Using predictive analytics for the oil and gas digital twin in operations

In the oil and gas industry, many businesses undergoing digital transformation are adopting digital twins, virtual representations of a physical product, process or facility. Until recently, digital twins have mainly been used in individual lifecycle stages, limiting their effectiveness. Because oil and gas equipment and systems generate massive data volumes throughout their entire lifecycles, adding predictive capabilities to a digital twin can dramatically increase its insights. Download our white paper to explore the value of using predictive analytics for the oil and gas digital twin in operations.

What is the predictive digital twin?

A digital twin is based on engineering and operational data from a physical asset or system. With more data, the digital twin becomes better informed, and can provide greater insights and benefits. In situations where data is unavailable or insufficient, predictive engineering analytics can fill the data gap, creating a predictive digital twin that simulates the real-world behavior of the asset or system. Learn about opportunities to use predictive engineering analytics throughout oil and gas equipment’s entire operating life. A digital twin’s lifecycle mirrors an actual engineering product or system, providing performance insights from concept development through end-of-life.

Using simulation techniques for predictive engineering analytics

The predictive digital twin provides uniquely powerful benefits for the oil and gas industry. With simulation playing a critical role in predictive engineering analytics, our white paper highlights specific simulation techniques and how to combine them to generate a predictive digital twin for oil and gas operations. These techniques include high fidelity, system level and reduced-order model (ROM) simulation. Because of simulation, oil and gas engineers can more accurately predict real-world behaviors of equipment or systems where data is missing.

Predictive digital twin examples in the oil and gas industry

Read an industry example of how a predictive digital twin can help ensure the integrity of a heat exchanger. This occurs through high-fidelity finite element analysis (FEA) and computational fluid dynamics (CFD) forecasting flow distribution and heat transfer. It can take place because the digital twin uses predictive data beyond the available temperature data. You’ll also learn how engineers can use predictive data for flow assurance in subsea production. By using reduced-order models, it’s now possible to predict hydrate formation risks in real-time while achieving quick reactions to critical situations.