Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Machine Learning Approach for Virtual Flow Metering and Forecasting   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 realistic noizy dataset
of a variable rate well test show that the model can also successfully forecast
multiphase rates for a system with changing flow patterns.

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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess