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...

Full description

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