Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Vanessa de Jesus da Silva
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações da UFRRJ
Texto Completo: https://rima.ufrrj.br/jspui/handle/20.500.14407/13423
Resumo: A garantia de um poço que apresente uma boa taxa de produção de óleo está diretamente relacionada com a etapa de perfuração do mesmo, sendo o controle da pressão anular de fundo ou bottomhole pressure (BHP) o ponto de interesse. Assim, este trabalho objetiva a implementação de controladores baseados em redes neuronais para regular a pressão anular de fundo, durante o processo de perfuração de poços de petróleo, frente a distúrbios como kick de gás, perda de circulação e o procedimento de conexão de tubos. Tais distúrbios, além de causar flutuações de pressão que podem danificar o poço, podem levar a danos ambientais, financeiros e de recursos humanos, nos casos mais extremos. Neste estudo, utilizou como variável manipulada o índice de abertura da válvula choke. Pra fins de identificação e controle em tempo real utilizou-se uma rede neuronal do tipo feedforward com uma camada de neurônios ocultos, apresentando como sinais de entrada: pressão anular, pressão no choke, frequência da bomba de água e de lama, abertura da choke, vazão do anular, tempo e set point, e um neurônio na camada de saída. Controladores neuronais são atrativos por apresentaremxi habilidade em lidar com sistema não lineares e inerentemente transientes, como é o caso do processo de perfuração de poços de petróleo. Os controladores neuronais foram comparados ao controlador clássico PI (Ziegler Nichols (1942) e Cohen-Coon (1953)). Além disso, foram realizados estudos de simulação e experimentos em unidade de perfuração. Os controladores desenvolvidos mostraram-se eficientes em controlar a pressão anular de fundo
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spelling Ribeiro, Vanessa de Jesus da SilvaDomiciano, Márcia Peixoto Vega02361179717http://lattes.cnpq.br/5519694469268323Domiciano, Márcia Peixoto VegaOssanai, CláudiaSouza, Marcio Nele de14043733739http://lattes.cnpq.br/55552435329724872023-12-22T02:46:24Z2023-12-22T02:46:24Z2018-08-29RIBEIRO, Vanessa de Jesus da Silva. Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais. 2018. 136 f.. Dissertação( Mestrado em Engenharia Química) - Instituto de Tecnologia, Universidade Federal Rural do Rio de Janeiro, Seropédica-RJ, 2018.https://rima.ufrrj.br/jspui/handle/20.500.14407/13423A garantia de um poço que apresente uma boa taxa de produção de óleo está diretamente relacionada com a etapa de perfuração do mesmo, sendo o controle da pressão anular de fundo ou bottomhole pressure (BHP) o ponto de interesse. Assim, este trabalho objetiva a implementação de controladores baseados em redes neuronais para regular a pressão anular de fundo, durante o processo de perfuração de poços de petróleo, frente a distúrbios como kick de gás, perda de circulação e o procedimento de conexão de tubos. Tais distúrbios, além de causar flutuações de pressão que podem danificar o poço, podem levar a danos ambientais, financeiros e de recursos humanos, nos casos mais extremos. Neste estudo, utilizou como variável manipulada o índice de abertura da válvula choke. Pra fins de identificação e controle em tempo real utilizou-se uma rede neuronal do tipo feedforward com uma camada de neurônios ocultos, apresentando como sinais de entrada: pressão anular, pressão no choke, frequência da bomba de água e de lama, abertura da choke, vazão do anular, tempo e set point, e um neurônio na camada de saída. Controladores neuronais são atrativos por apresentaremxi habilidade em lidar com sistema não lineares e inerentemente transientes, como é o caso do processo de perfuração de poços de petróleo. Os controladores neuronais foram comparados ao controlador clássico PI (Ziegler Nichols (1942) e Cohen-Coon (1953)). Além disso, foram realizados estudos de simulação e experimentos em unidade de perfuração. Os controladores desenvolvidos mostraram-se eficientes em controlar a pressão anular de fundoA well that presents a good oil production rate is directly related to the drilling stage being the control of the pressure is the most important step. Thus the major objective of this work is the implementation of neural network-based controllers to regulate the annulus bottomhole pressure (BHP) during drilling, in the event of disturbances such as gas kick, circulation and pipe connection procedure. Such disturbances, in addition to causing pressure fluctuations that can damage the well, can lead to environmental, financial and human resource damage in the most extreme cases. In this study, the choke valve was used as the manipulated variable. For the purpose of identification and control in real time, a feedforward neuronal network was used with a layer of hidden neurons, presenting as input signals: annular pressure, choke pressure, water pump and mud frequency, choke opening , annular flow, time and set point, and a neuron in the output layer. Neural controllers are attractive because they have the ability to deal with nonlinear and inherently transient systems, as is the case with of oil well drilling process. Neuronal controllers were compared with classical PI controller (Ziegler Nichols (1942) and Cohen-Coon (1953)). It is noteworthy that simulation studies and experiments in drilling unit were carried out. The developed closed loop scheme showed to be eficient to regulate