A Gulf of Mexico oil field has six subsea producing wells that require production allocation. The wells are not equipped with multiphase flow meters and therefore, an indication of production through a virtual flow meter (VFM) is desired. The complexity is that in this field, well tests are performed as infrequently as semi-annually. The accuracy of the developed VFM is evaluated using well test data held out for validation. This paper talks about a near real-time cloud-based VFM solution. The approach is based on combining well measurements with fundamental flow equations through the tubing and choke. Well test data is used to calibrate these equations by tuning parameters such as the friction factor and choke flow coefficient. The models are auto-tuned based on the incoming and past well test data. Additionally, the models require a fluid properties table and wellbore deviation survey. When performing predictions, typical real-time measurements such as downhole and well head pressure/temperature and choke position are utilized. The approach was tested on a well in a nearby field which had more frequent well tests and then extended to the considered field, which had very few calibration data points (~2 well tests/year). The results were validated using left-out well tests and showed mean absolute percentage (MAPE) errors within 10%. Current industry practice for VFMs is to tune the model using the latest well test and use correction factors to calibrate frictional and gravity terms, which leads to predictions biased towards the latest well test. In addition to addressing these issues, this paper also outlines the auto-tuned near real-time implementation of the system. The utilization of hydrodynamic models as a basis also allow us to extrapolate beyond the range of the calibration dataset with good accuracy.