Dynamic and recursive oil-reservoir proxy using Elman neural networks

Yvan Tupac, Alvaro Talavera, Cristian Rodriguez Rivero

Research output: Contribution to conferencePaper

1 Scopus citations

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.
Original languageEnglish
DOIs
StatePublished - 27 Jan 2017
EventProceedings of the 2016 IEEE ANDESCON, ANDESCON 2016 -
Duration: 27 Jan 2017 → …

Conference

ConferenceProceedings of the 2016 IEEE ANDESCON, ANDESCON 2016
Period27/01/17 → …

Keywords

  • Proxy
  • Recurrent Networks
  • Reservoir Simulation

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