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Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
Published on: January 16, 2018
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Soft measurement of oil-water two-phase flow using a multi-task sequence-based CapsNet
Lei OuYang1, Ningde Jin1, Landi Bai1
1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People's Republic of China.
ISA Transactions
|December 26, 2022
Summary
This study developed an improved CapsNet for predicting oil-water two-phase flow. The enhanced model accurately predicts flow patterns and superficial velocity in large pipes.
Area of Science:
- Multiphase flow dynamics
- Fluid mechanics
- Chemical engineering
Background:
- Accurate measurement of flow parameters is crucial for understanding two-phase flow.
- Predicting superficial velocity in oil-water two-phase flow within large diameter pipes remains a significant challenge due to complex flow structures.
Purpose of the Study:
- To develop an advanced deep learning model for accurate prediction of flow patterns and superficial velocity in vertical upward oil-water two-phase flow.
- To address the challenges posed by high-dimensional, time-varying, and nonlinear characteristics of multiphase flow.
Main Methods:
- Conducted vertical upward oil-water two-phase flow experiments in a 125 mm ID pipe using a vertical multi-electrode array (VMEA) conductance sensor.
- Employed novel data pre-processing (1D to 2D) and information fusion techniques (network channels).
- Optimized a multi-task sequence-based Capsule Network (CapsNet) with attention blocks, residual structures, inception blocks, and an improved dynamic routing algorithm for enhanced performance.
Main Results:
- The proposed multi-task sequence-based CapsNet demonstrated superior performance in both flow pattern classification and superficial velocity prediction compared to its variants and other networks.
- The optimized network architecture and improved dynamic routing algorithm effectively handled the complexities of the two-phase flow data.
- Extensive experiments validated the effectiveness of the improved modules and the overall network.
Conclusions:
- The developed multi-task sequence-based CapsNet shows significant potential for real-world applications in multiphase flow analysis.
- The model offers a robust solution for high-dimensional, time-varying, and nonlinear problems encountered in industrial fluid dynamics.
- This research advances the capabilities of sensor data analysis for precise flow parameter estimation.
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