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

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Petroleum 2023年第3期9卷 468-485页

作者： Farshad Jafarizadeh Babak Larki Bamdad Kazemi Mohammad Mehrad Sina Rashidi jalil ghavidel neycharan Mehdi Gandomgoun Mohammad Hossein Gandomgoun Department of Petroleum Engineering Amirkabir University of TechnologyTehranIran Department of Petroleum Engineering Omidiyeh Branch of Islamic Azad UniversityOmidiyehIran Science and Research Branch Islamic Azad UniversityTehranIran Faculty of Mining Petroleum and Geophysics EngineeringShahrood University of TechnologyShahroodIran Department of Information Technology and Computer Engineering Shahid Madani University of AzerbaijanTabrizIran Faculty of Petroleum Engineering Sahand University of TechnologyTabrizIran

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

关键词： Lost circulation prediction Artificial intelligence Deep learning Feature selection

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