Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning

In the face of increasing global energy demand and the need for energy transitions, improved decision-making processes in the oil and gas industry are essential. Waterflooding is a successful method for enhancing oil recovery. Numerical reservoir simulation software are essential tools for evaluatin...

Full description

Autores:
Rodríguez Castelblanco, Astrid Xiomara
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2023
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/68869
Acceso en línea:
http://hdl.handle.net/1992/68869
Palabra clave:
Waterflooding
Optimization
Machine Learning
Deep Learning
Reduced-order models
Diffusivity Equation
Reservoir Engineering
Ingeniería
Rights
openAccess
License
Atribución 4.0 Internacional
id UNIANDES2_8081c1fc67fb0d635be37d0ca2f8aec1
oai_identifier_str oai:repositorio.uniandes.edu.co:1992/68869
network_acronym_str UNIANDES2
network_name_str Séneca: repositorio Uniandes
repository_id_str
dc.title.none.fl_str_mv Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
title Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
spellingShingle Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
Waterflooding
Optimization
Machine Learning
Deep Learning
Reduced-order models
Diffusivity Equation
Reservoir Engineering
Ingeniería
title_short Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
title_full Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
title_fullStr Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
title_full_unstemmed Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
title_sort Decision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learning
dc.creator.fl_str_mv Rodríguez Castelblanco, Astrid Xiomara
dc.contributor.advisor.none.fl_str_mv Medaglia González, Andrés L.
Cabrales Arévalo, Sergio Andrés
dc.contributor.author.none.fl_str_mv Rodríguez Castelblanco, Astrid Xiomara
dc.contributor.jury.none.fl_str_mv Gildin, Eduardo
Gómez Ramírez, Jorge Mario
Calderón, Zuly
dc.contributor.researchgroup.es_CO.fl_str_mv Centro para la Optimización y Probabilidad Aplicada COPA
dc.subject.keyword.none.fl_str_mv Waterflooding
Optimization
Machine Learning
Deep Learning
Reduced-order models
Diffusivity Equation
Reservoir Engineering
topic Waterflooding
Optimization
Machine Learning
Deep Learning
Reduced-order models
Diffusivity Equation
Reservoir Engineering
Ingeniería
dc.subject.themes.es_CO.fl_str_mv Ingeniería
description In the face of increasing global energy demand and the need for energy transitions, improved decision-making processes in the oil and gas industry are essential. Waterflooding is a successful method for enhancing oil recovery. Numerical reservoir simulation software are essential tools for evaluating the waterflooding process before its implementation in a reservoir. However, this software can be expensive, requires extensive information, and it is challenging to calibrate and optimize. To address this issue, we propose a decision-making framework that employs machine learning models and metaheuristics for near-optimal well control management, ultimately achieving maximum profit and effective oilfield management. Our research approach involves the dynamic reservoir evaluation and fluid production forecast using the diffusivity equation as a predictive numerical model. We reduce the computational time and resource consumption integrating a Proper Orthogonal Decomposition (POD) model to the diffusivity equation. With this model we reproduced the historical oil and water production rate based on the operational wells constraints. Due the high nonlinearity of the numerical predictive model and the challenge to incorporate it into the optimization algorithm we use machine learning models to predict oil and water production rates for each well under changing operational well constraints. The evaluated models are long short-term memory models, convolutional neural networks, and a combination of both. These models, along with the financial evaluation, are integrated into a non-linear optimization component. To solve this component, we use an Iterative Local Search, a metaheuristic that allows us to evaluate several scenarios and find a near-optimal solution. The decision variables are the bottom-hole pressure for each producer well and the water injection rate for each injector well. The objective function is to maximize the net present value. Overall, our proposed framework seamlessly combines the accuracy of the numerical predictive models, the computational efficiency of the reduced-order models, the advantages of neural networks, and the search power of metaheuristics. This provides an efficient strategy for waterflooding optimization over the mid and short-term in practice.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-28T14:22:36Z
