are continuously refreshed with real-time data from sensors, IoT devices, and control systems. This live data flow ensures that the virtual representation always reflects the current state of the physical asset, enabling accurate monitoring and faster decision-making.
Simulation and Predictive Modeling
By incorporating advanced simulation tools, digital twins can replicate the behavior of assets under various conditions. This capability supports predictive analysis, performance optimization, and scenario planning, helping companies proactively manage risks and improve operational strategies.
Bi-Directional Communication
Digital twins create a two-way bridge between the digital model and the physical environment. Not only do they receive live data, but they can also send back insights and instructions to guide real-world actions, enhancing operational responsiveness and asset performance.
Remote Monitoring and Control
Operators can oversee and manage physical systems remotely through their digital twins. This enables real-time diagnostics, predictive maintenance, and performance optimization. All without needing to be physically present at the site, which improves safety and reduces costs.
Key Benefits of Digital Twins in Oil & Gas
As the oil and gas industry faces increasing pressure to improve efficiency, safety, and environmental performance, digital twins have emerged as a strategic enabler. By creating dynamic, data-driven models of physical assets and processes, digital twins unlock powerful capabilities that deliver value across the entire asset lifecycle. Below are some of the most impactful benefits observed in real-world applications.
Predictive Maintenance: Minimizing Downtime and Costs
Digital twins enable continuous monitoring of equipment health by integrating real-time sensor data with historical performance metrics. This proactive approach allows for the early detection of potential failures, facilitating timely maintenance interventions that prevent unexpected breakdowns and costly downtime. For instance, by analyzing vibration patterns and temperature fluctuations in compressors or pumps, operators can schedule maintenance before a minor issue escalates into a major failure.
Enhanced Safety: Simulating Risks Before They Occur
Digital twins allow for the simulation of hazardous scenarios, such as equipment malfunctions or emergency shutdowns, in a virtual environment. This capability enables operators to assess potential risks and develop mitigation strategies without exposing personnel to danger. Moreover, it aids in training staff to respond effectively to emergencies, enhancing overall safety protocols.
Optimized Production: Real-Time Operational Adjustments
By providing a comprehensive view of operations, digital twins empower operators to make informed decisions that enhance production efficiency. They can simulate various production scenarios, assess the impact of changes, and implement adjustments in real time. This dynamic optimization leads to increased output, reduced energy consumption, and improved resource utilization.
Environmental Sustainability: Monitoring and Reducing Impact
Digital twins contribute to environmental stewardship by enabling precise monitoring of emissions, waste, and resource consumption. They facilitate compliance with environmental regulations by providing accurate reporting and identifying areas for improvement. Additionally, they support the development of strategies to minimize ecological footprints, such as optimizing flaring operations or reducing water usage.
Different Types of Digital Twins in Oil & Gas
Different types of digital twins exist depending on the scale and focus of what is being modeled, with the main distinction lying in their specific areas of application. In many cases, multiple types of digital twins operate together within the same system or process, working in coordination. Let’s explore each type to better understand their roles and how they contribute to overall operations.
Component Twin
Definition: A digital replica of a single component, such as a valve or sensor.
Example: A pressure relief valve on a high-pressure pipeline can have its own component twin. The twin monitors real-time data like pressure thresholds and mechanical integrity, predicting wear and enabling proactive maintenance to prevent costly shutdowns.
Asset Twin
Definition: A digital model of an entire asset, made up of multiple components working together.
Example: A compressor skid; including compressors, motors, control panels, and piping — can have an asset twin. Operators use this twin to simulate operations, predict downtime, and fine-tune performance for optimal output and reduced maintenance costs.
System Twin
Definition: A full digital representation of interconnected assets working as a larger system.
Example: An offshore production platform, with its integrated drilling units, oil separation systems, and export pipelines, is modeled by a system twin. This allows real-time system-level monitoring, scenario simulations, and operational optimization across all platform activities.
Process Twin
Definition: A digital model of an entire business or operational process.
Example: The complete refining process that transforms crude oil into gasoline, diesel, and petrochemicals can be captured by a process twin. It simulates flows, reactions, and equipment interactions, helping to maximize yield, lower emissions, and streamline plant operations.
Available Software Solutions for Digital Twins
As digital twin technology gains momentum in the oil and gas industry, a wide range of software platforms has emerged to support its implementation. These solutions are offered by industrial automation leaders and major cloud service providers. Each brings unique capabilities, from asset modeling and real-time monitoring to advanced analytics and process optimization.