annulus bottomhole pressureapplication/pdfporUniversidade Federal Rural do Rio de JaneiroPrograma de Pós-Graduação em Engenharia QuímicaUFRRJBrasilInstituto de Tecnologiarede neuronalcontroleperfuração de poçosNeural networkcontroloil well drillingEngenharia QuímicaIdentificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais-info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisAHMADI, M. A.; SOLEIMANI, R.; LEE, M.; KASHIWAO, T.; BAHADORI, A.; Deterimantion of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool, Petroleum, http://dx.doi.org/10.1016/j.petlm.2015.06.004, 2015. AHMED, M. A.; HEGAB, O. A.; SABRY, A.; Early detection enhancement of the kick and near-balance drilling using mud logging warning sign, Egyptian Journal of Basic and Applied Science, 3, 85-93, 2016. ALMEIDA, L. F.; VELLASCO, M. M. B. R.; PACHECO, M. A. C.; Optimization system for valve control in intelligent wells under uncertainties, Journal of Petroleum Science and Engineering, 73, 129-140, 2010. AKIN, S.; ROSS, C. M.; KOVSCEK A. R.; Combination of well log and pore-scale data to predict petrophysical properties of diatomite, Journal of Petroleum Science and Engineering, 133-149, 2008. ALIZADEH, M.; MOVAHED, Z.; JUNIN, R.; MOHSIN, R.; ALIZADEH, M.; ALIZADEH, M.; A new techinique to predict the fractures dip using artificial neural networks and image logs data, Jurnal Teknologi (Science & Engineering), 75:11, 33-40, 2015. ARBIB, M. A.; The handbook of brain theory and neuronal networks, 2nd ed., Madison, Wisconsin, USA, 2002. ARDEKANI, O. S.; SHADIZADEH, S. R.; Development of drilling trip time model for southern Iranian oil fields: using artificial neural networks and multiple linear regression approaches, Jouranl of Petrol Explor Prod Technol, 3:287-295, 2013. AZADPOUR, M.; MANAMAN, N. S.; KADKHODAIE-ILKHCHI, A. SEDGHIPOUR, M.-R.; Pore pressure prediction and modeling using well-logging data in one of the gas fields in south of Iran, Journal of Petroleum Science and Engineering, 128, 15-23, 2015. BASSAM, A.; CASTILLO, A. A.; GARCÍA-VALLADARES, O.; SANTOYO, E.; Determination of pressure drops in flowing geothermal wells by using artificial neural networks and wellbore simulation tools, Applied Thermal Engineering, 75, 1217-1228, 2015. BOURGOYNE, A. T.; MILLHEIM, K. K.; CHENEVERT, M. E.; YOUNG, F. S.; Applied Drilling Engineering, Society of Petroleum Engineers, Richardson -TX 1986. BJORKEVOLL, K. S.; ANFINSEN, B.-T.; MERLO, A.; ERIKSEN, N.-H.; OLSEN, E.; Analysis of extended reach drilling data using an advanced pressure and temperature model, SPE 62728, 2000. BJORKEVOLL, K. S.; ROMMETVEIT, R.; AAS, I. B.; GJERALDSTVEIT, H.; MERLO, A.; Transient gel breaking model for critical wells applications with field data verification, SPE /IADC 79843, 2003. 74 BREYHOLTZ, O.; NYGAARD G.; NIKOLAOU, M.; Advanced automatic control or dual gradient drilling, in SPE Annual Technical Conference and Exhibition, SPE 124631, New Orleans, Louisiana, USA, 2009. CARVALHO, M. A. D.; Estudos experimentais, de simulação e de controle na perfuração de poços de petróleo utilizando a técnica pressurized mud cap drilling, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/ UFRRJ, Seropédica, 2018. CARVALHO, M. A. D.; OLIVEIRA, G. F. M.; FERNANDES, L. D.; MARTINS, A. L.; VEJA, M. P.; Two-phase flow model validation during conventional/Pressurized Mud Cap Drilling (PMCD) scenarios, Journal of Petroleum Science and Engineering, 2018. COHEN, G.H.; COON, G.A., Theoretical considerations of retarded control, Transactions of the ASME, p. 827-834, 1953. CUNHA, J. C.; ROSA, F. S. N.; LAGE, A. C. V. M.; NEGRÃO, A. F.; Planejamento e execução de poços de petróleo no Brasil utilizando-se perfuração sub-balanceada, XV Congresso Brasileiro de Engenharia Mecânica, São Paulo, 1999. ESIM, E.; YILDIRIM, S.; Drilling performance analysis of drill column machine using proposed neural networks, Neural Comput & Applic, 2016. FATTAHI, H.; BAZDAR, H.; Applying improved artificial neuronal network models to evaluate drilling rate index, Tunnelling and Underground Space Technology 70 114–124, 2017. FAUSETT, L. V.; Fundamentals of Neuronal Networks: Architectures, Algorithms and Applications, 1ª ed., Prentice – Hall, 1994. FREITAS, M. G.; Controle da pressão anular de fundo durante a perfuração de poços de petróleo, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/ UFRRJ, Seropédica, 2013. GANDELMAN, R. A.; TEIXEIRA, G. T.; WALDMANN, A. T. A.; MARI, A.; REZENDE, M. S.; ARAGÃO, A. F. L.; MARTINS, A. L.; Development of a computational tool to avoid and remediate drilling problemsby real time PWD data interpretation, International Congress of Mechanical Engineering, 2009. GAO, F.; WANG, F.; LI, M.; Neuronal network-based optimal iterative controller for nonlinear processes, The Canadian Journal of Chemical Engineering, v.78, 2000. GHOBADPOURI, S.; HAJIDAVALLOO, E.; NOGHREHABADI, A. R.; Modeling and simulation of gas-liquid-solid three-phase flow in under-balanced drilling operation, Journal of Petroleum Science and Engineering, 156, 348-355, 2017. GODHAVN, J. M.; Control requirements for automatic managed pressure drilling system, SPE, Statoil ASA, 2010. GODHAVN, J. M.; Control requirements for high-end automatic MPD operations, in: SPE/IADC Drilling Conference and Exhibition, SPE 119442, Amsterdam, Netherlands, 2009. 