Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness

One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial inte...

Full description

Autores:

Alanazi, Abdullah
Alizadeh, Seyed Mehdi
Nurgalieva, Karina
Nesic, Slavko
Grimaldo Guerrero, John William
Abo-Dief, Hala M.
Eftekhari-Zadeh, Ehsan
nazemi, ehsan
Igor, Narozhnyy

Tipo de recurso:

Article of journal

Fecha de publicación:

2022

Institución:

Corporación Universidad de la Costa

Repositorio:

REDICUC - Repositorio CUC

Idioma:

eng

OAI Identifier:

oai:repositorio.cuc.edu.co:11323/9121

Acceso en línea:

https://hdl.handle.net/11323/9121
https://doi.org/10.3390/app12031336
https://repositorio.cuc.edu.co/

Palabra clave:

Artificial intelligence
Feature extraction
Scale thickness
Two-phase flow
MLP neural network

Rights

openAccess

License

Atribución 4.0 Internacional (CC BY 4.0)