Abstract / Description of output
Accurate multiphase flowrate measurement is challenging but vital in the energy industry to monitor the production process. Machine learning has recently emerged as a promising method for estimating multiphase flowrates based on different conventional flow meters. In this paper, we propose a Convolutional Neural Network (CNN)-Long-Short Term Memory (LSTM) model and a Temporal Convolutional Network (TCN) model to estimate the volumetric liquid flowrate of oil/gas/water three-phase flow based on the Venturi tube. The volumetric flowrates of the liquid and gas phase vary from 0.1 - 10 m3/h and 7.6137 - 86.7506 m3/h, respectively. We collected time series sensing data from a Venturi tube installed in a pilot-scale multiphase flow facility and utilized single-phase flowmeters to acquire reference data before mixing. Experimental results suggest that the proposed CNN-LSTM and TCN models can effectively deal with the time series sensing data from the Venturi tube and achieve a good accuracy of multiphase flowrate estimation under different flow conditions. TCN achieves a better accuracy for both liquid and phase flowrate estimation than CNN-LSTM. The results indicate the possibility of leveraging conventional flow meters for multiphase flowrate estimation under various flow conditions.
Original language | English |
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Article number | 103875 |
Number of pages | 11 |
Journal | International Journal of Multiphase Flow |
Volume | 146 |
Early online date | 29 Oct 2021 |
DOIs | |
Publication status | Published - Jan 2022 |
Keywords / Materials (for Non-textual outputs)
- convolutional neural network (CNN)
- Long-Short Term Memory (LSTM)
- Temporal Convolutional Network (TCN)
- multiphase flowrate measurement
- time series data