Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube
In modern society, the oil industry has become the foundation of the world economy, and how to efficiently extract oil is a pressing problem. Among them, the accurate measurement of oil-gas two-phase parameters is one of the bottlenecks in oil extraction technology. It is found that through the expe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/4/1200 |
| _version_ | 1811184592923656192 |
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| author | Zhuoqun Xu Fan Wu Xinmeng Yang Yi Li |
| author_facet | Zhuoqun Xu Fan Wu Xinmeng Yang Yi Li |
| author_sort | Zhuoqun Xu |
| collection | DOAJ |
| description | In modern society, the oil industry has become the foundation of the world economy, and how to efficiently extract oil is a pressing problem. Among them, the accurate measurement of oil-gas two-phase parameters is one of the bottlenecks in oil extraction technology. It is found that through the experiment the flow patterns of the oil-gas two-phase flow will change after passing through the venturi tube with the same flow rates. Under the different oil-gas flow rate, the change will be diverse. Being motivated by the above experiments, we use the dual ECT sensors to collect the capacitance values before and after the venturi tube, respectively. Additionally, we use the linear projection algorithm (LBP) algorithm to reconstruct the image of flow patterns. This paper discusses the relationship between the change of flow patterns and the flow rates. Furthermore, a convolutional neural network (CNN) algorithm is proposed to predict the oil flow rate, gas flow rate, and GVF (gas void fraction, especially referring to sectional gas fraction) of the two-phase flow. We use ElasticNet regression as the loss function to effectively avoid possible overfitting problems. In actual experiments, we compare the Typical-ECT-imaging-based-GVF algorithm and SVM (Support Vector Machine) algorithm with CNN algorithm based on three different ECT datasets. Three different sets of ECT data are used to predict the gas flow rate, oil flow rate, and GVF, and they are respectively using the venturi front-based ECT data only, while using the venturi behind-based ECT data and using both these data. |
| first_indexed | 2024-04-11T13:14:56Z |
| format | Article |
| id | doaj.art-8a8f9c3278d54ce5aeca15a409b34760 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:14:56Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8a8f9c3278d54ce5aeca15a409b347602022-12-22T04:22:25ZengMDPI AGSensors1424-82202020-02-01204120010.3390/s20041200s20041200Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi TubeZhuoqun Xu0Fan Wu1Xinmeng Yang2Yi Li3Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Suzhou, University of Science and Technology of China, Suzhou 215000, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaIn modern society, the oil industry has become the foundation of the world economy, and how to efficiently extract oil is a pressing problem. Among them, the accurate measurement of oil-gas two-phase parameters is one of the bottlenecks in oil extraction technology. It is found that through the experiment the flow patterns of the oil-gas two-phase flow will change after passing through the venturi tube with the same flow rates. Under the different oil-gas flow rate, the change will be diverse. Being motivated by the above experiments, we use the dual ECT sensors to collect the capacitance values before and after the venturi tube, respectively. Additionally, we use the linear projection algorithm (LBP) algorithm to reconstruct the image of flow patterns. This paper discusses the relationship between the change of flow patterns and the flow rates. Furthermore, a convolutional neural network (CNN) algorithm is proposed to predict the oil flow rate, gas flow rate, and GVF (gas void fraction, especially referring to sectional gas fraction) of the two-phase flow. We use ElasticNet regression as the loss function to effectively avoid possible overfitting problems. In actual experiments, we compare the Typical-ECT-imaging-based-GVF algorithm and SVM (Support Vector Machine) algorithm with CNN algorithm based on three different ECT datasets. Three different sets of ECT data are used to predict the gas flow rate, oil flow rate, and GVF, and they are respectively using the venturi front-based ECT data only, while using the venturi behind-based ECT data and using both these data.https://www.mdpi.com/1424-8220/20/4/1200convolutional neural networkoil-gas two-phase flowelectrical capacitance tomography |
| spellingShingle | Zhuoqun Xu Fan Wu Xinmeng Yang Yi Li Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube Sensors convolutional neural network oil-gas two-phase flow electrical capacitance tomography |
| title | Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube |
| title_full | Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube |
| title_fullStr | Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube |
| title_full_unstemmed | Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube |
| title_short | Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube |
| title_sort | measurement of gas oil two phase flow patterns by using cnn algorithm based on dual ect sensors with venturi tube |
| topic | convolutional neural network oil-gas two-phase flow electrical capacitance tomography |
| url | https://www.mdpi.com/1424-8220/20/4/1200 |
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