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

Authors

Naseem Ali, Portland State UniversityFollow
Bianca Viggiano, Portland State UniversityFollow
Murat Tutkun, , Institute for Energy TechnologyFollow
Raul Bayoan Cal, Portland State UniversityFollow

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.

Locate the Document

https://doi.org/10.1016/j.petrol.2021.108488

DOI

10.1016/j.petrol.2021.108488

Persistent Identifier

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

Citation Details

Ali, N., Viggiano, B., Tutkun, M., & Cal, R. B. (2021). Data-driven machine learning for accurate prediction and statistical quantification of two phase flow regimes. Journal of Petroleum Science and Engineering, 202, 108488. https://doi.org/10.1016/j.petrol.2021.108488