CNN-Based Volume Flow Rate Prediction of Oil–Gas–Water Three-Phase Intermittent Flow from Multiple Sensors

In this paper, we propose a deep-learning-based method using a convolutional neural network (CNN) to predict the volume flow rates of individual phases in the oil–gas–water three-phase intermittent flow simultaneously by analyzing the measurement data from multiple sensors, including a temperature s...

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Detalles Bibliográficos
Autores principales: Li, Jinku, Hu, Delin, Chen, Wei, Li, Yi, Zhang, Maomao, Peng, Lihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916361/
https://www.ncbi.nlm.nih.gov/pubmed/33578690
http://dx.doi.org/10.3390/s21041245

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916361/
https://www.ncbi.nlm.nih.gov/pubmed/33578690
http://dx.doi.org/10.3390/s21041245

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