75 GRACE, R. D.; Advanced blowout and well control, Gulf Publishing Company, 1994. GREIN, E. A.; DANNENHAUER, C. E.; CRUZ, M. S.; SILVA, F. R. G.; MARTINS, A. L.; VANNI, G. S.; RIBEIRO, V. J. S.; VEGA, M. P.; Evolution of Control Strategies to Minimize Pressure Fluctuation During Connections in MPD Deepwater Operations, OTC 27990 MS, Rio de Janeiro, 2017. HANSEN, S. A.; ROMMETVEIT, R.; STERRY, N.; AAS, B.; MERLO, A.; A new hydraulics model slim hole drilling applications, SPE/IADC 575579, 1999. HAYAJNEH, M.; HASSAN, A. M.; MAYYAS, A. T.; Artificial neural network modeling of the drilling process of self-lubricated aluminum/alumina/graphite hybrid composites synthesized by powder metallurgy technique, Journal of Alloys and Compounds, 478, 559-565, 2009. HAYKIN, S.; Neuronal networks and learning machines, 3ª ed., Prentice – Hall, 2009. HIMMELBLAU, D. M.; Applications of artificial neural networks in chemical engineering, Korean J. Chem. Eng., 17(4), 373-392, 2000. HUNT, K. J.; SBARBARO, D.; ZBIKOWSKI, R.; GAWTHROP, P. J.; Neural networks for control system – a survey, Automatica, 28, 6, 1083-1112, 1992. INGHAM, J; DUNN, I. J.; HEINZLE, E.; PRENOSIL, J. E.; SNAPE, J. B.; Chemical Engineering Dynamics: An Introduction to Modelling and Computer Simulation, 3. Edition, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2007. IRAWAN, S.; FAHMI, R. M.; FATHADDIN, M. T.; Application of neuronal network for predictive control in bit properties selection, International Journal of Petroleum Science and Technology, v. 3, nº 1, pp. 101 – 112, 2009. JENNER, J.W., ELKINS, H.L., SPRINGETT, F., LURIE, P.G., WELLINGS, J.S., The continuous circulations systems: an advance in constante pressure drilling, in SPE anual technical Conferenca and Exhibition, no. SPE 90702, Houston, TX, USA, 2004. KAASA, G.; STAMNES, O. N.; IMSLAND, L.; AAMO, O. M.; Intelligent estimation of downhole pressure using a simple hydraulic model, IADC/SPE 143097, 2011. KHEZRIAN, M.; HAJIDAVALLOO, E.; SHEKARI, Y.; Modeling and simulationof under-balanced drilling operation using two-fluid model of two-phase flow, Chemical Engineering Research and Design, 93, 30-37, 2015. KHORIAKOV, V.; JOHANSEN, A. C.; JOHANSEN, T. E.; Transient flow modeling of advanced wells, Journal of Petroleum Science and Engineering, 86-87, 99 – 110, 2012. LAGE, A. C. V. M.; FJELDE, K. K.; TIME, R. W.; Underbalanced drilling dynamics: two-phase flow modeling and experiments, SPE Journal, 8, pp. 61-70, SPE 83607, 2003. LAGE, A. C. V. M.; TIME, R. W.; Mechanistic model for upward two-phase flow in annuli, SPE 63127, 2000. 76 LEITH, D. J.; LEITHEAD, W. E.; Survey of gain-scheduling analysis and design, International Journal of Control, v. 73, n. 11, p. 1001-1025, DOI: 10.1080/002071700411304, 2010. LI, X.; FENG, Y.; EL MOHTAR, C. S.; GRAY, K. E.; Transient modeling of borehole breakouts: A coupled thermo-hydro-mechanical approach, Journal of Petroleum Science and Engineering, 2018. LYONS, W. C.; Standard handbook of petroleum & natural gas engineering, v. 1, Gulf Publishing Company, 1996. MAROOKA, C. K.; GUILHERME, I. R.; MENDES, J. R. P.; Development of intelligent systems for well drilling and petroleum production, Journal of Petroleum Science and Engineering, 32, 191-199, 2001. MARTIN, M. D.; Managed pressure drilling techniques and tools, A thesis, Texas A&M University, 2006. MOHAGHEGH, S.; AREFI, R.; BILGESU, I.; AMERI, S.; ROSE, D.; Design and development of an artificial neural network for estimation of formation permeability, SPE 28237, 1995. NANDAN, A., IMTIAZ, S., 2017. Nonlinear model predictive control of managed pressure drilling, ISA Transactions, 69, 307-314. NARENDRA, K. S.; Neural networks for control: theory and practice, Proceedings of the IEEE, 84, 10, 1385-1406, 1996. NAS, S.; GEDGE, B.; PALAO, F. BOT, N. V.; Advantages of managed pressure drilling and the recent deployment of the technology in Vietnam, IADC/SPE 136513, 2010. NGUYEN, H. T.; PRASAD, N. R.; WALKER, C. L.; WALKER, E. A.; A first course in fuzzy and neuronal control, Chapman & Hall/CRC, 2003. NYGAARD, G.; NAEVDAL, G.; Nonlinear model predictive control scheme for stabilizing annulus pressure during oil well drilling, Journal of process control, ELSEVIER, 2006. NYGAARD, G.; Multivariable process control in high temperature and high-pressure environment using non-intrusive multi-sensor data fusion, Ph.D. Thesis, Norwegian University of Science and Technology, 2006. NYGAARD, G.H.; VEFRING, E. H.; FJELDE, K. K.; NAEVDAL, G.; LORENTZEN, R. J.; MYLVAGANAM, S.; Bottomhole pressure control during pipe connection in gas-dominant wells, SPE 91578, 2004a. NYGAARD, G.H.; VEFRING, E. H.; MYLVAGANAM, S.; LORENTZEN, R. J.; NAEVDAL, G.; FJELDE, K. K.; Underbalanced drilling: improving pipe connection procedures using automatic control, SPE 90962, 2004b. 77 NYGAARD, G.H., NAEVDAL, G., MYLVAGANAM, S., Evaluating non linear Kalman filters for parameter estimation in reservoirs during petroleum well drilling, International conference on control applications, Munich, Germany, October 4-6, 2006. NYGAARD, G.; JOHANNESSEN, E.; GRAVDAL, J. E.; IVERSEN, F.; Automatic coordinated control of pump rates and choke valve for compensating pressure fluctuations during surge and swab operations, in: IADC/SPE Managed Pressure Drilling and Underbalanced Operations Conference and Exhibition, SPE 108344, Galveston, Texas, U.S.A., 2007. OLIVEIRA, G. F. M.; VIEIRA, F. R. B.; VEGA, M. P.; COELHO, D. A. F.; RIBEIRO, V. J. S.; Estudo de controlador com compensação de tempo morto para regular a pressão anular de fundo – VI Encontro Nacional de Hidráulica de Poços de Petróleo e Gás, 2015. OKPO, E. E.; DOSUNMU, A.; ODAGME, B .S.; Artificial neural network model for predicting wellbore instabillity, SPE 184371 – MS, 2016. PATRÍCIO, R. V.; Estudo de controle na perfuração de poços de petróleo em presença de kick de gás, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/UFRRJ, Seropédica, 2016. PATRÍCIO, R. V.; OLIVEIRA, G. F. M.; CARVALHO, M. A. D.; FERNANDES, L. D.; MARTINS, A. L.; VEJA, M. P.; Dynamicgas kick regulation through control reconfiguration under MPD scenario – two-phase flow validation, Journal of Petroleum Science and Engineering, 2018. PEDERSEN, T.; AARSNES, U. J. F.; GODHAVN, J.