Analysis of multiphase flows using dual-energy gamma densitometry and neural networks
Analysis of multiphase flows using dual-energy gamma densitometry and neural networks
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DOI：
10.1016/0168-9002(93)90728-z
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发表时间：
1993-04
影响因子：
1.4
通讯作者：
C. Bishop;G. James
中科院分区：
文献类型：
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作者：
C. Bishop;G. James
双能伽马密度计为多相流的非侵入式分析提供了一种强大的技术。通过采用多束线，可以获得有关相位配置的信息。一旦配置已知，原则上就可以确定相分数。然而，在实践中，由于可能出现各种各样的相配置以及对多相流进行建模的相当大的困难，从密度计数据中提取相分数变得复杂。在本文中，我们表明神经网络技术提供了一种强大的方法来分析双能伽马密度计的数据，允许高精度地确定相配置和相分数，同时避免与建模相关的不确定性。该技术非常适合多相石油管道中油、水和气体组分的测定。比较了线性和非线性网络模型的结果，并描述了一种验证网络输出的新技术。
Dual-energy gamma densitometry offers a powerful technique for the non-intrusive analysis of multiphase flows. By employing multiple beam lines, information on the phase configuration can be obtained. Once the configuration is known, it then becomes possible in principle to determine the phase fractions. In practice, however, the extraction of the phase fractions from the densitometer data is complicated by the wide variety of phase configurations which can arise, and by the considerable difficulties of modelling multiphase flows. In this paper we show that neural network techniques provide a powerful approach to the analysis of data from dual-energy gamma densitometers, allowing both the phase configuration and the phase fractions to be determined with high accuracy, whilst avoiding the uncertainties associated with modelling. The technique is well suited to the determination of oil, water and gas fractions in multiphase oil pipelines. Results from linear and non-linear network models are compared, and a new technique for validating the network output is described.