Physics-Informed Machine Learning for Radiation-Free Water Liquid Ratio Estimation Using Coriolis Flow Measurements
This study introduces a radiation-free, physics-informed machine learning framework that accurately estimates the Water Liquid Ratio in three-phase oil–gas–water flows using only Coriolis mass flow measurements and a novel Harmonic Mixture Density Mean Factor (HMDMF), achieving high predictive performance (R² = 0.8943) and offering a cost-effective alternative to traditional gamma-based systems.