Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization

作     者：Choubineh, Abouzar Ghorbani, Hamzeh Wood, David A. Moosavi, Seyedeh Robab Khalafi, Elias Sadatshojaei, Erfan 

作者机构：Petr Univ Technol Petr Dept Ahvaz Iran Islamic Azad Univ Young Researchers Club Omidiyeh Branch Omidiyeh Iran DWA Energy Ltd Lincoln England Shiraz Univ Petr Dept Shiraz Iran 

出 版 物：《FUEL》 (燃料)

年 卷 期：2017年第207卷

页      面：547-560页

核心收录：

学科分类：0820[工学-石油与天然气工程] 080702[工学-热能工程] 0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：Liquid critical-flow rate Non-linear regression Artificial neural network Teaching-learning-based optimization Empirical wellhead coke flow rate published correlations Relevancy factor 

摘      要：Published relationships typically consider liquid critical-flow rate through wellhead chokes of producing oil wells as functions of wellhead pressure, choke size and gas-liquid ratio. Such correlations can be improved by taking into account three additional input variables: gas specific gravity, oil specific gravity and temperature. Novel liquid critical-flow rate models, hybridizing an artificial neural network (ANN) with a teaching-learning-based optimization (TLBO) algorithms, involving 3 and 6 input variables, demonstrate improved accuracy compared to nonlinear regression models, traditional ANN models and published correlations. The improved accuracy of the developed models is assessed statistically using a data set of 113 wellhead flow tests from oil wells in South Iran (with a full data listing included). The ANN-TLBO (6 parameters) developed model is the most accurate, yielding the best liquid critical-flow rate predictions for that data set: coefficient of determination of 0.981;root mean square error of 714;average relative error of 2.09%;and, average absolute relative error of 6.5%. The 6-parameters models outperform the 3-parameters models without over complicating model functionality. This justifies the consideration of all six input variables to deliver improved predictions of wellhead choke liquid critical-flow rates. Calculation of relevancy factors for the 6-parameters ANN-TLBO model to the data set for all six input variables reveals choke size and gas-liquid ratio have maximum and minimum influence in determining the liquid critical-flow rate, respectively. (C) 2017 Elsevier Ltd. All rights reserved.