Determination of Gas and Water Volume Fraction in Oil Water Gas Pipe Flow Using Neural Networks Based on Dual Modality Densitometry

• Published in: Advances in Neural Networks - ISNN 2006 pp. 1248 - 1253
• Main Authors: Jing, Chunguo, Xing, Guangzhong, Liu, Bin, Bai, Qiuguo
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Hide Layer; Mean Square Error; Radial Basis Function; Radial Basis Function Neural Network; Scattered Radiation
• ISBN: 9783540344827; 3540344829
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11760191_182

The models of dual modality densitometry were developed. It can be used for measuring the gas volume fraction and water volume fraction in oil water gas pipe flow. The models are complex. In order to solve models, it often uses simplified models. This reduces measurement precision. The method of measuring gas and water volume fraction using neural networks was presented. The simulation data was gotten using Geant4. The radial basis function networks were trained and tested on computer simulation data. The results show that networks predicted gas volume fraction fit true gas fraction well and water volume fraction has some deviations.