Estimation of mechanical properties of rock using artificial intelligence

This article presents the way two artificial intelligence techniques, neural networks and genetic algorithms were combined, for the development of a computational tool used to estimate mechanical properties such as tensile strength, uniaxial compression resistance and resistance to triaxial compress...

Full description

Autores:
Galvis, Laura Viviana
Augusto Ochoa, César
Arguello Fuentes, Henry
Carvajal Jiménez, Jenny Mabel
Calderón Carrillo, Zuly Himelda
Tipo de recurso:
Fecha de publicación:
2011
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
spa
OAI Identifier:
oai:repository.eafit.edu.co:10784/14465
Acceso en línea:
http://hdl.handle.net/10784/14465
Palabra clave:
Artificial Intelligence
Artificial Neural Network
Genetic Algorithm
Petrophysical Properties
Mechanical Properties
Inteligencia Artificial
Red Neuronal Artificial
Algoritmo Genético
Propiedades Petrofísicas
Propiedades Mecánicas
Rights
License
Acceso abierto
id REPOEAFIT2_97e47a0dc9bc13d1b29c524a0fffa103
oai_identifier_str oai:repository.eafit.edu.co:10784/14465
network_acronym_str REPOEAFIT2
network_name_str Repositorio EAFIT
repository_id_str
spelling Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2011-12-012019-11-22T18:55:37Z2011-12-012019-11-22T18:55:37Z2256-43141794-9165http://hdl.handle.net/10784/14465This article presents the way two artificial intelligence techniques, neural networks and genetic algorithms were combined, for the development of a computational tool used to estimate mechanical properties such as tensile strength, uniaxial compression resistance and resistance to triaxial compression in sandstones, from petrophysical properties using test data from the Rock Mechanics Laboratory of the Colombian Petroleum Institute - Ecopetrol SA as training data facilitating the design of non-destructive tests with a certain degree of confidence and leading to cost reduction.Este artículo presenta la forma como fueron combinadas dos técnicas de inteligencia artificial, redes neuronales y algoritmos genéticos, para el desarrollo de una herramienta computacional utilizada para la estimación de propiedades mecánicas tales como la resistencia a la tensión, la resistencia a la compresión uniaxial y la resistencia a la compresión triaxial en areniscas, a partir de propiedades petrofísicas utilizando datos de pruebas del Laboratorio de Mecánica de Rocas del Instituto Colombiano del Petróleo - Ecopetrol S.A. como datos de entrenamiento facilitando el diseño de ensayos no destructivos con cierto grado de confianza y dando lugar a una reducción de costos.application/pdfspaUniversidad EAFIThttp://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/430http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/430Copyright (c) 2011 Laura Viviana Galvis, César Augusto Ochoa, Henry Arguello Fuentes, Jenny Mabel Carvajal Jiménez, Zuly Himelda Calderón CarrilloAcceso abiertohttp://purl.org/coar/access_right/c_abf2instname:Universidad EAFITreponame:Repositorio Institucional Universidad EAFITIngeniería y Ciencia; Vol 7, No 14 (2011)Estimation of mechanical properties of rock using artificial intelligenceEstimación de propiedades mecánicas de roca utilizando inteligencia artificialarticleinfo:eu-repo/semantics/articlepublishedVersioninfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Artificial IntelligenceArtificial Neural NetworkGenetic AlgorithmPetrophysical PropertiesMechanical PropertiesInteligencia ArtificialRed Neuronal ArtificialAlgoritmo GenéticoPropiedades PetrofísicasPropiedades MecánicasGalvis, Laura VivianaAugusto Ochoa, CésarArguello Fuentes, HenryCarvajal Jiménez, Jenny MabelCalderón Carrillo, Zuly HimeldaUniversidad Industrial de Santander (UIS)Ingeniería y Ciencia71483103ing.cienc.ORIGINAL5.pdf5.pdfTexto completo PDFapplication/pdf3575593https://repository.eafit.edu.co/bitstreams/05a93f91-171a-45e6-9096-d812df8b66d4/download3b770aef81476351bc6faa391423deb9MD52articulo.htmlarticulo.htmlTexto completo HTMLtext/html373https://repository.eafit.edu.co/bitstreams/1eb367df-45af-4ac2-b2f7-f8e70df5cc90/download00fbf29b2ac04a39dfa8a5d27c2f107aMD53THUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/46276e92-6fb5-4e37-bd85-3c2ce34a860c/downloadda9b21a5c7e00c7f1127cef8e97035e0MD5110784/14465oai:repository.eafit.edu.co:10784/144652020-03-02 22:10:54.736open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv Estimation of mechanical properties of rock using artificial intelligence
