Data collection is crucial in the Oil and Gas business. Reliable production and pressure measurements for oil and gas operations can be acquired from Multiphase Flow metering and bottom hole pressure metering. Accurate and continuous production and pressure measurements are crucial not only for field surveillance, but also for effective production optimization. Virtual Flow & Pressure Metering (VFPM) is an alternative to the conventional physical meters and can exist in two forms, physics-based VFPM and Machine learning-based VFPM. The physics-based VFPM relies on multiphase flow and pressure simulations including thermodynamics, fluid dynamics, fluid modeling, and optimization techniques. Machine learning VFPM however, uncovers the relationship between target variables and sensor data. Both methods have strengths but also have some limitations.

The proposed algorithm is the hybrid VFPM that merges both types, the physics-based VFPM and the Machine Learning VFPM. The approach first tests each VFPM method independently and checks for time consumed and accuracy. Then, it tests for the hybrid model option. In the hybrid model case, the framework starts with the basic well data going into the Physics-based Model to simulate the well behavior and create sensitivity analysis. The generated data is then fed to the Machine Learning model. The process then considers correlation between input features and removes highly correlated features. Data is split into train and test and then, the regression model is built using multiple model options to choose the most suitable model based on accuracy metrics and elapsed time. The best model is picked and the target variables are generated. The model here was decision tree-based Machine Learning Model as it represented the data best and needed few hyper-parameters to tune.

The effectiveness of the algorithm was tested and validated, achieving a high accuracy of 90% in predicting Multiphase Flow Rate and 98% accuracy in predicting bottomhole pressure, and a reduction of 45% in time consumed to generate the data.

The algorithm's prediction reduces the time needed to generate data using solely physics-based VFPM while increasing the accuracy of the solely Machine Learning-based VFPM. In addition, this approach can translate into a huge cost saving since it eliminates the need for a physical meter in each well.

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