Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning
imágenes, ilustraciones, tablas
- Autores:
-
Guerrero Benavides, Christian David
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80331
- Palabra clave:
- 530 - Física::532 - Mecánica de fluidos
550 - Ciencias de la tierra
Viscosidad
Petrofisica
PVT
Machine Learning
Nuclear Magnetic
Regression
Nuclear Magnetic Resonance
Viscosity
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning |
| dc.title.translated.eng.fl_str_mv |
Viscosity predicted model from Resonance Magnetic Log using Machine Learning |
| title |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning |
| spellingShingle |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning 530 - Física::532 - Mecánica de fluidos 550 - Ciencias de la tierra Viscosidad Petrofisica PVT Machine Learning Nuclear Magnetic Regression Nuclear Magnetic Resonance Viscosity |
| title_short |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning |
| title_full |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning |
| title_fullStr |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning |
| title_full_unstemmed |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning |
| title_sort |
Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning |
| dc.creator.fl_str_mv |
Guerrero Benavides, Christian David |
| dc.contributor.advisor.none.fl_str_mv |
Ochoa Gutierrez, Luis Hernan Cundar Paredes, Cristiam David |
| dc.contributor.author.none.fl_str_mv |
Guerrero Benavides, Christian David |
| dc.subject.ddc.spa.fl_str_mv |
530 - Física::532 - Mecánica de fluidos 550 - Ciencias de la tierra |
| topic |
530 - Física::532 - Mecánica de fluidos 550 - Ciencias de la tierra Viscosidad Petrofisica PVT Machine Learning Nuclear Magnetic Regression Nuclear Magnetic Resonance Viscosity |
| dc.subject.proposal.spa.fl_str_mv |
Viscosidad Petrofisica PVT |
| dc.subject.proposal.eng.fl_str_mv |
Machine Learning Nuclear Magnetic Regression Nuclear Magnetic Resonance Viscosity |
| description |
imágenes, ilustraciones, tablas |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-02-14 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-28T19:41:33Z |
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2021-09-28T19:41:33Z |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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Text |
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http://purl.org/redcol/resource_type/TM |
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https://repositorio.unal.edu.co/handle/unal/80331 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/80331 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Mohtadi, M., R. Heidemann, and A. Jeje, An introduction to the properties of fluids and solids. 1984. Yarranton, H., Development of viscosity model for petroleum industry applications. 2013. Alazard, N. and L. Montadert, Oil resources for the next century: What's ahead? Nonrenewable Resources, 1993. 2(3): p. 197-206. Betancourt, S., et al., Avances en las mediciones de las propiedades de los fluidos. Spanish Oilfield Review.. Schlumberger, 2007. Coates, G.R., L. Xiao, and M.G. Prammer, NMR logging: principles and applications. Vol. 234. 1999: Haliburton Energy Services Houston. Dunn, K.-J., D.J. Bergman, and G.A. LaTorraca, Nuclear magnetic resonance: Petrophysical and logging applications. 2002: Elsevier. Morriss, C., et al. Hydrocarbon saturation and viscosity estimation from NMR logging in the Belridge Diatomite. in SPWLA 35th Annual Logging Symposium. 1994. Society of Petrophysicists and Well-Log Analysts. LaTorraca, G., et al. Heavy oil viscosity determination using NMR logs. in SPWLA 40th Annual Logging Symposium. 1999. Society of Petrophysicists and Well-Log Analysts. Yang, Z., Viscosity Evaluation of Heavy Oils from NMR Well Logging. 2011. Jang, J.-S., ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 1993. 23(3): p. 665-685. Speight, J.G., The chemistry and technology of petroleum. 2014: CRC press. Kidnay, A.J., W.R. Parrish, and D.G. McCartney, Fundamentals of natural gas processing. 2011: CRC press. McCain Jr, W., The Properties of Petroleum Fluids, secondedition. Tulsa, Oklahoma: PennWell Publishing Company, 1990. Handbook, B., Standard Guide for Petroleum Measurement Tables1. Okandan, E., Heavy crude oil recovery. Vol. 76. 