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Estimation of flow rates of individual phases in an oil-gas-water multiphase flow system using neural network approach and pressure signal analysis

Bahrami, B ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1016/j.flowmeasinst.2019.01.018
  3. Publisher: Elsevier Ltd , 2019
  4. Abstract:
  5. Up until now, different methods, including; flow pressure signal, ultrasonic, gamma-ray and combination of them with the neural network approach have been proposed for multiphase flow measurement. More sophisticated techniques such as ultrasonic waves and electricity, as well as high-cost procedures such as gamma waves gradually, can be replaced by simple methods. In this research, only flow parameters such as temperature, viscosity, pressure signals, standard deviation and coefficients of kurtosis and skewness are used as inputs of an artificial neural network to determine the three phase flow rates. The model is validated by the field data which were obtained from separators of two oil fields and 6 wells over ten-month with 8 h interval (totally 5400 sets of data). A linear relation can be observed between the actual data and the predictions which were obtained from separators and neural network approach, respectively. Furthermore, it is shown that using feed forward neural network with Levenberg–Marquardt algorithm which has two hidden layers is sufficient to determine the flow rates. Also, it is tried to see the effect of flow regimes on the results of neural network approach by determining kurtosis and skewness coefficients for different flow regimes in a horizontal pipeline
  6. Keywords:
  7. Artificial neural network ; Flow rate ; Multiphase flow ; Pressure signals analysis ; Gamma rays ; Higher order statistics ; Neural networks ; Oil fields ; Separators ; Signal analysis ; Flow parameters ; Horizontal pipelines ; Kurtosis and skewness ; Marquardt algorithm ; Multi-phase flow systems ; Pressure signal ; Standard deviation ; Three-phase flow
  8. Source: Flow Measurement and Instrumentation ; Volume 66 , 2019 , Pages 28-36 ; 09555986 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0955598618300086