Industrial Vendors
AVEVA offers comprehensive digital twin solutions that integrate engineering, operations, and performance data.
GE Digital offers digital twin technology that applies advanced analytics and machine learning to model assets and systems.
Cloud Vendors
AWS IoT TwinMaker facilitates the creation of digital twins for real-world systems, integrating data from various sources to provide a holistic view of operations.
Azure Digital Twins is a platform that enables the modeling of physical environments, allowing for real-time data integration and analysis.
Google Cloud offers digital twin solutions, such as the Supply Chain Twin, which aggregates data to provide comprehensive insights into operations.
Open Source
Eclipse Ditto is an open-source framework designed for managing digital twins of IoT-connected devices. It allows for real-time data synchronization and control, making it suitable for various industrial applications.
These software solutions offer a range of capabilities for implementing digital twins in the oil and gas industry, from asset modeling and simulation to real-time monitoring and predictive analytics.
Success Stories in Oil and Gas
Eni: Real-Time Optimization with Digital Twin and PI Asset Framework
Eni deployed a digital twin across its upstream operations by integrating the AVEVA PI System with its internal eDOF (Eni Digital Oil Field) platform. The digital twin enabled both simulation and real-time monitoring by connecting live field data with predictive models. This setup allowed Eni to continuously optimize operating parameters, proactively detect deviations from expected performance, and send timely recommendations to engineers for implementation in the field. One of the key benefits of this approach was the ability to identify untapped opportunities — including a 60% increase in oil production at a West Africa site. A critical enabler of this solution was PI Asset Framework (AF), which structured asset data by country and site and served as the foundation for model inputs and KPI monitoring. AF allowed Eni to efficiently manage simulation logic, automate trend building, and deliver insights through PI Vision dashboards. This seamless integration of PI AF and digital twin technology fostered real-time collaboration across teams, improved visibility into operations, and empowered engineers to make faster, data-driven decisions.AGC Chemicals: Process Digital Twin for Operational Optimization
AGC Chemicals integrated the AVEVA PI System with a process digital twin to enhance operations in their vinyl chloride monomer (VCM) plants. By combining first-principles models with statistical analysis, they achieved:- Enhanced process monitoring and visualization of previously unmonitored parameters.
- A preliminary study indicated a 6% improvement in fuel gas efficiency, translating to approximately $400,000 in annual energy cost savings.
Shell: Enhancing Offshore Operations with Digital Twin Technology
Shell has adopted digital twin technology to improve the operational efficiency and integrity of its offshore platforms. One notable application is the use of the Kognitwin® Energy platform, developed in collaboration with Kongsberg Digital. This solution enables Shell to create a real-time, virtual representation of its assets, combining 3D visualization, sensor data, and analytics in one integrated environment. Through this platform, Shell’s teams can monitor asset performance remotely, simulate operational scenarios, and optimize maintenance planning, all without interrupting live operations. This has enhanced decision-making, improved cross-disciplinary collaboration, and contributed to safer, more efficient offshore production.BP: Accelerating Optimization and Production with Digital Twins
BP began deploying its digital twin technology across production systems globally in 2017. The platform has helped engineers better monitor asset performance and proactively identify issues before they escalate, enabling more efficient maintenance planning and operational decision-making. One major improvement was in analysis speed: optimization tasks that previously took more than a day can now be completed in just 20 minutes. This acceleration has freed up valuable engineering resources and improved time-to-insight. In the first year alone, BP reported an additional 30,000 barrels of oil produced as a direct result of digital twin implementation. SCG Chemicals (Thailand): Boosting Efficiency and Collaboration with AVEVA SCG Chemicals developed a unified digital platform featuring a 3D virtual plant and an immersive, touch-based visualization tool powered by AVEVA™ Asset Information Management. This platform offers real-time operational visibility and rapid access to plant data across teams. As a result, SCG reduced information retrieval time to under 10 seconds, increased plant reliability to 100%, and achieved 9× ROI within just six months.Other Problems Digital Twins Could Solve
While digital twins are widely known for improving maintenance, safety, and production, their potential extends far beyond these areas. By providing a dynamic, real-time view of assets and operations, digital twins can help companies streamline complex processes, optimize resource usage, and train personnel more effectively. Here are additional challenges where digital twins are making a significant impact.
