Predicting reservoir quality in sandstones through neural modeling
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Vetor (Online) |
Texto Completo: | https://periodicos.furg.br/vetor/article/view/1337 |
Resumo: | Due to limited understanding of the details of many diagenetic processes, mathematical models become a very useful tool to predict reservoir quality prior to drilling. Porosity prediction is an important component in pre-drill and post-drill evaluation of reservoir quality. In this context, we have developed a mathematical model to predict porosity of sandstones reservoir systems. This model is based on artificial neural networks techniques. We propose a score to quantify their importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regression. The main contribution of this paper is the building of a reduced model just with the most relevant features to porosity prediction. A dataset about Uerê formation sandstone reservoir was investigated. This formation is an important oil exploration target in Solimões Basin, western Brazilian Amazonia. Study results show that progressive enhancement neural network is able to predict porosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction. |
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Predicting reservoir quality in sandstones through neural modelingNeural ModelingSandstones Reservoir QualityPorosity PredictionDue to limited understanding of the details of many diagenetic processes, mathematical models become a very useful tool to predict reservoir quality prior to drilling. Porosity prediction is an important component in pre-drill and post-drill evaluation of reservoir quality. In this context, we have developed a mathematical model to predict porosity of sandstones reservoir systems. This model is based on artificial neural networks techniques. We propose a score to quantify their importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regression. The main contribution of this paper is the building of a reduced model just with the most relevant features to porosity prediction. A dataset about Uerê formation sandstone reservoir was investigated. This formation is an important oil exploration target in Solimões Basin, western Brazilian Amazonia. Study results show that progressive enhancement neural network is able to predict porosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction.Due to limited understanding of the details of many diagenetic processes, mathematical models become a very useful tool to predict reservoir quality prior to drilling. Porosity prediction is an important component in pre-drill and post-drill evaluation of reservoir quality. In this context, we have developed a mathematical model to predict porosity of sandstones reservoir systems. This model is based on artificial neural networks techniques. We propose a score to quantify their importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regression. The main contribution of this paper is the building of a reduced model just with the most relevant features to porosity prediction. A dataset about Uerê formation sandstone reservoir was investigated. This formation is an important oil exploration target in Solimões Basin, western Brazilian Amazonia. Study results show that progressive enhancement neural network is able to predict porosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction.Universidade Federal do Rio Grande2013-01-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.furg.br/vetor/article/view/1337VETOR - Journal of Exact Sciences and Engineering; Vol. 22 No. 1 (2012); 57-70VETOR - Revista de Ciências Exatas e Engenharias; v. 22 n. 1 (2012); 57-702358-34520102-7352reponame:Vetor (Online)instname:Universidade Federal do Rio Grande (FURG)instacron:FURGporhttps://periodicos.furg.br/vetor/article/view/1337/2140Copyright (c) 2014 VETOR - Revista de Ciências Exatas e Engenhariasinfo:eu-repo/semantics/openAccessCamargo, Sandro da SilvaEngel, Paulo Martins2023-03-22T15:42:39Zoai:periodicos.furg.br:article/1337Revistahttps://periodicos.furg.br/vetorPUBhttps://periodicos.furg.br/vetor/oaigmplatt@furg.br2358-34520102-7352opendoar:2023-03-22T15:42:39Vetor (Online) - Universidade Federal do Rio Grande (FURG)false |
dc.title.none.fl_str_mv |
Predicting reservoir quality in sandstones through neural modeling |
title |
Predicting reservoir quality in sandstones through neural modeling |
spellingShingle |
Predicting reservoir quality in sandstones through neural modeling Camargo, Sandro da Silva Neural Modeling Sandstones Reservoir Quality Porosity Prediction |
title_short |
Predicting reservoir quality in sandstones through neural modeling |
title_full |
Predicting reservoir quality in sandstones through neural modeling |
title_fullStr |
Predicting reservoir quality in sandstones through neural modeling |
title_full_unstemmed |
Predicting reservoir quality in sandstones through neural modeling |
title_sort |
Predicting reservoir quality in sandstones through neural modeling |
author |
Camargo, Sandro da Silva |
author_facet |
Camargo, Sandro da Silva Engel, Paulo Martins |
author_role |
author |
author2 |
Engel, Paulo Martins |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Camargo, Sandro da Silva Engel, Paulo Martins |
dc.subject.por.fl_str_mv |
Neural Modeling Sandstones Reservoir Quality Porosity Prediction |
topic |
Neural Modeling Sandstones Reservoir Quality Porosity Prediction |
description |
Due to limited understanding of the details of many diagenetic processes, mathematical models become a very useful tool to predict reservoir quality prior to drilling. Porosity prediction is an important component in pre-drill and post-drill evaluation of reservoir quality. In this context, we have developed a mathematical model to predict porosity of sandstones reservoir systems. This model is based on artificial neural networks techniques. We propose a score to quantify their importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regression. The main contribution of this paper is the building of a reduced model just with the most relevant features to porosity prediction. A dataset about Uerê formation sandstone reservoir was investigated. This formation is an important oil exploration target in Solimões Basin, western Brazilian Amazonia. Study results show that progressive enhancement neural network is able to predict porosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-01-20 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.furg.br/vetor/article/view/1337 |
url |
https://periodicos.furg.br/vetor/article/view/1337 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.furg.br/vetor/article/view/1337/2140 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2014 VETOR - Revista de Ciências Exatas e Engenharias info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2014 VETOR - Revista de Ciências Exatas e Engenharias |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Rio Grande |
publisher.none.fl_str_mv |
Universidade Federal do Rio Grande |
dc.source.none.fl_str_mv |
VETOR - Journal of Exact Sciences and Engineering; Vol. 22 No. 1 (2012); 57-70 VETOR - Revista de Ciências Exatas e Engenharias; v. 22 n. 1 (2012); 57-70 2358-3452 0102-7352 reponame:Vetor (Online) instname:Universidade Federal do Rio Grande (FURG) instacron:FURG |
instname_str |
Universidade Federal do Rio Grande (FURG) |
instacron_str |
FURG |
institution |
FURG |
reponame_str |
Vetor (Online) |
collection |
Vetor (Online) |
repository.name.fl_str_mv |
Vetor (Online) - Universidade Federal do Rio Grande (FURG) |
repository.mail.fl_str_mv |
gmplatt@furg.br |
_version_ |
1797041761139294208 |