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On-Demand Webinar: Hybrid Modeling in Oil & Gas

How Physics and Machine Learning Combined Provide Unmatched Capabilities of Real-time Flow Simulations

Oil & gas operators are continuously aiming to achieve optimized production throughout the lifetime of their producing assets. Applying machine learning on real-life data and simulations from physics models can allow for improved agility, accuracy and robustness to flow simulation solutions in the market.


  • Physics-based and data-driven modeling approaches
  • Hybrid modeling philosophy – combining the best of the two worlds
  • Hybrid modeling applied – use cases
    • Maximize production and avoid operational upsets due to slugging
      Approach – Physics to produce data
    • Improve production insights for mature fields with high water rates
      Approach – Transfer learning to support physics
    • Monitor production to reduce well testing frequency
      Approach – Apply Physics to improve ML model, then complement the physics model


Johan Henriksson
Johan Henriksson, Product Portfolio Manager
Zongchang Yang, Senior Data Scientist
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