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.

这篇论文介绍了一种基于虚拟流量计（VFM）的连续校准方法，使用生产分离器提供的测量和假设观测流等于每个个别井的流量总和，应用时序 Monte Carlo (SMC) 推断调谐系数以及每个井的流量组成。通过使用合成数据和实际数据进行测试，结果表明该方法能够在无需依赖井试数据情况下提供合理的参数估计。