Title

Data-Driven Machine Learning for Accurate Prediction and Statistical Quantification of Two Phase Flow Regimes

Published In

Journal of Petroleum Science and Engineering

Document Type

Citation

Publication Date

7-1-2021

Abstract

Two different two-phase flow regimes including slug and dispersed flows are examined through the implementation of system identification methods to attain reduced-order models. The obtained models accurately capture the flow dynamics of the studied regimes. The models also provide state-space frequency by defining the transfer functions. The system identification results are compared with those of the bidirectional neural network to predict the phase fraction of the considered two-phase flows. The result of long short-term memory shows correlations of 91% between the real and predicted phase fractions.

Rights

© 2021 Elsevier B.V. All rights reserved.

DOI

10.1016/j.petrol.2021.108488

Persistent Identifier

https://archives.pdx.edu/ds/psu/36085

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