A new robust predictive model for lost circulation rate using convolutional neural network:A case study from Marun Oilfield

作     者：Farshad Jafarizadeh Babak Larki Bamdad Kazemi Mohammad Mehrad Sina Rashidi Jalil Ghavidel Neycharan Mehdi Gandomgoun Mohammad Hossein Gandomgoun Farshad Jafarizadeh;Babak Larki;Bamdad Kazemi;Mohammad Mehrad;Sina Rashidi;Jalil Ghavidel Neycharan;Mehdi Gandomgoun;Mohammad Hossein Gandomgoun

作者机构：Department of Petroleum EngineeringAmirkabir University of TechnologyTehranIran Department of Petroleum EngineeringOmidiyeh Branch of Islamic Azad UniversityOmidiyehIran Science and Research BranchIslamic Azad UniversityTehranIran Faculty of MiningPetroleum and Geophysics EngineeringShahrood University of TechnologyShahroodIran Department of Information Technology and Computer EngineeringShahid Madani University of AzerbaijanTabrizIran Faculty of Petroleum EngineeringSahand University of TechnologyTabrizIran 

出 版 物：《Petroleum》 (油气（英文）)

年 卷 期：2023年第9卷第3期

页      面：468-485页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 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）] 

主　　题：Lost circulation prediction Artificial intelligence Deep learning Feature selection 

摘      要：A major cause of some of serious issues encountered in a drilling project,including wellbore instability,formation damage,and drilling string stuck e which are known to increase non-productive time(NPT)and hence the drilling cost e is what we know as mud *** mud loss can be prevented or at least significantly reduced by taking proper measures beforehand provided the position and intensity of such loss can be properly predicted using an accurate predictor ***,in this study,we used the convolutional neural network(CNN)and hybridized forms of multilayer extreme learning machine(MELM)and least square support vector machine(LSSVM)with the Cuckoo optimization algorithm(COA),particle swarm optimization(PSO),and genetic algorithm(GA)for modeling the mud loss rate based on drilling data,mud properties,and geological information of 305 drilling wells penetrating the Marun *** this purpose,we began by a pre-processing step to attenuate the effect of noise using the Savitzky-Golay *** whole set of available data was divided into the modeling(including 2300 data points)and the validation(including 483 data points)***,the second generation of the non-dominated sorting genetic algorithm(NSGA-Ⅱ)was applied to the modeling data to identify the most significant features for estimating the mud *** results showed that the prediction accuracy increased with the number of selected features,but the increase became negligible when the number of selected features exceeded ***,the following 9 features were selected as input to the intelligent algorithms(IAs):pump pressure,mud weight,fracture pressure,pore pressure,depth,gel 10 min/gel 10 s,fan 600/fan 300,flowrate,and formation *** of the hybrid algorithms and simple forms of LSSVM and CNN to the training data(80%of the modeling data,i.e.1840 data points)showed that all of the models tend to underestimate the mud loss at higher mud loss rates,although the CNN exhibited l