Simultaneous Measuring Methods for Gas-Liquid Phase Flow Rates Using Wevelet Analysis

本研究课题结合海上油田开发的需要，旨在开发一种新的气液两相流流量测量方法。空气和水的混合物向上流过一根装有楔子的管子。当流动为单相流时，可利用楔形体上下游压差的频率来测量流量。但在气液两相流的条件下，频率变化较大，波形也是如此。但是，压力波动的随机特征，例如平均压差、波形的概率密度分布的标准偏差和偏斜度以及傅立叶分量，可以随着各个相流率的变化而特别地改变，如通过语音识别技术中的方法所预测的。因此，使用这些变量来测量两相流在这个项目中的模式匹配方法和神经网络来识别单个流量进行了研究。利用在不同相流率条件下测得的压力波形对随机特征变量进行了分析。并利用这些变强度来识别水的体积流量，得到的流量测量精度在3%以内。为了建立该方法，需要进一步调查，这种对空气相流量和S/N比的适用性，然后继续

Relevant to the development of off-shore oil field, this research project have been started with the intention of developing a new method for measuring individual phase flow rates of gas-liquid mixtures. The air-water mixture flows upwards through a pipe, in which a wedge is put. The frequency of the differential pressure between upstream and downstream of the wedge is available to measure the flow rates when the flow is a single-phase flow. The frequency, however, is much changed under the conditions of gas-liquid flows as well as the waveform. But the stochastic features of the pressure fluctuations, such as the averaged differential pressure, standard deviation and skewness for the probability density distributions of waveforms, and Fourier components, may alter peculiarly with a change in individual phase flow rates, as predicted by the method in a voice recognition technique. Thus, the usage of these variables to measure the two-phase flows is investigated in this project with a pattern matching methods and neural network to identify the individual flow rates. Stochastic feature variables were analyzed using the pressure waveforms measured under the various conditions of phase flow rates. And, the volumetric flow rate of water are identified by using these variable intensity, the resultant flow rate are found to be measured within 3% accuracy. To establish the method, further investigation, such applicability for air-phase flow rates and S/N ratio are necessary, and then is continued