-M.; Flow and pressure control of underbalanced drilling operations using NMPC, Journal of Process Control, 68, 73-85, 2018. PETERSEN, J.; BJORKEVOLL, K. S.; LEKVAM, K.; Computing the danger of hydrate formation using a modified dynamic kick simulator, SPE/IADC 67749, 2001. PETERSEN, J.; ROMMETVEIT, R.; TARR, B.; Kick with lost circulation simulator, a tool for design of complex well control situations, SPE 49956, 1998. RAMGULAM, A.; ERTEKIN, T.; FLEMINGS, P. B.; Utilization of artificial neural network in the optimization of history matching, SPE 107468, 2007. ROCHA, L. A. S.; AZEVEDO, C. T.; Projeto de poços de petróleo: Geopressões e Assentamentos de Colunas de Revestimento, 2º ed., Interciência, Rio de Janeiro, 2009. ROMMETVEIT, R.; VEFRING, E. H.; Comparison of results from an advanced gas kick simulator with surface and downhole data from full scale gas kick experiments in an inclined well, SPE 22558, 1991. ROOKI, R.; Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel-Bulkley drilling fluids in oil drilling, Measurement, 85, 184-191, 2016. 78 SANTOS, G.; SERRANO, M.; VILLA, L.; NIETO, E.; It is all about pressure: drilling a high – pressure wellin record time using real-time tools, Society of Petroleum Engineers, 2010. SEBORG, D. E.; EDGAR, T. F.; MELLICHAMP, D. A.; Process dynamics and control, 2º ed., John Wiley & Sons, Inc., 2003. SHAO, H.; JIANG, L.; LIU, L.; ZHAO, Q.; Modeling of multiphase flow through chokes, Flow Measurement and Instrumentation, 60, 44-50, 2018. SHAYEGI, S.; LOVORN, R.; Comparison of reservoir knowledge, drilling benefits and economic advantages of UBD, MP, IADC/SPE Managed Pressure Drilling and Underbalanced Operations Conference & Exhibition, Galveston-Texas, 2007. SPESIVTSEV, P.; SINKOV, K.; SOFRONOV, I.; ZIMINA, A.; UMNOV, A.; YARULLIN, R.; VETROV, D.; Predictive model for bottomhole pressure based on machine learning, Journal of Petroleum Science and Engineering, 166, 825-841, 2018. SUNDARESAN, K. R. & KRISHNASWANY P. R.; Estimation of time delay time constant parameters in time, frequency, and Laplace domains, Can J. Chem. Eng., 56, pp 257, 1977. THOMAS, J. E.; Fundamentos de Engenharia de Petróleo, Interciência, Rio de Janeiro, 2001. TURNEY, D. E.; KALAGA, D. V.; ANSARI, M.; YAKOBOV R.; JOSHI, J. B.; Reform of the drift-flux model of multiphase flow in pipes, wellbores, and reactors vessels, Chemical Engineering Science, 184, 251-258, 2018. VAZ, A.G.R.; ELSINGA, B.; van SARK, W. G. J. H. M.; BRITO, M. C.; An artificial neuronal network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands, Renewable Energy, v. 85, p 631-641, 2016. VEGA, M. P.; OLIVEIRA, G. F. M; FERNANDES, L. D.; MARTINS, A. L.; Monitoring and control strategies to manage pressure fluctuations during oil well drilling, Journal of Petroleum Science and Engineering, v 106, p. 337-349, 2018. VEGA, M. P.; VIEIRA, F. B.; FERNANDES, L. D.; FREITAS, M. G.; RUSSANO, E.; MARTINS, A. J.; Smart monitoring and decision making for regulating annulus bottom hole pressure while drilling oil wells, Brazilian Journal of Chemical Engineering, V. 33, No. 04, pp 969-983, 2016. VEGA, M.; FREITAS, M.; FERNANDES, L.; SCHEID, C.; MARTINS, A.; Best real-time model development of an oil well drilling system, Chemical Engineering Transactions, 26, 561-566 DOI:103303/CET1226094; 2012. VEGA, M. P.; SCHEID, C. M.; CALÇADA, L. A.; MANCINI, M. C.; MARTINS, A. L.; Nonlinear Identification and Model-Based Control of an Oil Well Drilling Process, ESCAPE, 2008. 79 VEGA, M. P.; LIMA, E. L.; PINTO, J. C.; Use of bifurcation analysis form development of nonlinear models for control applications, Chemical Engineering Science, v. 63, nº 21, p. 5129-5140, 2008. VIEIRA, F. R. B.; Controle da pressão anular de fundo durante a perfuração de poços de petróleo, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/UFRRJ, Seropédica, 2009. VIEIRA, F. R. B.; VEGA, M. P; FREITAS, M. G.; FOLSTA, M. G.; ARAÚJO, N. F; GANDELMAN, R.; Controle da pressão de fundo durante a perfuração de poços de petróleo, Boletim técnico da produção de petróleo, Rio de Janeiro, v. 4, p. 275-300, 2009. WALDMANN, A., ANDRADE, A.R., MARTINS A.L., LOMBA, R.F.T., ARAGÃO, A.F.L.; Sobre os mecanismos responsáveis por minimizar a invasão do fluido de perfuração na rocha reservatório - Encontro Nacional de Hidráulica de Perfuração e Completação de Poços de Petróleo e Gás Pedra Azul, Domingos Martins, 2006. YOU, J.; RAHNEMA, H.; Numerical modeling of multiphase steam flowin wellbore, Journal of Petroleum Science and Engineering, 164, 259-277, 2018. ZAHEDI, J.; ROUNAGHI, M. M.; Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange, Physica A, v. 438, p 178 – 187, 2015. ZHANG, S.; LV, J.; YUAN, X.; YIN, S.; BP neural network with genetic algorithm optimization for prediction of geo-stress