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An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores

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Petroleum 2023年第4期9卷 629-646页

作者： Chibuzo Cosmas Nwanwe Ugochukwu Ilozurike Duru Department of Minerals and Petroleum Resources Engineering Technology Federal Polytechnic NekedeOwerriP.M.B.1036OwerriNigeria Department of Petroleum Engineering Federal University of TechnologyOwerriP.M.B.1526OwerriNigeria

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are *** is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental *** addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical *** makes it difficult for other readers to use these ML models since the datasets used in their development are not *** study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)*** dataset was divided into two sets;80%for training and 20%for *** performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction *** optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter *** analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on *** addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning *** training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.

关键词： Machine learning models Empirical correlations mechanistic models Multiphase flowing bottom-hole pressure Adaptive neuro-fuzzy inference system White-box model

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An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

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Petroleum Research 2023年第3期8卷 370-385页

作者： Chibuzo Cosmas Nwanwe Ugochukwu Ilozurike Duru Charley Anyadiegwu Azunna I.B.Ekejuba Department of Minerals and Petroleum Resources Engineering Technology Federal Polytechnic NekedeOwerriP.M.B.1036OwerriImo StateNigeria Department of Petroleum Engineering Federal University of TechnologyOwerriP.M.B.1526OwerriImo StateNigeria

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production *** empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent *** machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box *** addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open *** make using the ML models on new datasets *** study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box *** data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for *** analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best *** trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and *** analysis shows that the new model produced the expected effects of physical attributes on ***,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box *** of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

关键词： Flowing bottom-hole pressure Real-time prediction Artificial neural network Visible mathematical model Levenberg-marquardt optimization algorithm Hyperbolic tangent activation function Empirical correlations mechanistic models

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Bridge Plug Drillouts Cleaning Practices—An Overview

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Natural Resources 2021年第2期12卷 19-33页

作者： Haithem Trabelsi Abdennour Seibi Ning Liu Fathi Boukadi Racha Trabelsi University of Louisiana Lafayette USA Utah Valley State University Orem USA

Horizontal fracture-simulated completions remain the most reliable method of producing hydrocarbons from shale formations. The vast majority of unconventional wells are completed using the “Plug and Perf” method. This method involves using either a coiled tubing (CT) with a positive displacement motor or a jointed pipe to mill out composite plugs after fracturing operations are completed. An estimated average of 120,000 composite plugs is installed in the US alone each year. Bridge plug drillouts from milling operations tend to accumulate in horizontal wells and can cause stuck pipe incidents and loss of well control. Efficient removal of composite plugs’ debris is crucial in achieving operational efficacies and full production potential. This paper provides an overview of the various bridge plug drillouts cleaning practices adopted in horizontal wells. It discusses several case histories, showcasing how operators solved cleanout challenges. Developed mechanistic models to better understand hole cleaning are also reviewed. As more unconventional wells are being set at more extensive depths, an economical and optimized coiled tubing process becomes increasingly important. This paper focuses on delivering a more conclusive set of recommendations to increase efficiency and improve current composite plug coiled tubing cleaning-milling practices, increase operational efficiency and reduce cost.

关键词： CT Horizontal Well Bridge Plug Drillout Hole Cleaning Field Cases mechanistic models Research Gap

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