Abstract
In this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10%—were simulated by MCNP-X code. After simulating all the states and collecting the signals, the Fast Fourier Transform (FFT) was used to convert the data to the frequency domain. The first and second dominant frequency amplitudes were extracted to be used as the inputs of neural networks. Three Radial Basis Function Neural Networks (RBFNN) were trained for determining the type of flow regimes and predicting gas and water volume fractions. The correct detection of all flow regimes and the determination of volume percentages with a Mean Relative Error (MRE) of less than 2.02% shows that the use of frequency characteristics in determining these important parameters can be very effective. Although X-ray radiation-based two-phase flowmeters have a lot of advantages over the radioisotope-based ones, they suffer from lower measurement accuracy. One reason might be that the X-ray multi-energy spectrum recorded in the detector has been analyzed in a simple way. It is worth mentioning that the X-ray sources generate multi-energy photons despite radioisotopes that generate single energy photons, therefore data analyzing of radioisotope sources would be easier than X-ray ones. As mentioned, one of the problems researchers have encountered is the lower measurement accuracy of the X-ray, radiation-based three-phase flowmeters. The aim of the present work is to resolve this problem by improving the precision of the X-ray, radiation-based three-phase flowmeter using artificial neural network (ANN) and feature extraction techniques.
Highlights
One of the most vital challenges in the oil, gas, and petrochemical industry is determining the volume percentages and diagnosing the type of flow regimes passing through the oil transmission lines
The purpose of this study is to use X-ray tubes, feature extraction techniques in the frequency domain, and the RBF neural network to increase the accuracy in determining critical parameters such as volume fractions and the type of flow regimes in oil–gas–water three-phase flows
Three RBF neural networks have been designed to determine the type of flow regimes and detect volume percentages
Summary
One of the most vital challenges in the oil, gas, and petrochemical industry is determining the volume percentages and diagnosing the type of flow regimes passing through the oil transmission lines. Two radioisotope sources and three NaI detectors were used to determine the volume percentages and the type of flow regimes. An artificial neural network was designed to detect the type of flow regimes. Researchers proposed a new structure for measuring volume percentages in a stratified regime. This structure used an NaI detector next to a cesium-137 source to record the backscattered gamma radiation’s energy spectrum. They used the MLP neural network to determine volume percentages with an error of less than 6.47% [2]
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