state from wellbore pressures, International Journal of Computational Intelligence and Applications, 15, 3, 2016. ZHAO, Q.; ZHANG, B.; HU, H.; Novel two-step filtering scheme for a logging-while-drilling system, Computer Physics Communications, 180, 1566-1571, 2009. ZIEGLER, J. G. NICHOLS, N. B. Optimum Settings for Automatic Controllers, Transactions of the A.S.M.E., V. 64, p. 759-768, 1942. ZHOU, J.; NYGAARD, G.; Automatic model-based control scheme for stabilizing pressure during dual-gradient drilling, Journal of Process Control, 21, 1138-1147, 2011. ZHOU, Y.; WOJTANOWICZ, A. K.; LI, X.; MIAO, Y.; CHEN, Y.; Improved model for gas migration velocity of stagnant non-Newtonian fluids in annulus, Journal of Petroleum Science and Engineering, 168, 190-200, 2018.https://tede.ufrrj.br/retrieve/66560/2018%20-%20Vanessa%20de%20Jesus%20da%20Silva%20Ribeiro.pdf.jpghttps://tede.ufrrj.br/jspui/handle/jspui/4995Submitted by Celso Magalhaes (celsomagalhaes@ufrrj.br) on 2021-08-31T13:31:05Z No. of bitstreams: 1 2018 - Vanessa de Jesus da Silva Ribeiro.pdf: 76347467 bytes, checksum: 23ddfd58bbf819969de4a39ba148eff4 (MD5)Made available in DSpace on 2021-08-31T13:31:05Z (GMT). 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dc.title.por.fl_str_mv Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
dc.title.alternative.eng.fl_str_mv -
title Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
spellingShingle Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
Ribeiro, Vanessa de Jesus da Silva
rede neuronal
controle
perfuração de poços
Neural network
control
oil well drilling
Engenharia Química
title_short Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
title_full Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
title_fullStr Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
title_full_unstemmed Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
title_sort Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais
author Ribeiro, Vanessa de Jesus da Silva
author_facet Ribeiro, Vanessa de Jesus da Silva
author_role author
dc.contributor.author.fl_str_mv Ribeiro, Vanessa de Jesus da Silva
dc.contributor.advisor1.fl_str_mv Domiciano, Márcia Peixoto Vega
dc.contributor.advisor1ID.fl_str_mv 02361179717
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/5519694469268323
dc.contributor.referee1.fl_str_mv Domiciano, Márcia Peixoto Vega
dc.contributor.referee2.fl_str_mv Ossanai, Cláudia
dc.contributor.referee3.fl_str_mv Souza, Marcio Nele de
dc.contributor.authorID.fl_str_mv 14043733739
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/5555243532972487
contributor_str_mv Domiciano, Márcia Peixoto Vega
Domiciano, Márcia Peixoto Vega
Ossanai, Cláudia
Souza, Marcio Nele de
dc.subject.por.fl_str_mv rede neuronal
controle
perfuração de poços
topic rede neuronal
controle
perfuração de poços
Neural network
control
oil well drilling
Engenharia Química
dc.subject.eng.fl_str_mv Neural network
control
oil well drilling
dc.subject.cnpq.fl_str_mv Engenharia Química
description A garantia de um poço que apresente uma boa taxa de produção de óleo está diretamente relacionada com a etapa de perfuração do mesmo, sendo o controle da pressão anular de fundo ou bottomhole pressure (BHP) o ponto de interesse. Assim, este trabalho objetiva a implementação de controladores baseados em redes neuronais para regular a pressão anular de fundo, durante o processo de perfuração de poços de petróleo, frente a distúrbios como kick de gás, perda de circulação e o procedimento de conexão de tubos. Tais distúrbios, além de causar flutuações de pressão que podem danificar o poço, podem levar a danos ambientais, financeiros e de recursos humanos, nos casos mais extremos. Neste estudo, utilizou como variável manipulada o índice de abertura da válvula choke. Pra fins de identificação e controle em tempo real utilizou-se uma rede neuronal do tipo feedforward com uma camada de neurônios ocultos, apresentando como sinais de entrada: pressão anular, pressão no choke, frequência da bomba de água e de lama, abertura da choke, vazão do anular, tempo e set point, e um neurônio na camada de saída. Controladores neuronais são atrativos por apresentaremxi habilidade em lidar com sistema não lineares e inerentemente transientes, como é o caso do processo de perfuração de poços de petróleo. Os controladores neuronais foram comparados ao controlador clássico PI (Ziegler Nichols (1942) e Cohen-Coon (1953)). Além disso, foram realizados estudos de simulação e experimentos em unidade de perfuração. Os controladores desenvolvidos mostraram-se eficientes em controlar a pressão anular de fundo
publishDate 2018
dc.date.issued.fl_str_mv 2018-08-29
dc.date.accessioned.fl_str_mv 2023-12-22T02:46:24Z
dc.date.available.fl_str_mv 2023-12-22T02:46:24Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv RIBEIRO, Vanessa de Jesus da Silva. Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais. 2018. 136 f.. Dissertação( Mestrado em Engenharia Química) - Instituto de Tecnologia, Universidade Federal Rural do Rio de Janeiro, Seropédica-RJ, 2018.
dc.identifier.uri.fl_str_mv https://rima.ufrrj.br/jspui/handle/20.500.14407/13423
identifier_str_mv RIBEIRO, Vanessa de Jesus da Silva. Identificação e controle em linha de processo de perfuração de poços de petróleo utilizando redes neuronais. 2018. 136 f.. Dissertação( Mestrado em Engenharia Química) - Instituto de Tecnologia, Universidade Federal Rural do Rio de Janeiro, Seropédica-RJ, 2018.
