Comparison of Backscattered and Transmitted Gamma Rays Spectra for Prediction of Volume Fraction of Three-Phase Flows Using Machine Learning Model

Abstract

Estimation of volume fraction percentage of the multiple phases flowing in pipes with limited access is a challenge in oil, gas, chemical processes, and petrochemical industries. In this research, the gamma backscattered spectra together with the machine learning model were used to predict precise volume fraction percentages in water-gasoil-air three-phase flows and solve the aforementioned challenge. The detection system includes a single energy ¹³⁷Cs source and a NaI(Tl) detector to measure the backscattered rays. The MCNPX code was used to simulate the setup and produce the required data for the artificial neural network. The volume fraction was calculated with mean relative error percentage 13.60% and the root mean square error 2.68, respectively. Then, the results were compared with the acquired results of transmitted gamma-ray spectra. The proposed design is a suitable, safe, and low-cost choice for industries.