Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.

ilustraciones

Autores:: Betancur Soto, Ángela María

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.
dc.title.translated.spa.fl_str_mv	Impacto de la distribución de las facies sedimentarias en la producción de crudo pesado en un campo de la Cuenca Llanos, oriente de Colombia.
title	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.
spellingShingle	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia. 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Geología - Métodos estadísticos Petróleo Litofacies Semilla Incertidumbre Aleatorio Correlación de permeabilidad relativa para crudo pesado Predictibilidad Precisión Lithofacies Seed Uncertainty Random Heavy oil relative permeability correlation Predictability Precision
title_short	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.
title_full	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.
title_fullStr	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.
title_full_unstemmed	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.
title_sort	Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.
dc.creator.fl_str_mv	Betancur Soto, Ángela María
dc.contributor.advisor.none.fl_str_mv	Gutierrez Granados, Zorel Cardona Molina, Agustín
dc.contributor.author.none.fl_str_mv	Betancur Soto, Ángela María
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Geología - Métodos estadísticos Petróleo Litofacies Semilla Incertidumbre Aleatorio Correlación de permeabilidad relativa para crudo pesado Predictibilidad Precisión Lithofacies Seed Uncertainty Random Heavy oil relative permeability correlation Predictability Precision
dc.subject.lemb.none.fl_str_mv	Geología - Métodos estadísticos Petróleo
dc.subject.proposal.spa.fl_str_mv	Litofacies Semilla Incertidumbre Aleatorio Correlación de permeabilidad relativa para crudo pesado Predictibilidad Precisión
dc.subject.proposal.eng.fl_str_mv	Lithofacies Seed Uncertainty Random Heavy oil relative permeability correlation Predictability Precision
description	ilustraciones
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-08-12T20:32:03Z
dc.date.available.none.fl_str_mv	2021-08-12T20:32:03Z
dc.date.issued.none.fl_str_mv	2021-07-01
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79932
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/79932 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Alfaro, C., & Alvarado, I. (2014). Heat Flow Evaluation at Eastern Llanos Sedimentary Basin , Colombia. Archer, J., & Wall, C. (1992). Petroleum Engineering Principles and Practice. London: Graham and Trotman. Bahar, A. (2014, November 18). Integrated 3D Reservoir Modelling-Practical Workshop. Bogotá: Next. Barca, E., Bruno, D., Lay-Ekuakille, A., Maggi, S., & Passarella, G. (2016). Heuristic Rules for a Reliable Variogram Parameters Tuning. Symposium on Environmental Instrumentation and Measurements. Reggio Calabria. Boggs, S. (2006). Principles of Sedimentology and Stratigraphy (Vol. Fourth Edition). (P. Lynch, Ed.) New Jersery: Pearson Prentice Hall. Bohling, G. (2005). Introduction to Geostatistics and Variogram Analysis. Kansas: Kansas Geological Survey. Campos, H., & Mann, P. (2015). Tectonostratigraphic Evolution of the Northern Llanos Foreland Basin of Colombia and Implications for its Hydrocarbon Potential. The American Association of Petroleum Geologists, 517-545. Cannon, S. (2018). Reservoir Modelling: A Practical Guide. Hoboken, Nj: John Wiley & sons, Inc . Cant, D.J. 1992. Subsurface facies analysis. In Facies Models: Response to Sea level Change (Walker, R.G.; James, N.P.; editors). Geological Association of Canada, p. 195-218. Cao, R., Zee, Y. M., & Gomez, E. (2014). Geostatistical Applications in Petroleum Reservoir Modelling. South African Institute of Mining and Metallurgy. Correia, U. M., Batezelli, A., & Pereira Leite, E. (2016). 