url https://rima.ufrrj.br/jspui/handle/20.500.14407/13423
dc.language.iso.fl_str_mv por
language por
dc.relation.references.por.fl_str_mv AHMADI, M. A.; SOLEIMANI, R.; LEE, M.; KASHIWAO, T.; BAHADORI, A.; Deterimantion of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool, Petroleum, http://dx.doi.org/10.1016/j.petlm.2015.06.004, 2015. AHMED, M. A.; HEGAB, O. A.; SABRY, A.; Early detection enhancement of the kick and near-balance drilling using mud logging warning sign, Egyptian Journal of Basic and Applied Science, 3, 85-93, 2016. ALMEIDA, L. F.; VELLASCO, M. M. B. R.; PACHECO, M. A. C.; Optimization system for valve control in intelligent wells under uncertainties, Journal of Petroleum Science and Engineering, 73, 129-140, 2010. AKIN, S.; ROSS, C. M.; KOVSCEK A. R.; Combination of well log and pore-scale data to predict petrophysical properties of diatomite, Journal of Petroleum Science and Engineering, 133-149, 2008. ALIZADEH, M.; MOVAHED, Z.; JUNIN, R.; MOHSIN, R.; ALIZADEH, M.; ALIZADEH, M.; A new techinique to predict the fractures dip using artificial neural networks and image logs data, Jurnal Teknologi (Science & Engineering), 75:11, 33-40, 2015. ARBIB, M. A.; The handbook of brain theory and neuronal networks, 2nd ed., Madison, Wisconsin, USA, 2002. ARDEKANI, O. S.; SHADIZADEH, S. R.; Development of drilling trip time model for southern Iranian oil fields: using artificial neural networks and multiple linear regression approaches, Jouranl of Petrol Explor Prod Technol, 3:287-295, 2013. AZADPOUR, M.; MANAMAN, N. S.; KADKHODAIE-ILKHCHI, A. SEDGHIPOUR, M.-R.; Pore pressure prediction and modeling using well-logging data in one of the gas fields in south of Iran, Journal of Petroleum Science and Engineering, 128, 15-23, 2015. BASSAM, A.; CASTILLO, A. A.; GARCÍA-VALLADARES, O.; SANTOYO, E.; Determination of pressure drops in flowing geothermal wells by using artificial neural networks and wellbore simulation tools, Applied Thermal Engineering, 75, 1217-1228, 2015. BOURGOYNE, A. T.; MILLHEIM, K. K.; CHENEVERT, M. E.; YOUNG, F. S.; Applied Drilling Engineering, Society of Petroleum Engineers, Richardson -TX 1986. BJORKEVOLL, K. S.; ANFINSEN, B.-T.; MERLO, A.; ERIKSEN, N.-H.; OLSEN, E.; Analysis of extended reach drilling data using an advanced pressure and temperature model, SPE 62728, 2000. BJORKEVOLL, K. S.; ROMMETVEIT, R.; AAS, I. B.; GJERALDSTVEIT, H.; MERLO, A.; Transient gel breaking model for critical wells applications with field data verification, SPE /IADC 79843, 2003. 74 BREYHOLTZ, O.; NYGAARD G.; NIKOLAOU, M.; Advanced automatic control or dual gradient drilling, in SPE Annual Technical Conference and Exhibition, SPE 124631, New Orleans, Louisiana, USA, 2009. CARVALHO, M. A. D.; Estudos experimentais, de simulação e de controle na perfuração de poços de petróleo utilizando a técnica pressurized mud cap drilling, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/ UFRRJ, Seropédica, 2018. CARVALHO, M. A. D.; OLIVEIRA, G. F. M.; FERNANDES, L. D.; MARTINS, A. L.; VEJA, M. P.; Two-phase flow model validation during conventional/Pressurized Mud Cap Drilling (PMCD) scenarios, Journal of Petroleum Science and Engineering, 2018. COHEN, G.H.; COON, G.A., Theoretical considerations of retarded control, Transactions of the ASME, p. 827-834, 1953. CUNHA, J. C.; ROSA, F. S. N.; LAGE, A. C. V. M.; NEGRÃO, A. F.; Planejamento e execução de poços de petróleo no Brasil utilizando-se perfuração sub-balanceada, XV Congresso Brasileiro de Engenharia Mecânica, São Paulo, 1999. ESIM, E.; YILDIRIM, S.; Drilling performance analysis of drill column machine using proposed neural networks, Neural Comput & Applic, 2016. FATTAHI, H.; BAZDAR, H.; Applying improved artificial neuronal network models to evaluate drilling rate index, Tunnelling and Underground Space Technology 70 114–124, 2017. FAUSETT, L. V.; Fundamentals of Neuronal Networks: Architectures, Algorithms and Applications, 1ª ed., Prentice – Hall, 1994. FREITAS, M. G.; Controle da pressão anular de fundo durante a perfuração de poços de petróleo, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/ UFRRJ, Seropédica, 2013. GANDELMAN, R. A.; TEIXEIRA, G. T.; WALDMANN, A. T. A.; MARI, A.; REZENDE, M. S.; ARAGÃO, A. F. L.; MARTINS, A. L.; Development of a computational tool to avoid and remediate drilling problemsby real time PWD data interpretation, International Congress of Mechanical Engineering, 2009. GAO, F.; WANG, F.; LI, M.; Neuronal network-based optimal iterative controller for nonlinear processes, The Canadian Journal of Chemical Engineering, v.78, 2000. GHOBADPOURI, S.; HAJIDAVALLOO, E.; NOGHREHABADI, A. R.; Modeling and simulation of gas-liquid-solid three-phase flow in under-balanced drilling operation, Journal of Petroleum Science and Engineering, 156, 348-355, 2017. GODHAVN, J. M.; Control requirements for automatic managed pressure drilling system, SPE, Statoil ASA, 2010. GODHAVN, J. M.; Control requirements for high-end automatic MPD operations, in: SPE/IADC Drilling Conference and Exhibition, SPE 119442, Amsterdam, Netherlands, 2009. 