2012: Springer Science & Business Media. Hein, F.J., Heavy oil and oil (tar) sands in North America: an overview & summary of contributions. Natural Resources Research, 2006. 15(2): p. 67-84. Zéberg-Mikkelsen, C.K., S.E. Quiñones-Cisneros, and E.H. Stenby, Viscosity prediction of hydrocarbon mixtures based on the friction theory. Petroleum science and technology, 2001. 19(7-8): p. 899-909. Ellis, D.V. and J.M. Singer, Well logging for earth scientists. Vol. 692. 2007: Springer. Rops, E., Predicting heavy oil and bitumen viscosity from well logs and calculated seismic properties. 2017, Graduate Studies. Schlumberger, Log interpretation charts. Houston, Texas, USA, 2009. Rider, M. and M. Kennedy, The geological interpretation of well logs: Rider-French Consulting Limited. 2011, Bell and Bain, Glasgow. Bendeck, J., Perfiles eléctricos una herramienta para la evaluación de formaciones. Memorias de Curso ACGGP, Bogotá, Colombia, 1992. Dasgupta, T. and S. Mukherjee, Sediment compaction and applications in petroleum geoscience. 2020: Springer. Schlumberger, Oilfield Glossary. 2020. Shukla, A.K. and J. Wiley, Analytical Characterization Methods for Crude Oil and Related Products. 2018: Wiley Online Library. Bloembergen, N., E.M. Purcell, and R.V. Pound, Relaxation effects in nuclear magnetic resonance absorption. Physical review, 1948. 73(7): p. 679. Freedman, R., Formation evaluation using magnetic resonance logging measurements. 2001, Google Patents. Bryan, J., A. Kantzas, and C. Bellehumeur, Oil-viscosity predictions from low-field NMR measurements. SPE Reservoir Evaluation & Engineering, 2005. 8(01): p. 44-52. Morriss, C., et al., Core analysis by low-field NMR. The log analyst, 1997. 38(02). Bird, R., W. Stewart, and E. Lightfoot, Transport Phenomena, John Wiley & Sons, New York, NY, USA. 2002. Kenyon, W., Petrophysical principles of applications of NMR logging. The Log Analyst, 1997. 38(02). Brown, R., Proton relaxation in crude oils. Nature, 1961. 189(4762): p. 387-388. Vinegar, H. NMR fluid properties: NMR Short Course. in SPWLA 36th Annual Symposium. 1995. Zhang, Y., et al. Oil and gas NMR properties: The light and heavy ends. in SPWLA 43rd Annual Logging Symposium. 2002. Society of Petrophysicists and Well-Log Analysts. Lo, S.-W., et al. Correlations of NMR relaxation time with viscosity, diffusivity, and gas/oil ratio of methane/hydrocarbon mixtures. in SPE Annual Technical Conference and Exhibition. 2000. Society of Petroleum Engineers. Bryan, J., D. Moon, and A. Kantzas, In situ viscosity of oil sands using low field NMR. Journal of Canadian Petroleum Technology, 2005. 44(09). Freedman, R. and N. Heaton, Fluid characterization using nuclear magnetic resonance logging. Petrophysics, 2004. 45(03). Nicot, B., M. Fleury, and J. Leblond. A New Methodology For Better Viscosity Prediction Using Nmr Relaxation. in SPWLA 47th Annual Logging Symposium. 2006. Society of Petrophysicists and Well-Log Analysts. Cheng, Y., et al. Power-law Relationship between the Viscosity of Heavy Oils and NMR Relaxation. in SPWLA 50th Annual Logging Symposium. 2009. Society of Petrophysicists and Well-Log Analysts. Nikravesh, M., Soft computing-based computational intelligent for reservoir characterization. Expert Systems with Applications, 2004. 26(1): p. 19-38. Alpaydin, E., Introduction to machine learning. 2014: MIT press. Tan, P.-N., M. Steinbach, and V. Kumar, Introduction to data mining. 2016: Pearson Education India. Drucker, H., et al. Support vector regression machines. in Advances in neural information processing systems. 1997. Smola, A.J. and B. Schölkopf, A tutorial on support vector regression. Statistics and computing, 2004. 14(3): p. 199-222. Alboudwarej, H, Felix, JJ, Taylor, S (2006) La importancia del petróleo pesado. Oilfield Review 18: 38–59. |
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xvi, 89 páginas |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Geofísica |
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Departamento de Geociencias |
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Facultad de Ciencias |