3-D Geological Modelling: A Siliciclastic Reservoir Case Study from Campos Basin. REM- International Engineering Journal, vol.69 no.4. Ecopetrol, S. (2017). Static Model Rubiales Field. Bogotá: Ecopetrol. G.J, M. (1997). Sampling Space of Uncertainty Through Stochastic Modelling of Geological Facies. Society of petroleum engineers, 273-287. Galloway, W. E., & Hobday, D. K. (1983). Terrigenous Clastic Depositional System. New York: Springer-Verlag. doi:10.1007/978-1-4684-0170-7. García Lugo, R., & Eggenschwiler, M. (2001). How Fluid and Rock Properties Affect Production Rates in a Heavy Oil Reservoir. SPE, SPE69694. Golan, M., & Whitson, C. H. (1991). Well performance, Second Edition. Trondheim: Prentice-Hall, Inc. Henry, C., & Mann, P. (2015). Tectonostratigraphic Evolution of the Northern Llanos Foreland Basin of Colombia and Implications for Its Hydrocarbon Potential. AAPG, 517-546. Hussein, A., Joao, F., Taylor, S., Badry, R., Brough, B., Baker, A., . . . Calvo, R. (2006). Highlighting Heavy Oil. Oilfield Review, 34-53. Isaacs, E. H., & Srivastava, R. M. (1989). Introduction to Applied Geostatistics. New York: Oxford University. James, N. P., & Dalrymple, R. W. (2010). Facies Models 4. Kingtong, Canada: Geological Association of Canada. Jassim M, A., & Mohammed S, A. (2019). Study of Different Geostatistical Methods to Model Formation Porosity (Cast Study of Zubair Formation in Luhais Oil Field). IOP. doi:10.1088/1757-899X/579/1/012031. Jika, H., Onuoha, K., & Dim, C. (2019, October 15). Application of Geostatistics in Facies Modelling of Reservoir E, “Hatch Field” Offshore Niger Delta Basin, Nigeria. Petroleum Exploration and Production Technology. Retrieved from https://doi.org/10.1007/s13202-019-00788-1. Journel, A., Gundeso, R., Gringarten, E., & Yao, T. (1998). Stochastic Modelling of a Fluvial Reservoir: A Comparative Review Algorithm. Journal of Petroleum Science and Engineering, 95-121. Li, Y., Xin, X., Yu, G., Wang, W., Zhang, Z., Zhang, M., . . . Chen, Z. (2017). Non-Newtonian Flow Characteristics of Heavy Oil in the Bohai Bay Oilfield: Experimental and Simulation Studies. MDPI. Madani, N., Biranvand, B., Naderi, A., & Keshavarz, N. (2019). Lithofacies Uncertainty Modelling in a Siliciclastic Reservoir Setting by Incorporating Geological Contacts and Seismic Information. Journal of Petroleum Exploration and Production Technology, 1-16. Mai, A. (2008). Mechanisms of Heavy Oil Recovery by Waterflooding. Phd, Thesis, University of Calgary, Calgary. Mariethoz, G., Renard, P., & Straubhaar, J. (2010). The Direct Sampling Method to Perform Multiple‐Point Geostatistical Simulations. Water Resources Research. Menad, N. A., Noureddine, Z., Hemmati-Sarapardeh, A., Shamshirband, S., Mosavi, A., & Chau, K.-w. (2019). Modelling Temperature Dependency of Oil - Water Relative Permeability in Thermal Enhanced Oil Recovery Processes Using Group Method of Data Handling and Gene Expression Programming. Engineering Applications of Computational Fluid mechanics, 724-743. Navas, G. A. (2016). Modelo Sedimentológico Rubiales. Bogotá: Ecopetrol S.A. Nichols, G. (2009). Sedimentology and Stratigraphy (Vol. Second Edition). United Kingdom: Wiley- Blackwell. Ortiz, J., & Deutsh V, C. (n.d.). Testing Pseudo-random Number Generators. Alberta: Alberta University. Paque, J., & Dercourt, J. (1985). Sedimentary Facies. In D. J. Paquet, Geology Principles & Methods (pp. 195-206). Springer, Dordecht. doi: https://doi.org/10.1007/978-94-009-4956-0_12 Park, H., Scheidt, C., Fenwick, D., Boucher, A., & Caers, J. (2013). History matching and uncertainty quantification of facies models with multiple geological interpretations. Springer. Porta, J. d. (1974). Stratigraphic Lexicon. Colombia:(Part Two). Tertiary and Quaternary. Paris: Centre National de la Reserche Scientifique. Posamentier, H. W., Jervey, M. T., & Vail, P. R. (1988). Eustatic Controls on Clastic Deposition I. Calgary: SEPM (Society of Economic paleontologists and mineralogits). Pyrcz, M., & Deutsch, C. (2014). Geostatistical Reservoir Modelling (Vol. 2). New York, United States of America: Oxford University. Ramirez, R. (2010). Informe Descripción de Corazones. Piedecuesta: Instituto Colombiano del Petróleo. Ramon, G. H. (2002). Introducción a la Geoestadística Teoria y Aplicación. Bogotá: Universidad Nacional de Colombia. Reading, H., & Levell, B. (1996). Controls on the Sedimentary Rock Record. In H. Reading, & H. Reading (Ed.), Sedimentary Environments: Process, Facies and Stratigraphy (Vol. Third edition, pp. 5-36). Wiley. Sarmiento, L. F. (2011). Petroleum Geology of Colombia: Llanos Basin. Medellín: Universidad EAFIT. Schlumberger. (2011). Multipoint and Conditional Facies Modelling. Houston: Schlumberger. Seifert, D., & Jensen, J. L. (1999). Using