Name
Abstract | ||
|---|---|---|
Due to the inherent disturbances associated with flow structures, measurement of the complicated flow parameters in multiphase flows remains a challenging problem of significant importance. The flow dynamical behaviors are still elusive. In this paper, a multichannel complex impedance measurement system is designed to cope with this difficult issue. First, the geometry of the distributed multielectrode impedance sensor is optimized and a matched hardware measurement system is developed. After performance evaluation, a convolutional neural network and long short-term memory based measurement model is formulated for measuring flow parameters with high accuracy. The mean absolute error is only 0.36% for water cut and 0.77% for total flow velocity. Further, from the perspective of Lempel–Ziv complexity and mutual information, the relationship between the diverse flow structures and spatial flow behaviors is explored, leading to a deeper understanding of oil-water flows. All the experimental and analytical results demonstrate that the combination of deep learning and the designed impedance sensor measurement system allows measuring the complicated flow parameters, thereby characterizing the flow structures and behaviors. This opens up a new venue for exploring industrial multiphase flows and serving for an efficient oilfield exploitation as well. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tii.2019.2908211 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Impedance measurement,Electrodes,Impedance,Deep learning,Oils,Sensitivity,Solid modeling | System of measurement,Convolutional neural network,Computer science,Flow (psychology),Control engineering,Multiphase flow,Solid modeling,Mutual information,Artificial intelligence,Flow velocity,Deep learning | Journal |
Volume | Issue | ISSN |
15 | 11 | 1551-3203 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei-Dong Dang | 1 | 25 | 3.60 |
| Zhongke Gao | 2 | 59 | 8.79 |
| Linhua Hou | 3 | 1 | 1.37 |
| Dongmei Lv | 4 | 1 | 1.71 |
| Shuming Qiu | 5 | 1 | 0.36 |
| Guanrong Chen | 6 | 12378 | 1130.81 |