Hybrid Approach Predicts Accurate Flow Rates and Bottomhole Pressure

Accurate and continuous production and pressure measurements are crucial not only for field surveillance but also for effective production optimization. Virtual flowmeters (VFM) are an alternative to conventional physical meters and can exist in physics-based and machine‑learning-based forms. The algorithm proposed in the complete paper is a hybrid virtual flow and pressure meter (VFPM) that merges both types. The algorithm’s prediction reduces the time needed to generate data with physics-based VFM while increasing the accuracy of machine-learning-based VFM.

Background

VFMs involve physics models to predict production flow rates in each well using thermodynamics, fluid dynamics, fluid modeling, and optimization techniques.