Sequential Indicator Simulation as a Tool in Reservoir Description: Issues and Uncertainties. In Mathematical Geology (pp. 527-550). Sisinni, V., Villarroel, V., McDougall, N., Victoria, M., Vallez, Y., & Garcia Mojonero, C. (2016). Facies Modelling Described by Probabilistic Patterns Using Multi-Point Statistics: An Application to the K-Field, Libya. AAPG/SEG International Conference & Exhibition. Barcelona. Talabi, O., & Okazawa, T. (2003). Effect of Rate and Viscosity on Gas Mobility during Solution-Gas Drive in Heavy Oils. SPE, SPE84032. Torabi, F., Mosavat, N., & Zarivnyy, O. (2016). Predicting Heavy Oil/Water Relative Permeability Using Modified Corey-Based Correlations. Elsevier, 196-204. Walker, R. G. (2006). Facies Models Revisited. (H. W. Posamentier, & R. G. Walker, Eds.) SEPM (Society for Sedimentary Geology), 1-17. Yang, S., Li, S., Nie, X., & Cao, L. (2016). The Effect of Temperature and Rock Permeability on Oil-Water Relative Permeability Curves of Waxy Crude Oil. International Journal of Engineering Research and Applications, 16-21.
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dc.format.extent.spa.fl_str_mv	160 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Ingeniería de Petróleos
dc.publisher.department.spa.fl_str_mv	Departamento de Procesos y Energía
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	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_abf2Gutierrez Granados, Zorel45c586a3214207c3ec7c4c628fdb016aCardona Molina, Agustín765d759de5d84bc9085321c1634df535600Betancur Soto, Ángela María221fdf90d0c02fdfa3ce28f456c1becf2021-08-12T20:32:03Z2021-08-12T20:32:03Z2021-07-01https://repositorio.unal.edu.co/handle/unal/79932Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesHistorically, lithofacies modeling and its uncertainty has been Aquila’s ankle in achieving reservoir model objectives, as the uncertainty generally is evaluated through variogram sensitivity. Throughout this thesis a new workflow will focused on capturing lithofacies uncertainty and assess its impact on heavy oil production. The workflow combines static and dynamic properties into the three-dimensional grid without performing a dynamic simulation process. Seed is the starting point of a random number generator for geostatistical simulation. In disciplines outside of oil and gas industry this is well understood and researched. However, during geological modeling seeds are fixed as an input parameter, which ignore the effect on unsampled areas. The new proposed methodology assesses the impact of seed number on lithofacies uncertainty distribution. In the dynamic section, the thesis focus on the applicability of Darcy’s equation to QC the static model and proposed a modified heavy oil relative permeability correlation to calculate oil rate directly from static model. 1D analysis shows excellent results in vertical wells which gives confidence on static model. A 3D blind test of the integrated workflow shows precision of the lithofacies and potential oil rate prediction ranging between 50 and 80 % within the ±1 feet window. The results show the importance of seed input on the distribution of properties in unsampled areas, which has been ignored for decades, on reducing the uncertainty on lithofacies distribution which has significant impact on STOIIP, hydrocarbon productivity and sweet spots identification. By including the modified oil relative permeability correlation in the static reservoir modeling workflow, a geomodeler can highlight prospective oil areas (sweet spots) through heat maps. This novel methodology can be implemented to any static reservoir modeling project from dry gas up to heavy oil. (Tomado de la fuente)Históricamente, el modelamiento de las litofacies y su incertidumbre han sido el talón de Aquiles para lograr los objetivos del modelamiento de yacimientos, ya que la incertidumbre generalmente se evalúa a través de la sensibilidad del variograma. A lo largo de esta tesis, un nuevo flujo de trabajo se centrará en capturar la incertidumbre de las litofacies y evaluará su impacto en la producción de crudo pesado. El flujo de trabajo combina propiedades estáticas y dinámicas en la malla tridimensional sin realizar un proceso de simulación dinámica. La semilla es el punto de partida de un generador de números aleatorios para