Dynamic and recursive oil-reservoir proxy using Elman neural networks

In this work, a reservoir simulation approximation model (proxy) based on recurrent artificial neural networks is proposed. This model is intended to obtain rates of oil, gas and water production at time t+1 from the respective production rates, average pressure and water cut at t time and the well...

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Detalles Bibliográficos
Autores principales: Túpac Valdivia, Yván Jesús, Talavera, Álvaro, Rodríguez Rivero, Cristian
Formato: Objeto de conferencia (Conference Object)
Idioma:Inglés (English)
Publicado: Institute of Electrical and Electronics Engineers Inc. 2019
Materias:
Forecasting
Neural networks
Petroleum reservoir engineering
Petroleum reservoirs
Production control
Elman neural network
Optimal operation conditions
Production forecasting
Production forecasts
Proxy
Recurrent artificial neural networks
Recurrent networks
Reservoir simulation
Recurrent neural networks
https://purl.org/pe-repo/ocde/ford#1.02.00
Acceso en línea:http://repositorio.ucsp.edu.pe/handle/UCSP/15802

Internet

http://repositorio.ucsp.edu.pe/handle/UCSP/15802

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