A Markovian model for oil wells failure and production losses prediction in an oil field in Colombia

dc.contributor.advisorRiascos Ochoa, Javier
dc.creatorGaviria Olaya, Diana Katherine
dc.date.accessioned2024-08-14T20:21:10Z
dc.date.available2024-08-14T20:21:10Z
dc.date.created2024-06-04
dc.description.abstractLa gestión eficiente de los pozos en los yacimientos petrolíferos es esencial para evitar pérdidas económicas significativas y maximizar la producción de petróleo. Este estudio presenta un modelo estocástico basado en Cadenas de Markov en Tiempo Discreto (DTMCs) para la evolución temporal de los estados de los pozos. En concreto, el modelo estima la dinámica entre los estados de funcionamiento del pozo y los estados de fallo debidos a diferentes causas operativas. Además, se propone un método de Monte Carlo para simular escenarios futuros y prever las pérdidas de producción de petróleo y los posibles resultados económicos negativos derivados de la indisponibilidad de los pozos. El enfoque se aplicó a un campo de explotación petrolífera en Colombia y se validó mediante pruebas estadísticas de las propiedades de los DTMC. El modelo propuesto ofrece beneficios prácticos inmediatos para la industria petrolera en la región estudiada, así como el potencial para su aplicación exitosa en otros campos. Esto proporciona una herramienta valiosa y versátil para la gestión global de la disponibilidad de pozos petrolíferos.spa
dc.description.abstractenglishEfficient management of wells in oil fields is essential to avoid significant economic losses and maximize oil production. This study presents a stochastic model based on Discrete Time Markov Chains (DTMCs) for the temporal evolution of well states. Specifically, the model estimates the dynamics among well working states and failure states due to different operating causes. Moreover, a Monte Carlo method is proposed to simulate future scenarios and forecast oil production losses and potential negative economic performance derived from the unavailability of wells. The approach was applied to an oil development field in Colombia and validated through statistical tests for the DTMCs properties. The proposed model offers immediate practical benefits for the oil industry in the studied region, as well as the potential for successful application in other fields. This provides a valuable and versatile tool for global oil well availability management.spa
dc.format.extent16 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.urihttps://hdl.handle.net/20.500.12010/34948
dc.language.isoengspa
dc.relation.referencesCrawley, G.: Fossil Fuels. World Scientific (2016)spa
dc.relation.referencesKohn, A., Schneider, R.: Markov Chain-Based Reliability Analysis for Automotive Fail-Operational Systems. SAE International Journal of Transportation Safety \textbf{5}(1), 30--38 (2017)spa
dc.relation.referencesArjannikov, T., Diemert, S., Ganti, S., Lampman, C., Wiebe, E.: Using Markov Chains to Model Sensor Network Reliability. Proceedings of the 12th International Conference on Availability, Reliability and Security (2017). \doi{10.1145/3098954.3098979}spa
dc.relation.referencesCochran, J., Murugan, A., Krishnamurthy, V.: Generic Markov models for availability estimation and failure characterization in petroleum refineries. Comput. and Operations Res \textbf{28}(1), (2001)spa
dc.relation.referencesKroese, D., Brereton, T., Botev, Z.: Why the Monte Carlo method is so important today. WIREs Computational Statistics \textbf{6}(1), (2014)spa
dc.relation.referencesAlban, A., Darji, A., Imamura, A., Nakayama, M.: Efficient Monte Carlo methods for estimating failure probabilities. Rel. Eng. and System Saf(2017). \doi{10.1016/j.ress.2017.04.001}spa
dc.relation.referencesGilman, J., Brickey, R., Red, M.: Monte Carlo Techniques for Evaluating Producing Properties. SPE Rocky Mountain Petroleum Technol. Conf. / Low Permeability Reservoirs Symp (1998)spa