75 GRACE, R. D.; Advanced blowout and well control, Gulf Publishing Company, 1994. GREIN, E. A.; DANNENHAUER, C. E.; CRUZ, M. S.; SILVA, F. R. G.; MARTINS, A. L.; VANNI, G. S.; RIBEIRO, V. J. S.; VEGA, M. P.; Evolution of Control Strategies to Minimize Pressure Fluctuation During Connections in MPD Deepwater Operations, OTC 27990 MS, Rio de Janeiro, 2017. HANSEN, S. A.; ROMMETVEIT, R.; STERRY, N.; AAS, B.; MERLO, A.; A new hydraulics model slim hole drilling applications, SPE/IADC 575579, 1999. HAYAJNEH, M.; HASSAN, A. M.; MAYYAS, A. T.; Artificial neural network modeling of the drilling process of self-lubricated aluminum/alumina/graphite hybrid composites synthesized by powder metallurgy technique, Journal of Alloys and Compounds, 478, 559-565, 2009. HAYKIN, S.; Neuronal networks and learning machines, 3ª ed., Prentice – Hall, 2009. HIMMELBLAU, D. M.; Applications of artificial neural networks in chemical engineering, Korean J. Chem. Eng., 17(4), 373-392, 2000. HUNT, K. J.; SBARBARO, D.; ZBIKOWSKI, R.; GAWTHROP, P. J.; Neural networks for control system – a survey, Automatica, 28, 6, 1083-1112, 1992. INGHAM, J; DUNN, I. J.; HEINZLE, E.; PRENOSIL, J. E.; SNAPE, J. B.; Chemical Engineering Dynamics: An Introduction to Modelling and Computer Simulation, 3. Edition, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2007. IRAWAN, S.; FAHMI, R. M.; FATHADDIN, M. T.; Application of neuronal network for predictive control in bit properties selection, International Journal of Petroleum Science and Technology, v. 3, nº 1, pp. 101 – 112, 2009. JENNER, J.W., ELKINS, H.L., SPRINGETT, F., LURIE, P.G., WELLINGS, J.S., The continuous circulations systems: an advance in constante pressure drilling, in SPE anual technical Conferenca and Exhibition, no. SPE 90702, Houston, TX, USA, 2004. KAASA, G.; STAMNES, O. N.; IMSLAND, L.; AAMO, O. M.; Intelligent estimation of downhole pressure using a simple hydraulic model, IADC/SPE 143097, 2011. KHEZRIAN, M.; HAJIDAVALLOO, E.; SHEKARI, Y.; Modeling and simulationof under-balanced drilling operation using two-fluid model of two-phase flow, Chemical Engineering Research and Design, 93, 30-37, 2015. KHORIAKOV, V.; JOHANSEN, A. C.; JOHANSEN, T. E.; Transient flow modeling of advanced wells, Journal of Petroleum Science and Engineering, 86-87, 99 – 110, 2012. LAGE, A. C. V. M.; FJELDE, K. K.; TIME, R. W.; Underbalanced drilling dynamics: two-phase flow modeling and experiments, SPE Journal, 8, pp. 61-70, SPE 83607, 2003. LAGE, A. C. V. M.; TIME, R. W.; Mechanistic model for upward two-phase flow in annuli, SPE 63127, 2000. 76 LEITH, D. J.; LEITHEAD, W. E.; Survey of gain-scheduling analysis and design, International Journal of Control, v. 73, n. 11, p. 1001-1025, DOI: 10.1080/002071700411304, 2010. LI, X.; FENG, Y.; EL MOHTAR, C. S.; GRAY, K. E.; Transient modeling of borehole breakouts: A coupled thermo-hydro-mechanical approach, Journal of Petroleum Science and Engineering, 2018. LYONS, W. C.; Standard handbook of petroleum & natural gas engineering, v. 1, Gulf Publishing Company, 1996. MAROOKA, C. K.; GUILHERME, I. R.; MENDES, J. R. P.; Development of intelligent systems for well drilling and petroleum production, Journal of Petroleum Science and Engineering, 32, 191-199, 2001. MARTIN, M. D.; Managed pressure drilling techniques and tools, A thesis, Texas A&M University, 2006. MOHAGHEGH, S.; AREFI, R.; BILGESU, I.; AMERI, S.; ROSE, D.; Design and development of an artificial neural network for estimation of formation permeability, SPE 28237, 1995. NANDAN, A., IMTIAZ, S., 2017. Nonlinear model predictive control of managed pressure drilling, ISA Transactions, 69, 307-314. NARENDRA, K. S.; Neural networks for control: theory and practice, Proceedings of the IEEE, 84, 10, 1385-1406, 1996. NAS, S.; GEDGE, B.; PALAO, F. BOT, N. V.; Advantages of managed pressure drilling and the recent deployment of the technology in Vietnam, IADC/SPE 136513, 2010. NGUYEN, H. T.; PRASAD, N. R.; WALKER, C. L.; WALKER, E. A.; A first course in fuzzy and neuronal control, Chapman & Hall/CRC, 2003. NYGAARD, G.; NAEVDAL, G.; Nonlinear model predictive control scheme for stabilizing annulus pressure during oil well drilling, Journal of process control, ELSEVIER, 2006. NYGAARD, G.; Multivariable process control in high temperature and high-pressure environment using non-intrusive multi-sensor data fusion, Ph.D. Thesis, Norwegian University of Science and Technology, 2006. NYGAARD, G.H.; VEFRING, E. H.; FJELDE, K. K.; NAEVDAL, G.; LORENTZEN, R. J.; MYLVAGANAM, S.; Bottomhole pressure control during pipe connection in gas-dominant wells, SPE 91578, 2004a. NYGAARD, G.H.; VEFRING, E. H.; MYLVAGANAM, S.; LORENTZEN, R. J.; NAEVDAL, G.; FJELDE, K. K.; Underbalanced drilling: improving pipe connection procedures using automatic control, SPE 90962, 2004b. 77 NYGAARD, G.H., NAEVDAL, G., MYLVAGANAM, S., Evaluating non linear Kalman filters for parameter estimation in reservoirs during petroleum well drilling, International conference on control applications, Munich, Germany, October 4-6, 2006. NYGAARD, G.; JOHANNESSEN, E.; GRAVDAL, J. E.; IVERSEN, F.; Automatic coordinated control of pump rates and choke valve for compensating pressure fluctuations during surge and swab operations, in: IADC/SPE Managed Pressure Drilling and Underbalanced Operations Conference and Exhibition, SPE 108344, Galveston, Texas, U.S.A., 2007. OLIVEIRA, G. F. M.; VIEIRA, F. R. B.; VEGA, M. P.; COELHO, D. A. F.; RIBEIRO, V. J. S.; Estudo de controlador com compensação de tempo morto para regular a pressão anular de fundo – VI Encontro Nacional de Hidráulica de Poços de Petróleo e Gás, 2015. OKPO, E. E.; DOSUNMU, A.; ODAGME, B .S.; Artificial neural network model for predicting wellbore instabillity, SPE 184371 – MS, 2016. PATRÍCIO, R. V.; Estudo de controle na perfuração de poços de petróleo em presença de kick de gás, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/UFRRJ, Seropédica, 2016. PATRÍCIO, R. V.; OLIVEIRA, G. F. M.; CARVALHO, M. A. D.; FERNANDES, L. D.; MARTINS, A. L.; VEJA, M. P.; Dynamicgas kick regulation through control reconfiguration under MPD scenario – two-phase flow validation, Journal of Petroleum Science and Engineering, 2018. PEDERSEN, T.; AARSNES, U. J. F.; GODHAVN, J.