BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices

Autores
Macote Yparraguirre, Erick Leonel; Cortés, Farid B.; Lerner, Betiana; Franco, Camilo A.; Perez, Maximiliano Sebastian
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method’s predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.
Fil: Macote Yparraguirre, Erick Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina
Fil: Cortés, Farid B.. Universidad Nacional de Colombia. Sede Medellín. Facultad de Minas. Departamento de Procesos y Energía; Colombia
Fil: Lerner, Betiana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina. Florida International University; Estados Unidos
Fil: Franco, Camilo A.. Universidad Nacional de Colombia. Sede Medellín. Facultad de Minas. Departamento de Procesos y Energía; Colombia
Fil: Perez, Maximiliano Sebastian. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Background subtraction technique
enhanced oil recovery
microfluidic devices
polymeric nanofluids
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/236389
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic DevicesMacote Yparraguirre, Erick LeonelCortés, Farid B.Lerner, BetianaFranco, Camilo A.Perez, Maximiliano SebastianBackground subtraction techniqueenhanced oil recoverymicrofluidic devicespolymeric nanofluidshttps://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method’s predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.Fil: Macote Yparraguirre, Erick Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Cortés, Farid B.. Universidad Nacional de Colombia. Sede Medellín. Facultad de Minas. Departamento de Procesos y Energía; ColombiaFil: Lerner, Betiana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina. Florida International University; Estados UnidosFil: Franco, Camilo A.. Universidad Nacional de Colombia. Sede Medellín. Facultad de Minas. Departamento de Procesos y Energía; ColombiaFil: Perez, Maximiliano Sebastian. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaAmerican Chemical Society2024-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/236389Macote Yparraguirre, Erick Leonel; Cortés, Farid B.; Lerner, Betiana; Franco, Camilo A.; Perez, Maximiliano Sebastian; BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices; American Chemical Society; ACS Omega; 5-2024; 1-122470-13432470-1343CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acsomega.4c00040info:eu-repo/semantics/altIdentifier/doi/10.1021/acsomega.4c00040info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:56:24Zoai:ri.conicet.gov.ar:11336/236389instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:56:24.848CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
title BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
spellingShingle BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
Macote Yparraguirre, Erick Leonel
Background subtraction technique
enhanced oil recovery
microfluidic devices
polymeric nanofluids
title_short BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
title_full BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
title_fullStr BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
title_full_unstemmed BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
title_sort BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices
dc.creator.none.fl_str_mv Macote Yparraguirre, Erick Leonel
Cortés, Farid B.
Lerner, Betiana
Franco, Camilo A.
Perez, Maximiliano Sebastian
author Macote Yparraguirre, Erick Leonel
author_facet Macote Yparraguirre, Erick Leonel
Cortés, Farid B.
Lerner, Betiana
Franco, Camilo A.
Perez, Maximiliano Sebastian
author_role author
author2 Cortés, Farid B.
Lerner, Betiana
Franco, Camilo A.
Perez, Maximiliano Sebastian
author2_role author
author
author
author
dc.subject.none.fl_str_mv Background subtraction technique
enhanced oil recovery
microfluidic devices
polymeric nanofluids
topic Background subtraction technique
enhanced oil recovery
microfluidic devices
polymeric nanofluids
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method’s predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.
Fil: Macote Yparraguirre, Erick Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina
Fil: Cortés, Farid B.. Universidad Nacional de Colombia. Sede Medellín. Facultad de Minas. Departamento de Procesos y Energía; Colombia
Fil: Lerner, Betiana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina. Florida International University; Estados Unidos
Fil: Franco, Camilo A.. Universidad Nacional de Colombia. Sede Medellín. Facultad de Minas. Departamento de Procesos y Energía; Colombia
Fil: Perez, Maximiliano Sebastian. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method’s predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.
publishDate 2024
dc.date.none.fl_str_mv 2024-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/236389
Macote Yparraguirre, Erick Leonel; Cortés, Farid B.; Lerner, Betiana; Franco, Camilo A.; Perez, Maximiliano Sebastian; BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices; American Chemical Society; ACS Omega; 5-2024; 1-12
2470-1343
2470-1343
CONICET Digital
CONICET
url http://hdl.handle.net/11336/236389
identifier_str_mv Macote Yparraguirre, Erick Leonel; Cortés, Farid B.; Lerner, Betiana; Franco, Camilo A.; Perez, Maximiliano Sebastian; BSEO─Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices; American Chemical Society; ACS Omega; 5-2024; 1-12
2470-1343
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acsomega.4c00040
info:eu-repo/semantics/altIdentifier/doi/10.1021/acsomega.4c00040
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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