Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

作     者：Hamzeh Ghorbani David A.Wood Abouzar Choubineh Afshin Tatar Pejman Ghazaeipour Abarghoyi Mohammad Madani Nima Mohamadian 

作者机构：Young Researchers and Elite ClubAhvaz BranchIslamic Azad UniversityAhvazIran DWA Energy LimitedLincolnUnited Kingdom Petroleum DepartmentPetroleum University of TechnologyAhwazIran Young Researchers and Elite ClubNorth Tehran BranchIslamic Azad UniversityTehranIran National Iranian South Oil Company(NISOC)AhvazIran Young Researchers and Elite ClubOmidiyeh BranchIslamic Azad UniversityOmidiyehIran 

出 版 物：《Petroleum》 (油气（英文）)

年 卷 期：2020年第6卷第4期

页      面：404-414页

核心收录：

学科分类：12[管理学] 0709[理学-地质学] 0819[工学-矿业工程] 0808[工学-电气工程] 08[工学] 0820[工学-石油与天然气工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0807[工学-动力工程及工程热物理] 0815[工学-水利工程] 0816[工学-测绘科学与技术] 0827[工学-核科学与技术] 0703[理学-化学] 0813[工学-建筑学] 0835[工学-软件工程] 0814[工学-土木工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Iranian South Oil Company 

主　　题：Orifice flow meters Flow-rate-predicting virtual meters Multiple machine-learning algorithm comparisons Metrics influencing oil flow Flow-rate prediction error analysis 

摘      要：Fluid-flow measurements of petroleum can be performed using a variety of equipment such as orifice meters and wellhead *** is useful to understand the relationship between flow rate through orifice meters(Qv)and the five fluid-flow influencing input variables:pressure(P),temperature(T),viscosity(μ),square root of differential pressure(ΔP^0.5),and oil specific gravity(SG).Here we evaluate these relationships using a range of machine-learning algorithms applied to orifice meter data from a pipeline flowing from the Cheshmeh Khosh Iranian oil *** coefficients indicate that(Qv)has weak to moderate positive correlations with T,P,andμ,a strong positive correlation with theΔP^0.5,and a weak negative correlation with oil specific *** order to predict the flow rate with reliable accuracy,five machine-learning algorithms are applied to a dataset of 1037 data records(830 used for algorithm training;207 used for testing)with the full input variable values for the data set *** algorithms evaluated are:Adaptive Neuro Fuzzy Inference System(ANFIS),Least Squares Support Vector Machine(LSSVM),Radial Basis Function(RBF),Multilayer Perceptron(MLP),and Gene expression programming(GEP).The prediction performance analysis reveals that all of the applied methods provide predictions at acceptable levels of *** MLP algorithm achieves the most accurate predictions of orifice meter flow rates for the dataset *** and RBF also achieve high levels of *** and LSSVM perform less well,particularly in the lower flow rate range(i.e.,40,000 stb/day).Some machine learning algorithms have the potential to overcome the limitations of idealized streamline analysis applying the Bernoulli equation when predicting flow rate across an orifice meter,particularly at low flow rates and in turbulent flow *** studies on additional datasets are required to confirm this.