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Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well

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Petroleum Research 2021年第3期6卷 271-282页

作者： Omid Hazbeh Saeed Khezerloo-ye Aghdam Hamzeh Ghorbani nima mohamadian Mehdi Ahmadi Alvar Jamshid Moghadasi Faculty of Earth Sciences Shahid Chamran UniversityAhwazIran Department of Petroleum Engineering Amirkabir University of TechnologyTehranIran Young Researchers and Elite Club Ahvaz BranchIslamic Azad UniversityAhvazIran Young Researchers and Elite Club Omidiyeh BranchIslamic Azad UniversityOmidiyehIran Faculty of Engineering Department of Computer EngineeringShahid Chamran UniversityAhwazIran Petroleum Engineering Department Petroleum Industry UniversityAhvazIran

Oil and gas reservoirs are of the main assets of countries possessing *** from these reservoirs is one of the main concerns of engineers,which can be achieved by drilling oil and gas *** of hydrocarbon wells is one of the most expensive operations in the oil *** of the most important parameters affecting drilling cost is the rate of penetration(ROP).This paper predicts the rate of penetration using artificial intelligence and hybrid models on Kaboud oil field well#7 in the directional *** this study,different models were constructed through various approaches based on 1878 dataset obtained from drilling operation in the well#***,the accuracy of the constructed models was compared with each *** was found that the MLP-ABC algorithm predicts the rate of penetration more accurately,by far,as compared with other *** MLP-ABC algorithm achieves impressive ROP prediction accuracy(RMSE=0.007211 m/h;AAPD=0.1871%;R^(2)=1.000 for the testing subset).Consequently,it can be concluded that this method is applicable to predict the drilling rate in that oilfield.

关键词： Rate of penetration Artificial intelligence Directional drilling MLP Prediction

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

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Petroleum 2020年第4期6卷 404-414页

作者： Hamzeh Ghorbani David A.Wood Abouzar Choubineh Afshin Tatar Pejman Ghazaeipour Abarghoyi Mohammad Madani nima mohamadian Young Researchers and Elite Club Ahvaz BranchIslamic Azad UniversityAhvazIran DWA Energy Limited LincolnUnited Kingdom Petroleum Department Petroleum University of TechnologyAhwazIran Young Researchers and Elite Club North Tehran BranchIslamic Azad UniversityTehranIran National Iranian South Oil Company(NISOC) AhvazIran Young Researchers and Elite Club Omidiyeh BranchIslamic Azad UniversityOmidiyehIran

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.

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

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