dc.date.available.none.fl_str_mv 2023-07-28T14:22:36Z
dc.date.issued.none.fl_str_mv 2023-07-26
dc.type.es_CO.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.es_CO.fl_str_mv Text
dc.type.redcol.none.fl_str_mv https://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/1992/68869
dc.identifier.doi.none.fl_str_mv 10.57784/1992/68869
dc.identifier.instname.es_CO.fl_str_mv instname:Universidad de los Andes
dc.identifier.reponame.es_CO.fl_str_mv reponame:Repositorio Institucional Séneca
dc.identifier.repourl.es_CO.fl_str_mv repourl:https://repositorio.uniandes.edu.co/
url http://hdl.handle.net/1992/68869
identifier_str_mv 10.57784/1992/68869
instname:Universidad de los Andes
reponame:Repositorio Institucional Séneca
repourl:https://repositorio.uniandes.edu.co/
dc.language.iso.es_CO.fl_str_mv eng
language eng
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spelling Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Medaglia González, Andrés L.e164ace3-c2fa-421b-9fb0-aed5e0c9de67600Cabrales Arévalo, Sergio Andrésvirtual::4004-1Rodríguez Castelblanco, Astrid Xiomara53747600Gildin, EduardoGómez Ramírez, Jorge MarioCalderón, ZulyCentro para la Optimización y Probabilidad Aplicada COPA2023-07-28T14:22:36Z2023-07-28T14:22:36Z2023-07-26http://hdl.handle.net/1992/6886910.57784/1992/68869instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/In the face of increasing global energy demand and the need for energy transitions, improved decision-making processes in the oil and gas industry are essential. Waterflooding is a successful method for enhancing oil recovery. Numerical reservoir simulation software are essential tools for evaluating the waterflooding process before its implementation in a reservoir. However, this software can be expensive, requires extensive information, and it is challenging to calibrate and optimize. To address this issue, we propose a decision-making framework that employs machine learning models and metaheuristics for near-optimal well control management, ultimately achieving maximum profit and effective oilfield management. Our research approach involves the dynamic reservoir evaluation and fluid production forecast using the diffusivity equation as a predictive numerical model. We reduce the computational time and resource consumption integrating a Proper Orthogonal Decomposition (POD) model to the diffusivity equation. With this model we reproduced the historical oil and water production rate based on the operational wells constraints. Due the high nonlinearity of the numerical predictive model and the challenge to incorporate it into the optimization algorithm we use machine learning models to predict oil and water production rates for each well under changing operational well constraints. The evaluated models are long short-term memory models, convolutional neural networks, and a combination of both. These models, along with the financial evaluation, are integrated into a non-linear optimization component. To solve this component, we use an Iterative Local Search, a metaheuristic that allows us to evaluate several scenarios and find a near-optimal solution. The decision variables are the bottom-hole pressure for each producer well and the water injection rate for each injector well. The objective function is to maximize the net present value. Overall, our proposed framework seamlessly combines the accuracy of the numerical predictive models, the computational efficiency of the reduced-order models, the advantages of neural networks, and the search power of metaheuristics. This provides an efficient strategy for waterflooding optimization over the mid and short-term in practice.Doctor en IngenieríaDoctoradoInvestigación de OperacionesOptimizaciónSimulaciónInteligencia Artificial100 páginasapplication/pdfengUniversidad de los AndesDoctorado en IngenieríaFacultad de IngenieríaDepartamento de Ingeniería IndustrialDecision-making framework to optimize the waterflooding process in an oilfield using reduced-order models and machine learningTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttps://purl.org/redcol/resource_type/TDWaterfloodingOptimizationMachine LearningDeep LearningReduced-order modelsDiffusivity EquationReservoir EngineeringIngenieríaAgencia Nacional de Hidrocarburos (2023). Informe de Reservas y Recursos Contingentes de Hidrocarburos, Corte 31 de Diciembre 2022.Ahmed, T. (2018). Reservoir engineering handbook. In Reservoir Engineering Handbook. https://doi.org/10.1016/C2016-0-04718-6Ahmed, T. (2019). Principles of Waterflooding. In Reservoir Engineering Handbook (pp. 