Intelligent prediction of hydrate induction time in oil–water emulsion system based on data-driven and driving force
•A data augmentation method is proposed for hydrate induction time.•A GBRT model for hydrate induction time prediction based on data augmentation is proposed.•The model has good accuracy and overcomes the disadvantages caused by small samples.•An empirical equation for hydrate induction time based o...
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| Published in | Chemical engineering science Vol. 307; p. 121305 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.03.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0009-2509 |
| DOI | 10.1016/j.ces.2025.121305 |
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| Abstract | •A data augmentation method is proposed for hydrate induction time.•A GBRT model for hydrate induction time prediction based on data augmentation is proposed.•The model has good accuracy and overcomes the disadvantages caused by small samples.•An empirical equation for hydrate induction time based on driving force is proposed.•The empirical equation has good physical consistency and can be used as a reference for field production.
The prevention of natural gas hydrates is critical to oil and gas flow assurance. The nucleation process of hydrates has always been a research hotspot, yet its randomness makes the induction time of hydrates difficult to predict. To address this issue, this paper uses a Noise Injection Target Autoencoder (NITAE) to augment data, followed by a GBRT model for predicting hydrate induction time. Finally, the gplearn method is employed to generate an empirical equation for the hydrate induction time. The GBRT model achieves an R2 of 0.9858, with an absolute error within ±0.02, addressing poor prediction performance due to data scarcity. The gplearn-based empirical equation achieves an R2 of 0.8353, with an error within ±20 %. These results provide a new direction for predicting the hydrate formation induction time in actual field conditions and the prevention of hydrate formation in oil and gas pipelines. |
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| AbstractList | •A data augmentation method is proposed for hydrate induction time.•A GBRT model for hydrate induction time prediction based on data augmentation is proposed.•The model has good accuracy and overcomes the disadvantages caused by small samples.•An empirical equation for hydrate induction time based on driving force is proposed.•The empirical equation has good physical consistency and can be used as a reference for field production.
The prevention of natural gas hydrates is critical to oil and gas flow assurance. The nucleation process of hydrates has always been a research hotspot, yet its randomness makes the induction time of hydrates difficult to predict. To address this issue, this paper uses a Noise Injection Target Autoencoder (NITAE) to augment data, followed by a GBRT model for predicting hydrate induction time. Finally, the gplearn method is employed to generate an empirical equation for the hydrate induction time. The GBRT model achieves an R2 of 0.9858, with an absolute error within ±0.02, addressing poor prediction performance due to data scarcity. The gplearn-based empirical equation achieves an R2 of 0.8353, with an error within ±20 %. These results provide a new direction for predicting the hydrate formation induction time in actual field conditions and the prevention of hydrate formation in oil and gas pipelines. |
| ArticleNumber | 121305 |
| Author | Duan, Ji-Miao Song, Shang-Fei Ma, Qian-Li Liu, Yang Zhou, Shi-Dong Lv, Xiao-Fang Wang, Chuan-Shuo Li, Xiao-Yan Peng, Ming-Guo Shi, Bo-Hui Chen, Shu-Kai |
| Author_xml | – sequence: 1 givenname: Xiao-Fang surname: Lv fullname: Lv, Xiao-Fang organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 2 givenname: Shu-Kai surname: Chen fullname: Chen, Shu-Kai organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 3 givenname: Yang orcidid: 0000-0002-9897-4019 surname: Liu fullname: Liu, Yang email: liu.y@cczu.edu.cn organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 4 givenname: Ming-Guo surname: Peng fullname: Peng, Ming-Guo organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 5 givenname: Ji-Miao surname: Duan fullname: Duan, Ji-Miao organization: Army Logistics Academy, Chongqing 401331, China – sequence: 6 givenname: Chuan-Shuo surname: Wang fullname: Wang, Chuan-Shuo organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 7 givenname: Qian-Li surname: Ma fullname: Ma, Qian-Li organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 8 givenname: Shi-Dong surname: Zhou fullname: Zhou, Shi-Dong organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 9 givenname: Xiao-Yan surname: Li fullname: Li, Xiao-Yan organization: Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology/Engineering Laboratory for High-Value Utilization of Biomass Waste in Petroleum and Chemical Industries, Changzhou University, Changzhou, Jiangsu 213016, China – sequence: 10 givenname: Bo-Hui surname: Shi fullname: Shi, Bo-Hui organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China – sequence: 11 givenname: Shang-Fei surname: Song fullname: Song, Shang-Fei organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China |
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| Keywords | Driving force Data augmentation Oil-water emulsion Small sample Hydrate induction time Machine learning |
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| Snippet | •A data augmentation method is proposed for hydrate induction time.•A GBRT model for hydrate induction time prediction based on data augmentation is... |
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| SubjectTerms | Data augmentation Driving force Hydrate induction time Machine learning Oil-water emulsion Small sample |
| Title | Intelligent prediction of hydrate induction time in oil–water emulsion system based on data-driven and driving force |
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