la simulación geoestadística. En disciplinas fuera de la industria del petróleo y el gas, esta está bien entendida e investigada. Sin embargo, durante el modelamiento geológico, las semillas se fijan como un parámetro de entrada, que ignora el efecto en las áreas no muestreadas. La nueva metodología propuesta evalúa el impacto de la semilla en la distribución de la incertidumbre de las litofacies. En la sección dinámica, la tesis se centra en la aplicabilidad de la ecuación de Darcy al control de calidad del modelo estático y se propone una correlación de permeabilidad relativa para petróleo pesado modificada para calcular la tasa de petróleo directamente a partir del modelo estático. El análisis 1D muestra excelentes resultados en pozos verticales, lo que brinda confianza en el modelo estático. Una prueba ciega en el flujo de trabajo 3D integrado muestra la precisión de la predicción de las litofacies y el potencial de la tasa de aceite, que varía entre el 50 y el 80% evaluado dentro de la ventana de ± 1 pie. Los resultados muestran la importancia de la entrada de la semilla en la distribución de propiedades en áreas no muestreadas, la cual ha sido ignorada durante décadas, en la reducción de la incertidumbre en la distribución de litofacies que tiene un impacto significativo en STOIIP, productividad de hidrocarburos e identificación de zonas de hidrocarburo prospectivas. Al incluir la correlación de la permeabilidad relativa del petróleo modificado en el flujo de trabajo de modelamiento de yacimientos estáticos, un geomodelador puede resaltar las posibles áreas de petróleo (Sweet spots) a través de mapas de prospectividad de aceite. Esta nueva metodología se puede implementar en cualquier proyecto de modelamiento de yacimientos estáticos, desde gas seco hasta petróleo pesado. (Tomado de la fuente)MaestríaMagíster en Ingeniería – Ingeniería de Petróleos160 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería de PetróleosDepartamento de Procesos y EnergíaFacultad de MinasMedellínUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaGeología - Métodos estadísticosPetróleoLitofaciesSemillaIncertidumbreAleatorioCorrelación de permeabilidad relativa para crudo pesadoPredictibilidadPrecisiónLithofaciesSeedUncertaintyRandomHeavy oil relative permeability correlationPredictabilityPrecisionSedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.Impacto de la distribución de las facies sedimentarias en la producción de crudo pesado en un campo de la Cuenca Llanos, oriente de Colombia.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAlfaro, C., & Alvarado, I. (2014). Heat Flow Evaluation at Eastern Llanos Sedimentary Basin , Colombia.Archer, J., & Wall, C. (1992). Petroleum Engineering Principles and Practice. London: Graham and Trotman.Bahar, A. (2014, November 18). Integrated 3D Reservoir Modelling-Practical Workshop. Bogotá: Next.Barca, E., Bruno, D., Lay-Ekuakille, A., Maggi, S., & Passarella, G. (2016). Heuristic Rules for a Reliable Variogram Parameters Tuning. Symposium on Environmental Instrumentation and Measurements. Reggio Calabria.Boggs, S. (2006). Principles of Sedimentology and Stratigraphy (Vol. Fourth Edition). (P. Lynch, Ed.) New Jersery: Pearson Prentice Hall.Bohling, G. (2005). Introduction to Geostatistics and Variogram Analysis. Kansas: Kansas Geological Survey.Campos, H., & Mann, P. (2015). Tectonostratigraphic Evolution of the Northern Llanos Foreland Basin of Colombia and Implications for its Hydrocarbon Potential. The American Association of Petroleum Geologists, 517-545.Cannon, S. (2018). Reservoir Modelling: A Practical Guide. Hoboken, Nj: John Wiley & sons, Inc .Cant, D.J. 1992. Subsurface facies analysis. In Facies Models: Response to Sea level Change (Walker, R.G.; James, N.P.; editors). Geological Association of Canada, p. 195-218.Cao, R., Zee, Y. M., & Gomez, E. (2014). Geostatistical Applications in Petroleum Reservoir Modelling. South African Institute of Mining and Metallurgy.Correia, U. M., Batezelli, A., & Pereira Leite, E. (2016). 