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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15802
Title: Dynamic and recursive oil-reservoir proxy using Elman neural networks
Authors: Túpac Valdivia, Yván Jesús
Talavera, Álvaro
Rodríguez Rivero, Cristian
Keywords: 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
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015217277&doi=10.1109%2fANDESCON.2016.7836224&partnerID=40&md5=b0fd3ee7e40c1d167290145723b9a65a
Abstract: 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 operation points to be applied in t + 1. Also, this model is able to follow the dynamics of the reservoir system applying online learning from real production observed values. Also, this model allows perform fast and accurate production forecasting for several steps using a recursive mechanism. This model will be inserted into an oil-production control tool to find the optimal operation conditions within a forecast horizon. The obtained outcomes over the approximation tests indicate the methodology is adequate to perform production forecasts. © 2016 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15802
ISBN: 9781509025312
Appears in Collections:Artículos de investigación

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