A Machine Learning Approach for Virtual Flow Metering and Forecasting

Nikolai Andrianov*

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

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    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. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
    Original languageEnglish
    Book seriesI F A C Workshop Series
    Issue number8
    Pages (from-to)191-196
    Publication statusPublished - 2018
    Event3rd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production - Aalborg University, Esbjerg, Denmark
    Duration: 30 May 20181 Jun 2018
    Conference number: 3


    Conference3rd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production
    LocationAalborg University
    Internet address


    • Artificial neural networks
    • Multiphase flow
    • Severe slugging
    • Well testing


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