901-1107). Elsevier. https://doi.org/10.1016/b978-0-12-813649-2.00014-1Alagorni, A., Yaacob, Z., & Nour, A. (2015). An Overview of Oil Production Stages: Enhanced Oil Recovery Techniques and Nitrogen Injection. International Journal of Environmental Science and Development, 6, 693-701. https://doi.org/10.7763/IJESD.2015.V6.682Acosta, A. (2017). El recobro mejorado: la tabla de salvación.British Petroleum. (2019). BP Statistical Review of World Energy Statistical Review of World, 68th edition. In The Editor BP Statistical Review of World Energy.Economides, M., Zhu, D., Hill, D., & Ehlig-Economides, C. (2012). Petroleum Production Systems. Pearson.Fragoso, A., Selvan, K., & Aguilera, R. (2018). Breaking a paradigm: Can oil recovery from shales be larger than oil recovery from conventional reservoirs? The answer is yes! Society of Petroleum Engineers - SPE Canada Unconventional Resources Conference, URC 2018, 2018-March. https://doi.org/10.2118/189784-msGrema, A. S. (2014). Optimization of reservoir waterflooding (Issue October). Cranfield University.Grema, A. S., & Cao, Y. (2016). Optimal feedback control of oil reservoir waterflooding processes. International Journal of Automation and Computing, 13(1), 73-80. https://doi.org/10.1007/s11633-015-0909-7Hussein, A. (2023). Oil and Gas Production Operations and Production Fluids. In Essentials of Flow Assurance Solids in Oil and Gas Operations (pp. 1-52). Elsevier. https://doi.org/10.1016/B978-0-323-99118-6.00012-5IEA. (2020). The Oil and Gas Industry in Energy Transitions. https://www.iea.org/reports/the-oil-and-gas-industry-in-energy-transitionsInternational Labour Organization (ILO). (2022). The future of work in the oil and gas industry.Jansen, J. D., Douma, S. D., Brouwer, D. R., Van Den Hof, P. M. J., Bosgra, O. H., & Heemink, A. W. (2009). Closed-loop reservoir management. SPE Reservoir Simulation Symposium Proceedings. https://doi.org/10.3997/1365-2397.2005002Latil, M. (2015). Enhanced oil recovery. https://doi.org/10.1016/B978-0-12-803734-8.00016-3Muggeridge, A., Cockin, A., Webb, K., Frampton, H., Collins, I., Moulds, T., & Salino, P. (2014). Recovery rates, enhanced oil recovery and technological limits. In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Vol. 372, Issue 2006). Royal Society. https://doi.org/10.1098/rsta.2012.0320Naevdal, G., Brouwer, D., & Jansen, J. (n.d.). Water flooding using closed-loop control. Computational Geosciences, 10(1), 37-60.Rafiei, Y. (2014). Improved Oil Production and Waterflood Performance by Water Allocation Management. March, 203.Speight, J. (2016). Introduction to Enhanced Recovery Methods for Heavy Oil and Tar Sands (2nd ed.). Elsevier. https://doi.org/10.1016/C2014-0-01296-8Walid Al Shalabi, E., & Sepehrnoori, K. (2017). 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Improved reservoir management through optimal control and continuous model updating. Proceedings - SPE Annual Technical Conference and Exhibition. https://doi.org/10.2523/90149-msBuckley, S. E., & Leverett, M. C. (1942). Mechanism of Fluid Displacement in Sands. Transactions of the AIME, 146(01), 107-116. https://doi.org/10.2118/942107-GCao, F., Luo, H., & Lake, L. W. (2015). Oil-rate forecast by inferring fractional-flow models from field data with Koval method combined with the capacitance/resistance model. SPE Reservoir Evaluation and Engineering, 18(4), 534-553. https://doi.org/10.2118/173315-PACarrion, J., & Villegas, J. (2022). Optimizacion de producción aplicando técnicas de waterflooding management (WFM) y machine learning en el reservorio "U Inferior" del sector norte del campo Shushufindi-Aguarico-Bloque 57. Universidad Estatal Península de Santa Elena.Chen, C., Yang, M., & Han, X. (2019). SPE-197585-MS Water Flooding Performance Prediction in Layered Reservoir Using Big Data and Artificial Intelligence Algorithms. http://onepetro.org/SPEADIP/proceedings-pdf/19ADIP/3-19ADIP/D032S184R002/1124030/spe-197585-ms.pdfCraig, J., Geffen, T., & Morse, R. (1955). Oil Recovery Performance Of Pattern Gas Or Water Injection Operations From Model Tests. Society of Petroleum Engineers. https://www.onepetro.org/general/SPE-413-G?sort=&start=0&q=CRAIG+F.%2C+GEFFEN+T.%2C+MORSE+R.+Oil+Recovery+Performance+of+pattern+gas+or+water+injection+operations+from+model+tests+&from_year=&peer_reviewed=&published_between=&fromSearchResults=true&to_yeaDNV. (2022). Energy Transition Outlook 2022 . https://www.dnv.com/energy-transition-outlook/download.html?utm_source=Google&utm_medium=Search&utm_campaign=eto22&gclid=Cj0KCQiA0oagBhDHARIsAI-BbgdX2I8j6XAHdsvOSLG1VAeAOlJqdeYi6aTEtpfqWCNJuh8ymp0Yct0aAv3JEALw_wcBDykstra, H., & Parsons, R. L. (1950). 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