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Our FLUX VFM ensures a life-of-field valid virtual window into your production systems using live sensor data and self-adjusting technology. The solution provides real-time information, such as individual phase flow rates and true bottomhole pressure at unmatched accuracy.

FLUX VFM is a hybrid technology that combines physics-based, first-principles modeling with machine learning. The solution is self-adjusting and utilize available sensor and reference data to maintain the highest level of accuracy. Provided sufficient model data and good quality sensor data, FLUX VFM targets a maximum full-scale error of 5 to 10% in estimated flow rates [Article link]. In real-time deployment, FLUX VFM consumes sensor data and operational parameters and delivers real-time phase flow rates.

Real-time flow rates and true bottomhole pressure at unmatched accuracy

The primary output from FLUX VFM is flow rates. Additional insights such as virtual sensing offers data where sensors are not available or flow stability assessments are facilitated by the FLUX Simulator model. Access to a calibrated simulation model enables extensions to scenario simulations. In scenario mode, different operations can be assessed either to understand near-future production if operations are maintained (look-ahead), plan and optimize future operations or potential events (what-if), or retrospectively analyze past operations.

Sample of real-time production monitoring on wells

Accurate information about the flow in your production system enables better and faster well optimization decisions

With FLUX VFM you can optimize operations through active adjustment of production from your individual wells and are able to detect potential challenges before your production is affected.

CORE BENEFITS

  • Access to real-time flow rates
  • Accurate predictions through the life of the asset
  • Insights can be consumed on any surface, e.g. proprietary surveillance dashboards
  • Easy integration and secure connection to proprietary systems
  • Consumes information from existing sensors through data platform/historian
  • Low maintenance needs – human intervention practically zero
  • Fast to deploy
  • Scalable
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