dc.title.spa.fl_str_mv Estimación de propiedades mecánicas de roca utilizando inteligencia artificial
title Estimation of mechanical properties of rock using artificial intelligence
spellingShingle Estimation of mechanical properties of rock using artificial intelligence
Artificial Intelligence
Artificial Neural Network
Genetic Algorithm
Petrophysical Properties
Mechanical Properties
Inteligencia Artificial
Red Neuronal Artificial
Algoritmo Genético
Propiedades Petrofísicas
Propiedades Mecánicas
title_short Estimation of mechanical properties of rock using artificial intelligence
title_full Estimation of mechanical properties of rock using artificial intelligence
title_fullStr Estimation of mechanical properties of rock using artificial intelligence
title_full_unstemmed Estimation of mechanical properties of rock using artificial intelligence
title_sort Estimation of mechanical properties of rock using artificial intelligence
dc.creator.fl_str_mv Galvis, Laura Viviana
Augusto Ochoa, César
Arguello Fuentes, Henry
Carvajal Jiménez, Jenny Mabel
Calderón Carrillo, Zuly Himelda
dc.contributor.author.spa.fl_str_mv Galvis, Laura Viviana
Augusto Ochoa, César
Arguello Fuentes, Henry
Carvajal Jiménez, Jenny Mabel
Calderón Carrillo, Zuly Himelda
dc.contributor.affiliation.spa.fl_str_mv Universidad Industrial de Santander (UIS)
dc.subject.keyword.eng.fl_str_mv Artificial Intelligence
Artificial Neural Network
Genetic Algorithm
Petrophysical Properties
Mechanical Properties
topic Artificial Intelligence
Artificial Neural Network
Genetic Algorithm
Petrophysical Properties
Mechanical Properties
Inteligencia Artificial
Red Neuronal Artificial
Algoritmo Genético
Propiedades Petrofísicas
Propiedades Mecánicas
dc.subject.keyword.spa.fl_str_mv Inteligencia Artificial
Red Neuronal Artificial
Algoritmo Genético
Propiedades Petrofísicas
Propiedades Mecánicas
description This article presents the way two artificial intelligence techniques, neural networks and genetic algorithms were combined, for the development of a computational tool used to estimate mechanical properties such as tensile strength, uniaxial compression resistance and resistance to triaxial compression in sandstones, from petrophysical properties using test data from the Rock Mechanics Laboratory of the Colombian Petroleum Institute - Ecopetrol SA as training data facilitating the design of non-destructive tests with a certain degree of confidence and leading to cost reduction.
publishDate 2011
dc.date.issued.none.fl_str_mv 2011-12-01
dc.date.available.none.fl_str_mv 2019-11-22T18:55:37Z
dc.date.accessioned.none.fl_str_mv 2019-11-22T18:55:37Z
dc.date.none.fl_str_mv 2011-12-01
dc.type.eng.fl_str_mv article
info:eu-repo/semantics/article
publishedVersion
info:eu-repo/semantics/publishedVersion
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.local.spa.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 2256-4314
1794-9165
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/14465
identifier_str_mv 2256-4314
1794-9165
url http://hdl.handle.net/10784/14465
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.isversionof.none.fl_str_mv http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/430
dc.relation.uri.none.fl_str_mv http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/430
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Acceso abierto
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.coverage.spatial.eng.fl_str_mv Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.publisher.spa.fl_str_mv Universidad EAFIT
dc.source.none.fl_str_mv instname:Universidad EAFIT
reponame:Repositorio Institucional Universidad EAFIT
dc.source.spa.fl_str_mv Ingeniería y Ciencia; Vol 7, No 14 (2011)
instname_str Universidad EAFIT
institution Universidad EAFIT
reponame_str Repositorio Institucional Universidad EAFIT
collection Repositorio Institucional Universidad EAFIT
bitstream.url.fl_str_mv https://repository.eafit.edu.co/bitstreams/05a93f91-171a-45e6-9096-d812df8b66d4/download
https://repository.eafit.edu.co/bitstreams/1eb367df-45af-4ac2-b2f7-f8e70df5cc90/download
https://repository.eafit.edu.co/bitstreams/46276e92-6fb5-4e37-bd85-3c2ce34a860c/download
bitstream.checksum.fl_str_mv 3b770aef81476351bc6faa391423deb9
00fbf29b2ac04a39dfa8a5d27c2f107a
da9b21a5c7e00c7f1127cef8e97035e0
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad EAFIT
repository.mail.fl_str_mv repositorio@eafit.edu.co
_version_ 1814110217532604416

Publicaciones similares