The predictive digital twin in the oil and gas industry: technology use cases and examples - 10 de noviembre de 2021 - TecnoWebinars.com

 

The oil and gas industry is under constant pressure to improve operational efficiency. With digital technology adoption slower than other industries, the time is now for oil and gas businesses to adopt digital twins, virtual representations of a physical product, process or facility.

 

Join our November 10 webinar to learn how a digital twin helps your operations use engineering data to become better informed, improve decision-making and predict responses to future scenarios. In situations where your data is unavailable or insufficient, predictive engineering analytics can fill the data gap. This webinar shows you how predictive engineering analytics enhances oil and gas digital twins to provide operational excellence.

 

 

A digital twin is based on your engineering and operational data. With more data, the digital twin is better informed, helping it provide greater insights and benefits. If you lack data in a particular area, the digital twin relies on physics-based simulations to increase its reliability.

 

Physics-based simulations include many forms. These include high-fidelity predictive methods such as finite element analysis (FEA) to computational fluid dynamics (CFD) and reduced-order models (ROM) for systems simulations.

 

In this webinar, we demonstrate how predictive simulations can provide data:

 

    - In locations where sensor data is not available, such as subsea systems

    - In extreme operating conditions, including never-before-experienced scenarios

    - To safely and efficiently improve a plant or asset’s productivity beyond its expected lifespan

 

 

During the webinar, you’ll view a demonstration of an oil and gas digital twin helping ensure the integrity of a heat exchanger. This includes high-fidelity finite element analysis (FEA) and computational fluid dynamics (CFD) to forecast flow distribution and heat transfer. It can take place because the digital twin uses predictive data beyond the available temperature data.

 

 

You’ll also view a demonstration of predictive data for flow assurance in subsea production. Because hydrates can form inside subsea production trees, potentially impairing operation, subsea trees are typically designed with insulation to ensure thermal performance meets design criteria while minimizing risk.

 

Engineers can now use computational fluid dynamics (CFD) to design and validate subsea tree system performance. These are complex and time-consuming simulations, but essential to get the detailed system designs correct. The webinar demonstrates the usage of reduced-order models to predict hydrate formation risks in real-time while enabling quick reactions to critical situations.

 

 

Rikesh Mistry:Engineering Consultant, Norton Straw

 

Rikesh Mistry, Engineering Consultant at Norton Straw has experience in a wide array of industries including Oil and Gas, Food Processing, and Power. He has delivered insight and solutions to engineering problems through the use of commercial computational fluid dynamics and finite element analysis codes. He has a particular interest in workflow and methods development to create streamlined and accurate means for generating valuable data from simulation modeling which can be used to inform future design choices. Rikesh is currently working with multiple clients in their development and application of digital twins and is strongly interested in the relationship between data and engineering in the digital age.