The lifecycle of a digital twin parallels that of an actual engineering product or system, offering performance insights from concept development to end-of-life.
FREMONT, CA: Many organizations undergoing digital transformation in the oil and gas industry are implementing digital twins, virtual copies of a physical product, process, or facility. Until recently, digital twins mainly were used in separate stages of the lifecycle, which limited their effectiveness. Because oil and gas equipment and systems create vast amounts of data throughout their lives, adding predictive capabilities to a digital twin can significantly boost its insights.
Engineering and operational data from a real asset or system are used to create a digital twin. With more data, the digital twin gets more knowledgeable and may deliver additional insights and benefits. Predictive engineering analytics can cover the data gap in cases where data is unavailable or insufficient, generating a predictive digital twin that simulates the real-world behavior of the asset or system. There is an opportunity to employ predictive engineering analytics throughout the life of oil and gas equipment.
Predictive engineering analytics using simulation techniques
The predictive digital twin offers the oil and gas business unrivaled advantages. Given the importance of simulation in predictive engineering analytics, our white paper focuses on specific simulation approaches and how to integrate them to create a predictive digital twin for oil and gas operations. Among these strategies are high fidelity, system-level, and reduced-order model (ROM) simulation. Thanks to simulation, oil and gas engineers can more precisely predict real-world behaviors of equipment or systems where data is lacking.
Predictive digital twins in the oil and gas sector
A predictive digital twin can assist protect the integrity of a heat exchanger in the industry. High-fidelity finite element analysis (FEA) and computational fluid dynamics (CFD) are used to anticipate flow distribution and heat transport. It is possible because the digital twin employs predictive data and the available temperature data. You'll also discover how engineers may use predictive data to ensure flow in subsea production. It is now possible to estimate hydrate formation risks in real-time while responding quickly to crucial situations using reduced-order models.