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 

机构地区：[1]Young Researchers and Elite Club,Ahvaz Branch,Islamic Azad University,Ahvaz,Iran [2]DWA Energy Limited,Lincoln,United Kingdom [3]Petroleum Department,Petroleum University of Technology,Ahwaz,Iran [4]Young Researchers and Elite Club,North Tehran Branch,Islamic Azad University,Tehran,Iran [5]National Iranian South Oil Company(NISOC),Ahvaz,Iran [6]Young Researchers and Elite Club,Omidiyeh Branch,Islamic Azad University,Omidiyeh,Iran

出　　处：《Petroleum》2020年第4期404-414,共11页油气（英文）

摘　　要：Fluid-flow measurements of petroleum can be performed using a variety of equipment such as orifice meters and wellhead chokes.It 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 field.Correlation 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 gravity.In 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 provided.The 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 accuracy.The MLP algorithm achieves the most accurate predictions of orifice meter flow rates for the dataset studied.GEP and RBF also achieve high levels of accuracy.ANFIS 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 conditions.Further studies on additional datasets are required to confirm this.

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

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]