-M.; Flow and pressure control of underbalanced drilling operations using NMPC, Journal of Process Control, 68, 73-85, 2018. PETERSEN, J.; BJORKEVOLL, K. S.; LEKVAM, K.; Computing the danger of hydrate formation using a modified dynamic kick simulator, SPE/IADC 67749, 2001. PETERSEN, J.; ROMMETVEIT, R.; TARR, B.; Kick with lost circulation simulator, a tool for design of complex well control situations, SPE 49956, 1998. RAMGULAM, A.; ERTEKIN, T.; FLEMINGS, P. B.; Utilization of artificial neural network in the optimization of history matching, SPE 107468, 2007. ROCHA, L. A. S.; AZEVEDO, C. T.; Projeto de poços de petróleo: Geopressões e Assentamentos de Colunas de Revestimento, 2º ed., Interciência, Rio de Janeiro, 2009. ROMMETVEIT, R.; VEFRING, E. H.; Comparison of results from an advanced gas kick simulator with surface and downhole data from full scale gas kick experiments in an inclined well, SPE 22558, 1991. ROOKI, R.; Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel-Bulkley drilling fluids in oil drilling, Measurement, 85, 184-191, 2016. 78 SANTOS, G.; SERRANO, M.; VILLA, L.; NIETO, E.; It is all about pressure: drilling a high – pressure wellin record time using real-time tools, Society of Petroleum Engineers, 2010. SEBORG, D. E.; EDGAR, T. F.; MELLICHAMP, D. A.; Process dynamics and control, 2º ed., John Wiley & Sons, Inc., 2003. SHAO, H.; JIANG, L.; LIU, L.; ZHAO, Q.; Modeling of multiphase flow through chokes, Flow Measurement and Instrumentation, 60, 44-50, 2018. SHAYEGI, S.; LOVORN, R.; Comparison of reservoir knowledge, drilling benefits and economic advantages of UBD, MP, IADC/SPE Managed Pressure Drilling and Underbalanced Operations Conference & Exhibition, Galveston-Texas, 2007. SPESIVTSEV, P.; SINKOV, K.; SOFRONOV, I.; ZIMINA, A.; UMNOV, A.; YARULLIN, R.; VETROV, D.; Predictive model for bottomhole pressure based on machine learning, Journal of Petroleum Science and Engineering, 166, 825-841, 2018. SUNDARESAN, K. R. & KRISHNASWANY P. R.; Estimation of time delay time constant parameters in time, frequency, and Laplace domains, Can J. Chem. Eng., 56, pp 257, 1977. THOMAS, J. E.; Fundamentos de Engenharia de Petróleo, Interciência, Rio de Janeiro, 2001. TURNEY, D. E.; KALAGA, D. V.; ANSARI, M.; YAKOBOV R.; JOSHI, J. B.; Reform of the drift-flux model of multiphase flow in pipes, wellbores, and reactors vessels, Chemical Engineering Science, 184, 251-258, 2018. VAZ, A.G.R.; ELSINGA, B.; van SARK, W. G. J. H. M.; BRITO, M. C.; An artificial neuronal network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands, Renewable Energy, v. 85, p 631-641, 2016. VEGA, M. P.; OLIVEIRA, G. F. M; FERNANDES, L. D.; MARTINS, A. L.; Monitoring and control strategies to manage pressure fluctuations during oil well drilling, Journal of Petroleum Science and Engineering, v 106, p. 337-349, 2018. VEGA, M. P.; VIEIRA, F. B.; FERNANDES, L. D.; FREITAS, M. G.; RUSSANO, E.; MARTINS, A. J.; Smart monitoring and decision making for regulating annulus bottom hole pressure while drilling oil wells, Brazilian Journal of Chemical Engineering, V. 33, No. 04, pp 969-983, 2016. VEGA, M.; FREITAS, M.; FERNANDES, L.; SCHEID, C.; MARTINS, A.; Best real-time model development of an oil well drilling system, Chemical Engineering Transactions, 26, 561-566 DOI:103303/CET1226094; 2012. VEGA, M. P.; SCHEID, C. M.; CALÇADA, L. A.; MANCINI, M. C.; MARTINS, A. L.; Nonlinear Identification and Model-Based Control of an Oil Well Drilling Process, ESCAPE, 2008. 79 VEGA, M. P.; LIMA, E. L.; PINTO, J. C.; Use of bifurcation analysis form development of nonlinear models for control applications, Chemical Engineering Science, v. 63, nº 21, p. 5129-5140, 2008. VIEIRA, F. R. B.; Controle da pressão anular de fundo durante a perfuração de poços de petróleo, Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Química/UFRRJ, Seropédica, 2009. VIEIRA, F. R. B.; VEGA, M. P; FREITAS, M. G.; FOLSTA, M. G.; ARAÚJO, N. F; GANDELMAN, R.; Controle da pressão de fundo durante a perfuração de poços de petróleo, Boletim técnico da produção de petróleo, Rio de Janeiro, v. 4, p. 275-300, 2009. WALDMANN, A., ANDRADE, A.R., MARTINS A.L., LOMBA, R.F.T., ARAGÃO, A.F.L.; Sobre os mecanismos responsáveis por minimizar a invasão do fluido de perfuração na rocha reservatório - Encontro Nacional de Hidráulica de Perfuração e Completação de Poços de Petróleo e Gás Pedra Azul, Domingos Martins, 2006. YOU, J.; RAHNEMA, H.; Numerical modeling of multiphase steam flowin wellbore, Journal of Petroleum Science and Engineering, 164, 259-277, 2018. ZAHEDI, J.; ROUNAGHI, M. M.; Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange, Physica A, v. 438, p 178 – 187, 2015. ZHANG, S.; LV, J.; YUAN, X.; YIN, S.; BP neural network with genetic algorithm optimization for prediction of geo-stress state from wellbore pressures, International Journal of Computational Intelligence and Applications, 15, 3, 2016. ZHAO, Q.; ZHANG, B.; HU, H.; Novel two-step filtering scheme for a logging-while-drilling system, Computer Physics Communications, 180, 1566-1571, 2009. ZIEGLER, J. G. NICHOLS, N. B. Optimum Settings for Automatic Controllers, Transactions of the A.S.M.E., V. 64, p. 759-768, 1942. ZHOU, J.; NYGAARD, G.; Automatic model-based control scheme for stabilizing pressure during dual-gradient drilling, Journal of Process Control, 21, 1138-1147, 2011. ZHOU, Y.; WOJTANOWICZ, A. K.; LI, X.; MIAO, Y.; CHEN, Y.; Improved model for gas migration velocity of stagnant non-Newtonian fluids in annulus, Journal of Petroleum Science and Engineering, 168, 190-200, 2018.
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