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

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

dc.creatorChen, Heng
dc.creatorDuan, Jinying
dc.creatorPonkratov, Vadim
dc.creatorGrimaldo Guerrero, John William
dc.date2021-09-08T14:57:43Z
dc.date2021-09-08T14:57:43Z
dc.date2021-06-25
dc.date.accessioned2023-10-03T19:31:30Z
dc.date.available2023-10-03T19:31:30Z
dc.identifier23524847
dc.identifierhttps://hdl.handle.net/11323/8652
dc.identifierhttps://doi.org/10.1016/j.egyr.2021.06.080
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170407
dc.descriptionField 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.
dc.descriptionEl 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherEnergy Reports
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceEnergy Reports
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S2352484721004455?via%3Dihub#!
dc.subjectSupport vector machine
dc.subjectRate of penetration
dc.subjectDrilling efficiencies
dc.subjectWeight on bit
dc.subjectMáquina de vectores de soporte
dc.subjectTasa de penetración
dc.subjectEficiencias de perforación
dc.subjectPeso de la broca
dc.titlePrediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters
dc.titlePredicción 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
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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