Transmissibility Prediction: A Deep Learning Approach

Detalhes bibliográficos
Autor(a) principal: Bayer, Biazi
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/117792
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Transmissibility Prediction: A Deep Learning ApproachMachine LearningNeural NetworkDeep LearningOilWaterRockFractureTransmissibilityDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsTransmissibility is a very important issue in the study of fractured rocks, as it is directly related to the efficiency of drilling and extracting oil wells, water reservoirs, and even gas exploration. In this piece of work, based on data from transmissibility simulations performed in oil fields in Norway, three different machine learning approaches were applied for predicting the transmissibility of fractured rock areas. First, the fracture diagram image was applied in two different Neural Networks architectures: GoogleNet and ResNet. Second, from the fracture diagram image, it was performed a decomposition of all fracture lines (scratches) on each image into X-axis and Y-axis and it was sent to the same two Neural Network architectures on the previous approach (GoogleNet and ResNet). And finally, it was performed a discretizing continuous variable, and applied on neural network ResNet, thus performing a multi-class classification for predictions instead of regression. Overall, this study provides contributions for transmissibility prediction on oil well fields. Creating options to the traditional technique of calculating transmissibility by computer simulation which is very costly and time-consuming.Castelli, MauroVanneschi, LeonardoRUNBayer, Biazi2021-05-17T16:58:52Z2021-05-122021-05-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/117792TID:202737543enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:00:44Zoai:run.unl.pt:10362/117792Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:41.239371Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Transmissibility Prediction: A Deep Learning Approach
title Transmissibility Prediction: A Deep Learning Approach
spellingShingle Transmissibility Prediction: A Deep Learning Approach
Bayer, Biazi
Machine Learning
Neural Network
Deep Learning
Oil
Water
Rock
Fracture
Transmissibility
title_short Transmissibility Prediction: A Deep Learning Approach
title_full Transmissibility Prediction: A Deep Learning Approach
title_fullStr Transmissibility Prediction: A Deep Learning Approach
title_full_unstemmed Transmissibility Prediction: A Deep Learning Approach
title_sort Transmissibility Prediction: A Deep Learning Approach
author Bayer, Biazi
author_facet Bayer, Biazi
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Bayer, Biazi
dc.subject.por.fl_str_mv Machine Learning
Neural Network
Deep Learning
Oil
Water
Rock
Fracture
Transmissibility
topic Machine Learning
Neural Network
Deep Learning
Oil
Water
Rock
Fracture
Transmissibility
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-05-17T16:58:52Z
2021-05-12
2021-05-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/117792
TID:202737543
url http://hdl.handle.net/10362/117792
identifier_str_mv TID:202737543
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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