The problem of how to accurately measure the flow rate of oil–gas–water mixtures in a pipeline remains one of the key challenges in the petroleum industry. This paper proposes a new methodology for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems using dual energy fan-beam gamma-ray attenuation technique and artificial neural networks. The novelty of this study in comparison with previous works, is using just 4 extracted features (photo peaks of 241Am and 137Cs in 2 detectors) from the gamma ray spectrums instead of using the whole gamma ray spectrum, which reduces the undesired noises and also improves the speed of recognition in real situations. Radial basis function was used for developing the neural network model in MATLAB software in order to classify the flow patterns (annular, stratified and homogenous) and predict the value of volume fractions. The ideal and static theoretical models for flow regimes have been developed using MCNP-X code. The proposed networks could correctly recognize all the three different flow regimes and also determine volume fractions with mean absolute error of less than 5.68% according to the recognized regime.