dc.relation.referencesSchlumberger Energy Glossary, \url{https://glossary.slb.com/en/terms/n/net_oil_production}, last accessed 2024/05/16.spa
dc.relation.referencesInstituto Argentino del Petróleo y Gas. (2000). Capítulo 10 Tanques de producción y almacenamiento. En C. Albano (Ed.), El abecé del Petróleo y del Gas en el mundo y en la Argentina. (1ª Edición, pp.84-86). IAPG.spa
dc.relation.referencesJ. Yang, H. Gu, y G. Rong. (2010). Supply Chain Optimization for Refinery with Considerations of Operation Mode Changeover and Yield Fluctuations. Industrial \& Engineering Chemistry Research, (49), 276–287.spa
dc.relation.referencesEl País: La producción de petróleo de Colombia cae un 7% por bloqueos en el Meta. https://elpais.com/america-colombia/2024-02-08/ la-produccion-de-petroleo-de-colombia-cae-un-7-por-bloqueos-en-el-meta. html.spa
dc.relation.referencesEl País: El 2023, un año complicado en la Vía al Llano. https://elpais.com/america-colombia/2023-12-27/ el-2023-un-ano-complicado-en-la-via-al-llano.html#?rel=mas.spa
dc.relation.referencesK. Ling y J. He, "Theoretical Bases of Arps Empirical Decline Curves", en Abu Dabi, United Arab Emirates, 16 de mayo de 2024. Abu Dhabi Int. Petroleum Conf. Exhib., 2012.spa
dc.relation.references50minutes.com, Pareto’s Principle: Expand your business with the 80/20 rule. https://books.google.com.co/books?id=QtVmCgAAQBAJ.spa
dc.relation.referencesS. Ross, Introduction to Probability Models, 9ª ed. San Diego, California: Elsevier, 2007spa
dc.relation.referencesG. A. Spedicato, Easy Handling Discrete Time Markov Chains. 2020.spa
dc.relation.referencesP. M. Odell, A. Keaven M., y D. Ralph B-, "Maximum Likelihood Estimation for Interval-Censored Data Using a Weibull-Based Accelerated Failure Time Model", Biometrics, vol. 48, n.º 3, 1992.spa
dc.relation.referencesM. Boyd, "An Introduction to Markov Modeling: Concepts and Uses", en Annu. Rel. Maintainability Symp., United States, 19 de enero de 1998. Anahiem, CA: NASA Ames Res. Center, 1998, p. 7spa
dc.relation.referencesM. Stowasser, "Modelling rain risk: a multi-order Markov chain model approach", J. Risk Finance, vol. 13, n.º 1, pp. 45–60, 2012.spa
dc.relation.referencesM. Dhawalikar, V. Mariappan, P. Srividhya, y K. V., "Multi-state failure phenomenon and analysis using semi-Markov model", Int. J. Qual. & Rel. Manage., vol. 35, n.º 9, 2018.spa
dc.relation.referencesD. Duxbury, "Sunk Cost Effects: The Influences of Instruction and Future Return Estimates", Organizational Behav. Human Decis. Processes, vol. 63, n.º 3, pp. 311–319, 1995.spa
dc.relation.references"What is Brent crude?". Accedido el 20 de mayo de 2024. [En línea]. Disponible: https://www.economist.com/the-economist-explains/ 2018/10/29/what-is-brent-crude?utm_medium=cpc.adword.pd& amp;utm_source=google&ppccampaignID=19495686130& ppcadID=&utm_campaign=a.22brand_pmax&utm_content= conversion.direct-response.anonymous&gad_source=1&gclid= Cj0KCQjw7ZO0BhDYARIsAFttkCh6yxDfsDk7ZNj6siyBRo7IPobsHNPE0DagmuIM-BU_ Kcwee1Vf0w8aAg-dEALw_wcB&gclsrc=aw.dsspa
dc.relation.referencesG. Forrest A., "Oil and Gas Reserves Classification, Estimation, and Evaluation", J Pet Technol, vol. 37, n.º 3, 1985.spa
dc.subjectMonte Carlospa
dc.subjectPetróleo y gasspa
dc.subjectFiabilidadspa
dc.subjectDisponibilidadspa
dc.subjectAnálisis de datosspa
dc.subjectModelos estocásticosspa
dc.subjectValor actual netospa
dc.subject.keywordOil and Gasspa
dc.subject.keywordReliabilityspa
dc.subject.keywordAvailabilityspa
dc.subject.keywordData Analysisspa
dc.subject.keywordStochastic modelsspa
dc.subject.keywordMonte Carlospa
dc.subject.keywordNet Present Valuespa
dc.subject.lembPozos petrolíferos - Gestión
dc.subject.lembModelos estocásticos
dc.subject.lembAnálisis de Monte Carlo
dc.titleA Markovian model for oil wells failure and production losses prediction in an oil field in Colombiaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa

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