Shell Showcases Real-Time Digital Twin Breakthroughs
Shell Showcases Real-Time Digital Twin Breakthroughs
Synopsis
- World Oil reports Shell presented real-time digital twin advancements at the Tomorrow Show in Oslo.
- Shell’s Mun Hon Chow detailed how dynamic simulations optimize one of Norway’s largest offshore gas fields.
- The technology helps engineers run scenarios, avoid shutdowns, and enhance subsea compression.
- Benefits include greater efficiency, predictive modeling, and potential AI-driven risk detection.
- Digital twins are positioned as vital for balancing production growth with emissions reduction.
5 mins Read
World Oil reports on Shell’s presentation at the Tomorrow Show in Oslo, where Mun Hon Chow, Flow Assurance Lead, outlined how real-time dynamic digital twins are reshaping offshore field management and driving production efficiency.
In his talk, “The Future of Field Management: Real-time Dynamic Digital Twins Drive Production Optimization,” Chow explained how Shell applied dynamic simulation technology on one of Norway’s largest offshore gas fields. Since operations began in 2007, the field has relied on two 120-km multiphase export pipelines and more recently subsea gas compression systems, with recovery targets rising from 75% to 85%.
Shell’s adoption of digital twins—combining live data, visualization, and modeling—allows engineers to replicate field conditions virtually. This enables “what-if” testing, benchmarking against live data, and identifying issues before they disrupt production.
During commissioning and operations, Shell avoided full shutdowns, improved subsea compression performance, and reduced hydrate risks during pipeline reconfigurations. “The ability to test operational modes virtually ensures production continuity while minimizing risk,” Chow said.
Looking ahead, Shell views digital twins as a long-term platform for operational excellence. Real-time data paired with predictive modeling supports anomaly detection, surveillance prioritization, and equipment optimization. Future upgrades may integrate AI to automatically flag critical risks and propose corrective actions.
“We are only scratching the surface,” Chow noted. “With the right data, in the right place, at the right time, digital twins allow faster, smarter decisions that enhance reliability and performance.”
As the industry works to grow production while cutting emissions, Shell believes digital twin technology will become central to next-generation field management.
Source Here – Have a Story? Address it to the Editor and submit it here
About Shell
Shell is one of the world’s largest integrated energy companies, operating across more than 70 countries with a workforce of over 90,000 employees. Headquartered in London, the company is involved in every stage of the energy value chain, from oil and gas exploration and production to refining, distribution, and petrochemicals manufacturing.
Shell is a leading supplier of liquefied natural gas (LNG) and operates extensive upstream and downstream businesses, including chemicals, lubricants, and fuel retail. In recent years, the company has expanded into renewable energy, hydrogen, and electric vehicle charging, as part of its long-term energy transition strategy. Shell also invests heavily in digital transformation, using technologies such as real-time digital twins, artificial intelligence, and predictive modeling to optimize operations, reduce risks, and enhance sustainability. Balancing energy demand with emissions reduction, Shell continues to position itself at the forefront of the global shift toward cleaner, more efficient energy systems.
Featured image Source: Manufacturing.Net
Disclaimer
The information provided in this article is for general informational purposes only and from publicly available sources. While we strive for accuracy, we do not make any representations or warranties, express or implied, regarding the completeness, reliability, or validity of the content. This article does not make any direct claims about specific companies, individuals, or organizations. Any references to reports or external sources are for context and do not imply endorsement or verification of any specific allegations. Readers are encouraged to conduct their own research and seek professional advice before making business decisions. We disclaim any liability for any losses or damages incurred as a result of reliance on the information provided.