A machine learning approach for virtual flow metering and forecasting

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27 Citationer (Scopus)

Abstrakt

We are concerned with robust and accurate forecasting of multiphase flow rates in wells and pipelines during oil and gas production. In practice, the possibility to physically measure the rates is often limited; besides, it is desirable to estimate future values of multiphase rates based on the previous behavior of the system. In this work, we demonstrate that a Long Short-Term Memory (LSTM) recurrent artificial network is able not only to accurately estimate the multiphase rates at current time (i.e., act as a virtual flow meter), but also to forecast the rates for a sequence of future time instants. For a synthetic severe slugging case, LSTM forecasts compare favorably with the results of hydrodynamical modeling. LSTM results for a synthetic noisy dataset of a variable rate well test show that the model can also successfully forecast multiphase rates for a system with changing flow patterns.

OriginalsprogEngelsk
Sider (fra-til)191-196
Antal sider6
TidsskriftIFAC-PapersOnLine
Vol/bind51
Udgave nummer8
DOI
StatusUdgivet - 1 jan. 2018
Udgivet eksterntJa
Begivenhed3rd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production - Esbjerg, Danmark
Varighed: 30 maj 20181 jun. 2018
Konferencens nummer: 3

Programområde

  • Programområde 3: Energiressourcer

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