A physics-informed neural network (PINN) was developed to solve the complex Navier-Stokes equations for multiphase flow measurement, including flow rates of individual phases in the multiphase mixture and phase ratios. The results were compared with a mathematics-based multiphase-flow-measurement approach and validated through a controlled loop test using oil, water, and gas. The PINN reduced reliance on predefined correlations and offered greater adaptability to changing fluid properties. The repeatability of results confirmed the model’s reliability, positioning PINNs as a promising alternative for complex multiphase-flow-measurement applications.
Result
To evaluate the performance of the proposed PINN for multiphase-flow-rate estimation, its predictions were compared with those from a mathematical-model-based multiphase flowmeter (MPFM) and the ground-truth values obtained from the high-temperature flow loop.
