Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters

Field information analysis is the main element of reducing costs and improving drilling operations. Therefore, the development of field data analysis tools is one of the ways to improve drilling operations. This paper uses mathematical programming and optimization-based methods to present and review...

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Autores:
Chen, Heng
Duan, Jinying
Ponkratov, Vadim
Grimaldo Guerrero, John William
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8652
Acceso en línea:
https://hdl.handle.net/11323/8652
https://doi.org/10.1016/j.egyr.2021.06.080
https://repositorio.cuc.edu.co/
Palabra clave:
Support vector machine
Rate of penetration
Drilling efficiencies
Weight on bit
Máquina de vectores de soporte
Tasa de penetración
Eficiencias de perforación
Peso de la broca
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_bb6187773b326f4760856fb377ea05cc
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8652
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
dc.title.translated.spa.fl_str_mv Predicción de la tasa de penetración mediante el aprendizaje de la máquina de vectores de soporte de mínimos cuadrados acoplados simulados (CSA_LSSVM) en una formación de hidrocarburos basada en parámetros de perforación
title Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
spellingShingle Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
Support vector machine
Rate of penetration
Drilling efficiencies
Weight on bit
Máquina de vectores de soporte
Tasa de penetración
Eficiencias de perforación
Peso de la broca
title_short Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
title_full Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
title_fullStr Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
title_full_unstemmed Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
title_sort Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
dc.creator.fl_str_mv Chen, Heng
Duan, Jinying
Ponkratov, Vadim
Grimaldo Guerrero, John William
dc.contributor.author.spa.fl_str_mv Chen, Heng
Duan, Jinying
Ponkratov, Vadim
Grimaldo Guerrero, John William
dc.subject.spa.fl_str_mv Support vector machine
Rate of penetration
Drilling efficiencies
Weight on bit
Máquina de vectores de soporte
Tasa de penetración
Eficiencias de perforación
Peso de la broca
topic Support vector machine
Rate of penetration
Drilling efficiencies
Weight on bit
Máquina de vectores de soporte
Tasa de penetración
Eficiencias de perforación
Peso de la broca
description Field information analysis is the main element of reducing costs and improving drilling operations. Therefore, the development of field data analysis tools is one of the ways to improve drilling operations. This paper uses mathematical programming and optimization-based methods to present and review learning models for data classification. A comprehensive multi-objective optimization model is proposed by extracting commonalities and the same philosophy of some of the most popular mathematical optimization models in the last few years. The geometric representation of the model will be to make it easier to understand the characteristics of the proposed model. Then it is shown that a large number of models studied in the past and present are subsets, and exceptional cases of this proposed comprehensive model and how to convert the proposed comprehensive model to these methods will be examined. This seeks to bridge the gap between new multi-objective programming models and the powerful and improved CSA-LSSVM methods presented for classification in data mining and to generalize studies to improve each of these methods.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-09-08T14:57:43Z
dc.date.available.none.fl_str_mv 2021-09-08T14:57:43Z
dc.date.issued.none.fl_str_mv 2021-06-25
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 23524847
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8652
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.egyr.2021.06.080
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 23524847
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8652
https://doi.org/10.1016/j.egyr.2021.06.080
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
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spelling Chen, HengDuan, JinyingPonkratov, VadimGrimaldo Guerrero, John William2021-09-08T14:57:43Z2021-09-08T14:57:43Z2021-06-2523524847https://hdl.handle.net/11323/8652https://doi.org/10.1016/j.egyr.2021.06.080Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Field information analysis is the main element of reducing costs and improving drilling operations. Therefore, the development of field data analysis tools is one of the ways to improve drilling operations. This paper uses mathematical programming and optimization-based methods to present and review learning models for data classification. A comprehensive multi-objective optimization model is proposed by extracting commonalities and the same philosophy of some of the most popular mathematical optimization models in the last few years. The geometric representation of the model will be to make it easier to understand the characteristics of the proposed model. Then it is shown that a large number of models studied in the past and present are subsets, and exceptional cases of this proposed comprehensive model and how to convert the proposed comprehensive model to these methods will be examined. This seeks to bridge the gap between new multi-objective programming models and the powerful and improved CSA-LSSVM methods presented for classification in data mining and to generalize studies to improve each of these methods.El análisis de la información de campo es el elemento principal para reducir costos y mejorar las operaciones de perforación. Por lo tanto, el desarrollo de herramientas de análisis de datos de campo es una de las formas de mejorar las operaciones de perforación. Este artículo utiliza programación matemática y métodos basados ​​en optimización para presentar y revisar modelos de aprendizaje para la clasificación de datos. Se propone un modelo integral de optimización multiobjetivo extrayendo los puntos en común y la misma filosofía de algunos de los modelos matemáticos de optimización más populares en los últimos años. La representación geométrica del modelo servirá para facilitar la comprensión de las características del modelo propuesto. Luego se muestra que una gran cantidad de modelos estudiados en el pasado y el presente son subconjuntos, y se examinarán casos excepcionales de este modelo integral propuesto y cómo convertir el modelo integral propuesto a estos métodos. Esto busca cerrar la brecha entre los nuevos modelos de programación multiobjetivo y los métodos CSA-LSSVM poderosos y mejorados presentados para la clasificación en la minería de datos y generalizar los estudios para mejorar cada uno de estos métodos.Chen, Heng-will be generated-orcid-0000-0002-7498-2460-600Duan, JinyingPonkratov, Vadim-will be generated-orcid-0000-0001-7706-5011-600Grimaldo Guerrero, John William-will be generated-orcid-0000-0002-1632-5374-600application/pdfengEnergy ReportsCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Energy Reportshttps://www.sciencedirect.com/science/article/pii/S2352484721004455?via%3Dihub#!Support vector machineRate of penetrationDrilling efficienciesWeight on bitMáquina de vectores de soporteTasa de penetraciónEficiencias de perforaciónPeso de la brocaPrediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parametersPredicción de la tasa de penetración mediante el aprendizaje de la máquina de vectores de soporte de mínimos cuadrados acoplados simulados (CSA_LSSVM) en una formación de hidrocarburos basada en parámetros de perforaciónArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAbbas, A.K., Al-haideri, N.A., Bashikh, A.A., 2019. 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