Deep learning models for improved accuracy of a multiphase flowmeter

Measurement of oil and gas two-phase flow with variable flow regimes relies to a large extent on flow patterns and their transitions. Using multiphase flowmeters in flows with high gas volume fractions is therefore usually associated with large uncertainties. This work presents a dynamic neural network method to measure the flow rate using a nonlinear autoregressive network with exogenous inputs (NARX). Total temperature and total pressure are used as network inputs and the obtained results are compared with a multilayer perceptron (MLP). Comparison between modeling results and the experimental data shows that the NARX network can predict oil and gas flow with variable flow regimes with less error compared to the MLP model, e.g. an absolute average percentage deviation (AAPD) of 0.68% instead of 1.02%. The present work can hence be seen as a proof-of-concept study that should motivate further applications of deep learning models to facilitate enhanced accuracy in flow metering.