3-D Geological Modelling: A Siliciclastic Reservoir Case Study from Campos Basin. REM- International Engineering Journal, vol.69 no.4.Ecopetrol, S. (2017). Static Model Rubiales Field. Bogotá: Ecopetrol.G.J, M. (1997). Sampling Space of Uncertainty Through Stochastic Modelling of Geological Facies. Society of petroleum engineers, 273-287.Galloway, W. E., & Hobday, D. K. (1983). Terrigenous Clastic Depositional System. New York: Springer-Verlag. doi:10.1007/978-1-4684-0170-7.García Lugo, R., & Eggenschwiler, M. (2001). How Fluid and Rock Properties Affect Production Rates in a Heavy Oil Reservoir. SPE, SPE69694.Golan, M., & Whitson, C. H. (1991). Well performance, Second Edition. Trondheim: Prentice-Hall, Inc.Henry, C., & Mann, P. (2015). Tectonostratigraphic Evolution of the Northern Llanos Foreland Basin of Colombia and Implications for Its Hydrocarbon Potential. AAPG, 517-546. Hussein, A., Joao, F., Taylor, S., Badry, R., Brough, B., Baker, A., . . . Calvo, R. (2006). Highlighting Heavy Oil. Oilfield Review, 34-53.Isaacs, E. H., & Srivastava, R. M. (1989). Introduction to Applied Geostatistics. New York: Oxford University.James, N. P., & Dalrymple, R. W. (2010). Facies Models 4. Kingtong, Canada: Geological Association of Canada.Jassim M, A., & Mohammed S, A. (2019). Study of Different Geostatistical Methods to Model Formation Porosity (Cast Study of Zubair Formation in Luhais Oil Field). IOP. doi:10.1088/1757-899X/579/1/012031.Jika, H., Onuoha, K., & Dim, C. (2019, October 15). Application of Geostatistics in Facies Modelling of Reservoir E, “Hatch Field” Offshore Niger Delta Basin, Nigeria. Petroleum Exploration and Production Technology. Retrieved from https://doi.org/10.1007/s13202-019-00788-1.Journel, A., Gundeso, R., Gringarten, E., & Yao, T. (1998). Stochastic Modelling of a Fluvial Reservoir: A Comparative Review Algorithm. Journal of Petroleum Science and Engineering, 95-121.Li, Y., Xin, X., Yu, G., Wang, W., Zhang, Z., Zhang, M., . . . Chen, Z. (2017). Non-Newtonian Flow Characteristics of Heavy Oil in the Bohai Bay Oilfield: Experimental and Simulation Studies. MDPI.Madani, N., Biranvand, B., Naderi, A., & Keshavarz, N. (2019). Lithofacies Uncertainty Modelling in a Siliciclastic Reservoir Setting by Incorporating Geological Contacts and Seismic Information. Journal of Petroleum Exploration and Production Technology, 1-16.Mai, A. (2008). Mechanisms of Heavy Oil Recovery by Waterflooding. Phd, Thesis, University of Calgary, Calgary.Mariethoz, G., Renard, P., & Straubhaar, J. (2010). The Direct Sampling Method to Perform Multiple‐Point Geostatistical Simulations. Water Resources Research.Menad, N. A., Noureddine, Z., Hemmati-Sarapardeh, A., Shamshirband, S., Mosavi, A., & Chau, K.-w. (2019). Modelling Temperature Dependency of Oil - Water Relative Permeability in Thermal Enhanced Oil Recovery Processes Using Group Method of Data Handling and Gene Expression Programming. Engineering Applications of Computational Fluid mechanics, 724-743.Navas, G. A. (2016). Modelo Sedimentológico Rubiales. Bogotá: Ecopetrol S.A.Nichols, G. (2009). Sedimentology and Stratigraphy (Vol. Second Edition). United Kingdom: Wiley- Blackwell. Ortiz, J., & Deutsh V, C. (n.d.). Testing Pseudo-random Number Generators. Alberta: Alberta University.Paque, J., & Dercourt, J. (1985). Sedimentary Facies. In D. J. Paquet, Geology Principles & Methods (pp. 195-206). Springer, Dordecht. doi: https://doi.org/10.1007/978-94-009-4956-0_12Park, H., Scheidt, C., Fenwick, D., Boucher, A., & Caers, J. (2013). History matching and uncertainty quantification of facies models with multiple geological interpretations. Springer.Porta, J. d. (1974). Stratigraphic Lexicon. Colombia:(Part Two). Tertiary and Quaternary. Paris: Centre National de la Reserche Scientifique.Posamentier, H. W., Jervey, M. T., & Vail, P. R. (1988). Eustatic Controls on Clastic Deposition I. Calgary: SEPM (Society of Economic paleontologists and mineralogits).Pyrcz, M., & Deutsch, C. (2014). Geostatistical Reservoir Modelling (Vol. 2). New York, United States of America: Oxford University.Ramirez, R. (2010). Informe Descripción de Corazones. Piedecuesta: Instituto Colombiano del Petróleo.Ramon, G. H. (2002). Introducción a la Geoestadística Teoria y Aplicación. Bogotá: Universidad Nacional de Colombia.Reading, H., & Levell, B. (1996). Controls on the Sedimentary Rock Record. In H. 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Sedimentary facies distribution Impact on heavy oil production in a Llanos Basin field, eastern Colombia.

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