  Soft-sensors are gaining popularity due to their ability to provide estimates
of key process variables with little intervention required on the asset and at
a low cost. In oil and gas production, virtual flow metering (VFM) is a popular
soft-sensor that attempts to estimate multiphase flow rates in real time. VFMs
are based on models, and these models require calibration. The calibration is
highly dependent on the application, both due to the great diversity of the
models, and in the available measurements. The most accurate calibration is
achieved by careful tuning of the VFM parameters to well tests, but this can be
work intensive, and not all wells have frequent well test data available. This
paper presents a calibration method based on the measurement provided by the
production separator, and the assumption that the observed flow should be equal
to the sum of flow rates from each individual well. This allows us to jointly
calibrate the VFMs continuously. The method applies Sequential Monte Carlo
(SMC) to infer a tuning factor and the flow composition for each well. The
method is tested on a case with ten wells, using both synthetic and real data.
The results are promising and the method is able to provide reasonable
estimates of the parameters without relying on well tests. However, some
challenges are identified and discussed, particularly related to the process
noise and how to manage varying data quality.
