Data-Driven Machine Learning for Accurate Prediction and Statistical Quantification of Two Phase Flow Regimes
Journal of Petroleum Science and Engineering
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.
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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