Three-phase flow meters based on X-rays and artificial neural network to measure the flow compositions

Abstract

The methodology presented in this study is based on a 149.5 keV X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions in a three-phase system. Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. A mathematical algorithm based on an artificial neural network was used to correlate the energy spectra from both detectors with the fluids volume fractions. The pulse height distributions obtained by the detectors are used as input data of the network that outputs the volume fractions of gas and water. The mean relative error, using the procedure presented here, for all data, was below 2.5% for both phases investigated. These results show that the methodology based on an X-ray beam has the potential to be used with flow meters.