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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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The Effects of Fluid Rheology and Drillstring Eccentricity on Drilling Hydraulics

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Open Journal of Yangtze Oil and Gas 2021年第4期6卷 129-145页

作者： Anthony Kerunwa Julian Ubanozie Obibuike ugochukwu ilozurike duru Stanley Toochukwu Ekwueme Federal University of Technology Owerri Nigeria

Accurate determination of hydraulic parameters such as pressure losses, equivalent circulation density (ECD), etc. plays profound roles in drilling, cementing and other well operations. Hydraulics characterization requires that all factors are considered as the neglect of any could become potential sources of errors that would be detrimental to the overall well operation. Drilling Hydraulics has been extensively treated in the literature. However, these works almost entirely rely on the assumption that the drill string lies perfectly at the center of the annulus—the so-called “concentric annulus”. In reality, concentricity is almost never achieved even when centralizers are used. This is because of high well inclination angles and different string geometries. Thus, eccentricity exists in practical oil and gas wells especially horizontal and extended reach wells (ERWs) and must be accounted for. The prevalence of drillstring (DS) eccentricity in the annulus calls for a re-evaluation of existing hydraulic models. This study evaluates the effect of drilling fluid rheology types and DS eccentricity on the entire drilling hydraulics. Three non-Newtonian fluid models were analyzed, viz: Herschel Bulkley, power law and Bingham plastic models. From the results, it was observed that while power law and Bingham plastic models gave the upper and lower hydraulic values, Herschel Bulkley fluid model gave annular pressure loss (APL) and ECD values that fall between the upper and lower values and provide a better fit to the hydraulic data than power law and Bingham plastic fluids. Furthermore, analysis of annular eccentricity reveals that APLs and ECD decrease with an increase in DS eccentricity. Pressure loss reduction of more than 50% was predicted for the fully eccentric case for Herschel Bulkley fluids. Thus, DS eccentricity must be fully considered during well planning and hydraulics designs.

关键词： Wellbore Drilling Fluid String Geometries Pressure Losses Newtonian Fluid Models

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