What Is a Digital Twin?

A digital twin is a dynamic virtual model of a physical asset, system, or process. Digital twin technology is used by asset-intensive industries to digitally replicate physical assets, run simulations based on historical data, and receive insights into future performance that will drive decisions about maintaining the real-life assets. These virtual replications of assets enable asset owners to prevent problems before they occur, identify opportunities for improvement, and plan for the future. In short, digital twins create a digital environment that informs asset strategies.

In the oil and gas space, digital twins empower owner operators to make better decisions regarding asset reliability. For example, they can be used to simulate changes to operating processes or capital investments before applying the changes in the real world.

A digital twin is a key part of the reliability-based digital transformation movement in which industrial leaders are digitizing existing data collection, analysis, and modeling processes. This digital conversion helps owner operators efficiently leverage facility data for more sustainable and profitable operations.

Pinnacle refers to the reliability digital transformation movement as Data-Driven Reliability.

Data-Driven Reliability and Digital Twins

Data-Driven Reliability is an approach to reliability stemming from reliability-based digital transformation practices and advancements in data collection, modeling, and analytics. Data-Driven Reliability leverages advanced technology, data science, and reliability modeling to provide a truly optimized reliability program. By applying Data-Driven Reliability, an owner operator will achieve maximized production, minimized process safety risk, and optimized lifecycle spend.

Data-Driven Reliability is comprised of four primary steps: data collection, reliability data contextualization & organization, reliability intelligence, and strategic decisions.

Data Collection

In this stage, appropriate process, operations, inspection, and maintenance data is collected through a combination of human, robotic, and Internet of Things (IoT) mediums.

Reliability Data Contextualization & Organization

Data is then digitized and integrated between systems. During this stage, a live reliability digital twin is created or updated.

Reliability Intelligence

After the data has been collected and contextualized, the data is fed into intelligent models that bring together Risk-Based Inspection (RBI), Reliability Centered Maintenance (RCM), Reliability Availability Maintainability (RAM), Condition Monitoring, and Multi-Variate Machine Learning. This approach is called Quantitative Reliability Optimization (QRO).

Strategic Decisions

In the final step, action plans—based on the data analysis—will be applied to the process and operational strategy, capital deployment, preventative and predictive maintenance execution, and turnaround planning.

Data-Driven Reliability is powered by Newton™, a platform that facilitates the QRO methodology to intelligently model how every data point, task, and potential change impacts future performance. With Newton™, asset owners can identify how to best allocate limited resources to yield the greatest return on reliability investment, achieving a strategic balance across availability, cost, and risk.

How Does a Live Reliability Digital Twin Work?

Digital twins rely on data to create a virtual model of a physical asset. Therefore, smart components and sensors are placed on an asset to gather data about the asset’s performance. This data is then fed into a processing system and applied to the digital replica. In addition to real-time asset data, historical data is also fed into the system to provide a richer context for the analysis. Once the necessary data is collected, the digital twin uses machine learning algorithms to run simulations and predict outcomes.

By integrating live operational data from sensors placed on the assets with historical recorded data, the digital twin provides a real-time, data rich interface. The digital twin integrates all industrial data, including time series, process diagrams, 3D models, event histories, asset models, unstructured documents, and more. With the reliability digital twin, owner operators can connect to physics simulators, deploy physics guided machine learning, view intuitive data visualizations, and carry out data-driven actions to improve reliability.

Benefits of a Live Reliability Digital Twin

A live reliability digital twin delivers real-time insights and accurate forecasts to help owner operators make data-driven decisions with confidence. By running advanced simulations, asset owners can test changes, predict future outcomes, implement production optimization strategies, detect impending maintenance issues, and develop faster responses to adverse events. Equipped with the data supplied by a live reliability digital twin, asset owners are empowered to increase safety, enhance asset performance, reduce downtime, and optimize costs.

Simulate changes, see the impact in near real-time, and discover how to achieve the greatest return on your reliability investment. Learn more about how Newton™ is powering the next evolution in digital twin technology.

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