The energy market is transforming, pivoting towards a low-carbon economy with a focus on increased automation and improved productivity and pursuing new energy sources and to meet ESG requirements. 

By creating a digital replica (a digital twin) of operational sites, energy companies gain access to a simulated real-time environment that can be used for many purposes, including:

• Fine-tuning operational efficiency 
• Minimal-risk training scenarios
• Reducing maintenance costs 
• Provide insights into operational disruptions 
• Enabling more intelligent decision-making
• Increasing profit

Operational sites in the energy sector already make use of cutting edge technology such as Internet of Things (IoT) sensors and real-time 3D modeling. However, these systems often operate in isolation, making it difficult to get a true picture of the overall status of an operations site at any given time.

Benefits of digital twins

With digital twin technology, data silos can be connected, enabling energy companies to make better decisions in real-time, increase production, improve maintenance and address employee safety.

Digital twins are driving the energy sector’s transition. Watch this video to get an overview of how to get started.

The components required to build a digital twin for an operations site vary depending on the type of equipment and the complexity of the operation.

Generally, the components can be broken down into three main categories: 

• Data pipelines, including sensor data
• Predictive analytics
• Visualization tools 

Data pipelines are used to collect and transfer data from multiple sources (sensors, usage patterns etc.) for analysis. Sensors gather accurate readings from the environment so that energy providers can gain insight into how their networks are functioning. 

AI-driven analytics platforms use this data to generate insights about operations, including identifying potential issues before they occur. 

Finally, visualization tools allow companies to depict their digital twins in an easy-to-understand format with contextual data and enable intuitive interaction with the model. This allows teams to quickly identify any areas of concern or potential opportunities for improvement

Digital twins in the energy sector are created by either importing conceptual models like BIM, CAD, and GIS, or scanning existing entities in the real world like manufactured products and facilities.

You should ensure you have data available for all the components that can be incorporated into the final model. This could include:

• Sensor information
• Machine design specifications
• Engineering information
• Performance statistics 
• Production processes
• Geospatial data
• Environmental conditions
• Market demand models or business information data
• Enterprise Resource Planning (ERP) data
• Asset Lifecycle Information Management (ALIM) integration 
• Process Intelligence Vision data (PI Vision)
• Piping and Instrumentation diagrams (P&ID)
• Process Flow Diagrams (PFD)
• Electronic Control of Work (eCOW) 

A digital twin enables interoperability and interconnection of these disparate data sources.

The next step is to develop a platform that brings together all the relevant data at the right time for accurate contextual representation. 

The priority is to efficiently ingest the essential data components that make a true digital twin possible. This requires data transformation tools that optimize existing data for interactive visualization (such as converting CAD models to mesh data). 

Once your existing data is prepared for visualization, use a real-time 3D platform to build and develop the digital twin. This enables physical and virtual data, as well as real-time conditions like weather forecasts, to be combined. The model should be able to simulate various scenarios and predict how the system will behave under different conditions, as well as provide real-time data on the site at any given time. 

The real-time 3D platform is connected to a virtual replica of the physical operation site, which allows the interactive experience to include visual references of the space. This creates an intuitive interface for operators to visualize, access, and monitor the data provided by the platform.

The choice of delivery platform for your digital twin will be influenced by your end users. For example, field workers may benefit from access via a mobile phone or tablet, and designers or engineers may find a mixed reality application more effective. The delivery devices will also impact which technology supplier or solution you choose for creating your digital twin.

Digital twins built with Unity’s real-time 3D technology can be deployed to more than 17 platforms. This approach enables workers to monitor and receive alerts on multiple devices across platforms including iOS, Android, PC, Mac, virtual reality (VR), and augmented reality (AR).

You can apply predictive algorithms to optimize the design of operational sites, maximize efficiency, minimize costs, or recommend decisions in complex scenarios.

Advanced analytics tools provide insights into the performance of the digital twin. AI algorithms can analyze the vast amounts of data collected by the system and generate predictions about future performance. These analytics can help identify areas where improvements are needed, suggest alternative strategies, or provide recommendations on how best to manage different scenarios. You can see a sample maintenance recommendation process in this digital twin demo.

Digital twins have varying levels of maturity and sophistication. The maturity level is determined by the complexity of the digital twin, the degree of integration with data sources, and the number of use cases it is deployed for. Building a connected foundation on a real-time 3D platform enables you to develop the maturity level of your digital twin according to your business requirements