Phase Fractions and Velocities in Multiphase Flow – Estimation using Sensor Data Fusion and Machine Learning

Abstract

There is a strong interest in quantifying the amount of gas and its flow rate to facilitate better control of the processes involved in many industries. There are usually many sensors monitoring these processes, both intrusive and invasive, as well as non-invasive sensors which are usually clamped on to the process pipelines in which the multiphase flow occurs. In the multiphase flow rigs at Equinor and the University of South-Eastern Norway, experiments have been performed with different combinations and velocities of the phases and multiple sensors have been logged. The data from these sensors have been used to estimate volume fractions of the phases as well as their flow rates. This paper presents the estimated results of volume fractions and velocities of selected phases, obtained by fusing data from multiple sensors that monitor density, differential pressure, temperature, and acoustic emission using machine learning (ML) algorithms. These ML algorithms use neural networks with the non-linear input-output type with Levenberg-Marquardt training and provide estimates of volume fractions and phase velocities with RMSE values in the range of 4.6 to 16 m3/h, with the lowest RMSE for gas and the highest for multiphase flow. The total flow rate for the multiphase flow was in the range 30 to 120 m3/h. Results are compared with ML models using data from non-invasive sensors.