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.

翻译：软传感器因其能够以低成本、低干预的方式提供关键过程变量的估计值而日益受到关注。在石油与天然气生产中，虚拟流量计（VFM）是一种常用的软传感器，旨在实时估算多相流流量。VFM基于模型实现，而这些模型需要校准。由于模型多样性及可用测量数据的差异，校准则高度依赖于具体应用场景。最精确的校准方式是通过对VFM参数进行精细调整以匹配单井测试数据，但这需要大量人工操作，且并非所有井均可获取频繁的单井测试数据。本文提出一种基于生产分离器测量数据的校准方法，并假设观察到的总流量应等于各单井流量之和。这一方法允许我们对所有井的VFM进行持续联合校准。该方法采用序贯蒙特卡洛（SMC）推断每口井的调谐因子与流量组分。我们在包含十口井的案例上分别使用合成数据与真实数据进行了测试。结果表明，该方法无需依赖单井测试即可合理估计参数，具有良好前景。然而，本文识别并讨论了若干挑战，特别是过程噪声以及如何管理变化的数据质量等问题。