Study On The Metering Technology Of Wet Gas Based On Slotted Orifice

Wet gas metering is a subset of multiphase flow metering, and it is one of the major unsolved problems for oil and gas industry. In this thesis it is simplified to the metering of gas-liquid two-phase flow with low liquid fractions. Based on a self developed prototype of wet gas meter and experimental data set, a series of works have been carried out. The main target of this thesis is to develop a fast and efficient data processing method, and to improve the performance of wet gas meter, such as expending the metering range, high accuracy, fast response and so on.This thesis consists of two parts. The first part is mainly focus on the metering characteristics of a single slotted orifice, which included the two-phase multiplier analysis based on the mean value of differential pressure, and the dynamic feature extraction based on fluctuation of the differential pressure. The second part is mainly focus on the metering algorithm development, which included the phase fraction measurement based on correlation analysis, flow regime identification and flow rate measurement based on neural networks.Based on the mean value of differential pressure, detail analysis about how two-phase multipliers changing with X,Frg andγwas made for slotted orifices with different beta ratios. A new correlation for slotted orifice was put forward based on the modification to the Murdock correlation. The proposed correlation shows a more accurate calculation results compared with the existing correlations for standard orifice plates as the effect ofγhas been considered.Based on the recognition that the differential pressure is nonlinear, non-stationary and non-Gaussian, Hilbert-Huang transform and high-order statistics were employed to analyze the differential pressure. A group of features such as the energy and fraction of IMFs, entropy of IMF, HHT and bispectrum were extracted and qualitative analysis shows that the features are sensitive to the variation of flow parameters. The results provide the basis for the building of soft-sensing models for flow parameters measurement.Based on the dual slotted orifices of wet gas meter, the upstream and downstream differential pressures were processed through wavelet analysis and empirical mode decomposition approaches. Correlation velocities were obtained based on the corresponding components of a, d and IMFs, and the relationship between correlation velocities and X, phase fraction was investigated. The results show that the relationship between X, phase fraction and correlation velocities calculated by the above signal components are more stable than those by the original differential pressures.A novel feature selection approach called mRMR+BP was put forward and used to preprocess the original features together with the principal component analysis method. Flow regimes and gas/liquid individual phase flow rates were taken as outputs of the BP network, and those selected features as inputs, then three sub networks were built. Soft-sensing models for flow regimes identification and flow rates measurement were built based on neural networks ensemble. The results show that the neural network ensemble based model is more accurate than any sub network, and the accuracy rate of flow regimes identification is above 93%, and the mean relative errors are below 5% and 15% for gas and liquid flow rates respectively.