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Bogotá - Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ochoa Gutierrez, Luis Hernan42361dea7c3cf56533207ccfecfb018dCundar Paredes, Cristiam David2640c7e15949658b6140f52fdf738879Guerrero Benavides, Christian David8081bcc442a21c9b2075a87f69deac382021-09-28T19:41:33Z2021-09-28T19:41:33Z2020-02-14https://repositorio.unal.edu.co/handle/unal/80331Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/imágenes, ilustraciones, tablasLa viscosidad es una propiedad física importante para la simulación del flujo en el medio poroso, producción, transporte y refinación de hidrocarburos. La medición directa de la viscosidad es obtenida mediante pruebas de laboratorio a una muestra de crudo de fondo de pozo. Estas muestras son difíciles de adquirir y las pruebas toman tiempo en realizarse. Por ello, existen diferentes técnicas para estimar la viscosidad, una de ellas mediante la relación empírica con el registro de Resonancia Magnética Nuclear. Este trabajo presenta una metodología para el desarrollo de un modelo predictivo de viscosidad representativo, de acuerdo con las condiciones del yacimiento a partir de mediciones de laboratorio y registros de pozo usando el aprendizaje de máquina. (Texto tomado de la fuente)Viscosity is a very important physical property to simulate how the fluid flows thru the porous space, hydrocarbon production, oil pipe transport and refination. The direct value of viscosity is measure thru lab test to an oil sample from bottom hole. That is why these samples are difficult to get and test takes time to perform; so, there are different techniques to estimate viscosity; one of them is by an empirical relationship of nuclear magnetic resonance log. This research presents a methodology to develop a predictive model to get a representative viscosity value at reservoir conditions from lab measures and petrophysical well logs using Machine Learning methods.MaestríaMagíster en Ciencias - Geofísicaxvi, 89 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - GeofísicaDepartamento de GeocienciasFacultad de CienciasBogotá - ColombiaUniversidad Nacional de Colombia - Sede Bogotá530 - Física::532 - Mecánica de fluidos550 - Ciencias de la tierraViscosidadPetrofisicaPVTMachine LearningNuclear MagneticRegressionNuclear Magnetic ResonanceViscosityModelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine LearningViscosity predicted model from Resonance Magnetic Log using Machine LearningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMMohtadi, M., R. Heidemann, and A. Jeje, An introduction to the properties of fluids and solids. 1984. Yarranton, H., Development of viscosity model for petroleum industry applications. 2013. Alazard, N. and L. Montadert, Oil resources for the next century: What's ahead? Nonrenewable Resources, 1993. 2(3): p. 197-206. Betancourt, S., et al., Avances en las mediciones de las propiedades de los fluidos. Spanish Oilfield Review.. Schlumberger, 2007. Coates, G.R., L. Xiao, and M.G. Prammer, NMR logging: principles and applications. Vol. 234. 1999: Haliburton Energy Services Houston. Dunn, K.-J., D.J. Bergman, and G.A. LaTorraca, Nuclear magnetic resonance: Petrophysical and logging applications. 2002: Elsevier. Morriss, C., et al. Hydrocarbon saturation and viscosity estimation from NMR logging in the Belridge Diatomite. in SPWLA 35th Annual Logging Symposium. 1994. Society of Petrophysicists and Well-Log Analysts. LaTorraca, G., et al. Heavy oil viscosity determination using NMR logs. in SPWLA 40th Annual Logging Symposium. 1999. Society of Petrophysicists and Well-Log Analysts. Yang, Z., Viscosity Evaluation of Heavy Oils from NMR Well Logging. 2011. Jang, J.-S., ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 1993. 23(3): p. 665-685. Speight, J.G., The chemistry and technology of petroleum. 2014: CRC press. Kidnay, A.J., W.R. Parrish, and D.G. McCartney, Fundamentals of natural gas processing. 2011: CRC press. McCain Jr, W., The Properties of Petroleum Fluids, secondedition. Tulsa, Oklahoma: PennWell Publishing Company, 1990. Handbook, B., Standard Guide for Petroleum Measurement Tables1. Okandan, E., Heavy crude oil recovery. Vol. 76. 2012: Springer Science & Business Media. Hein, F.J., Heavy oil and oil (tar) sands in North America: an overview & summary of contributions. Natural Resources Research, 2006. 