The self-adjusted Virtual Flow Meter (VFM)
The Turbulent Flux software applies a self-adjusting hybrid solution, which combines first-principles physics with machine learning, increases reliability and reduces the need for human intervention. The self-adjustment methodology utilizes additional sensors to self-adjust and maintain accuracy, reference well rate measurements are no longer primarily for calibration, but rather a means for validation. A self-adjusted VFM will give accurate results at any time compared with legacy VFMs. VFM solutions had unresolved challenges, particularly in terms of maintenance and reliability by infrequent manual adjustments that is now adressed with self-adjustment.
In that, the self-adjusted VFMs are well suited to be deployed at scale on new and exiting assets for flow metering or side-by-side with multiphase flow meters (MPFM) in case of failure.
A user example are Turbulent Flux VFMs for wells in shale plays where production conditions are in rapid changes and self-adjusting capabilities are a requisite to predict accurate flow rates.
Beyond VFM
By incorporating a physics-based simulator at the core of a VFM, the potential reach of the solution extends far beyond real-time well rates. Access to the underlying simulation model facilitates additional workflows on top, such as operations support and production optimization is available in the FLUX Applied solution.
One example is the possibility to run what-if scenarios to investigate how changes in operational setpoints impact operations ahead of their implementation. This in turn can facilitate optimization to, e.g., optimize use of gas lift and maximize utilization of limited resources.
Another use-case is to gain insights into flow assurance challenges such as the onset of water breakthrough, impending liquid loading, risk of solids deposition, etc. When preconfigured, automated, and combined with alerts, this caters for operational excellence.
The VFM history
Virtual Flow Metering in real time has been developed, tested, and deployed for decades. Various solutions have attracted an increased interest with increasing access to sensor data, and the capabilities have even further increased with the entry of machine learning. However, there are still unresolved challenges, particularly in terms of maintenance and reliability. Whether based on first-principles physics or machine learning algorithms, regular recalibration/retraining is required, which often requires manual intervention.
A VFM can deliver continuous well rates based on data from existing sensors. This can act as a supplement to well testing and offer continuous insights in between well tests. VFM combined with intermittent well testing can replace the need for continuous well flow measurements with maintained understanding of the well performance. For assets already employing intermittent well testing, a reliable VFM solutions can extend the time in between well tests, and by that reduce both operational expenditure and deferred production.