15(2): p. 67-84. Zéberg-Mikkelsen, C.K., S.E. Quiñones-Cisneros, and E.H. Stenby, Viscosity prediction of hydrocarbon mixtures based on the friction theory. Petroleum science and technology, 2001. 19(7-8): p. 899-909. Ellis, D.V. and J.M. Singer, Well logging for earth scientists. Vol. 692. 2007: Springer. Rops, E., Predicting heavy oil and bitumen viscosity from well logs and calculated seismic properties. 2017, Graduate Studies. Schlumberger, Log interpretation charts. Houston, Texas, USA, 2009. Rider, M. and M. Kennedy, The geological interpretation of well logs: Rider-French Consulting Limited. 2011, Bell and Bain, Glasgow. Bendeck, J., Perfiles eléctricos una herramienta para la evaluación de formaciones. Memorias de Curso ACGGP, Bogotá, Colombia, 1992. Dasgupta, T. and S. Mukherjee, Sediment compaction and applications in petroleum geoscience. 2020: Springer. Schlumberger, Oilfield Glossary. 2020. Shukla, A.K. and J. Wiley, Analytical Characterization Methods for Crude Oil and Related Products. 2018: Wiley Online Library. Bloembergen, N., E.M. Purcell, and R.V. Pound, Relaxation effects in nuclear magnetic resonance absorption. Physical review, 1948. 73(7): p. 679. Freedman, R., Formation evaluation using magnetic resonance logging measurements. 2001, Google Patents. Bryan, J., A. Kantzas, and C. Bellehumeur, Oil-viscosity predictions from low-field NMR measurements. SPE Reservoir Evaluation & Engineering, 2005. 8(01): p. 44-52. Morriss, C., et al., Core analysis by low-field NMR. The log analyst, 1997. 38(02). Bird, R., W. Stewart, and E. Lightfoot, Transport Phenomena, John Wiley & Sons, New York, NY, USA. 2002. Kenyon, W., Petrophysical principles of applications of NMR logging. The Log Analyst, 1997. 38(02). Brown, R., Proton relaxation in crude oils. Nature, 1961. 189(4762): p. 387-388. Vinegar, H. NMR fluid properties: NMR Short Course. in SPWLA 36th Annual Symposium. 1995. Zhang, Y., et al. Oil and gas NMR properties: The light and heavy ends. in SPWLA 43rd Annual Logging Symposium. 2002. Society of Petrophysicists and Well-Log Analysts. Lo, S.-W., et al. Correlations of NMR relaxation time with viscosity, diffusivity, and gas/oil ratio of methane/hydrocarbon mixtures. in SPE Annual Technical Conference and Exhibition. 2000. Society of Petroleum Engineers. Bryan, J., D. Moon, and A. Kantzas, In situ viscosity of oil sands using low field NMR. Journal of Canadian Petroleum Technology, 2005. 44(09). Freedman, R. and N. Heaton, Fluid characterization using nuclear magnetic resonance logging. Petrophysics, 2004. 45(03). Nicot, B., M. Fleury, and J. Leblond. A New Methodology For Better Viscosity Prediction Using Nmr Relaxation. in SPWLA 47th Annual Logging Symposium. 2006. Society of Petrophysicists and Well-Log Analysts. Cheng, Y., et al. Power-law Relationship between the Viscosity of Heavy Oils and NMR Relaxation. in SPWLA 50th Annual Logging Symposium. 2009. Society of Petrophysicists and Well-Log Analysts. Nikravesh, M., Soft computing-based computational intelligent for reservoir characterization. Expert Systems with Applications, 2004. 26(1): p. 19-38. Alpaydin, E., Introduction to machine learning. 2014: MIT press. Tan, P.-N., M. Steinbach, and V. Kumar, Introduction to data mining. 2016: Pearson Education India. Drucker, H., et al. Support vector regression machines. in Advances in neural information processing systems. 1997. Smola, A.J. and B. Schölkopf, A tutorial on support vector regression. Statistics and computing, 2004. 14(3): p. 199-222. Alboudwarej, H, Felix, JJ, Taylor, S (2006) La importancia del petróleo pesado. Oilfield Review 18: 38–59.EstudiantesInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80331/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINALModelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning.pdfModelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning.pdfTesis de Maestria en Geofisicaapplication/pdf13211928https://repositorio.unal.edu.co/bitstream/unal/80331/4/Modelo%20de%20predicci%c3%b3n%20de%20Viscosidad%20a%20partir%20del%20registro%20de%20Resonancia%20magn%c3%a9tica%20Nuclear%20usando%20Machine%20Learning.pdf7604c38100be008c3aaa3919fe91e3bfMD54THUMBNAILModelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning.pdf.jpgModelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine 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