Machine Learning Application in Gas Hydrates
The issue of gas hydrates is one of the major concerns in flow assurance industry. In order to study the gas hydrates theroretically, reserachers have developed different thermodynamic and kinetic models to predict the hydrate formation parameÂters. The use of machine learning in gas hydrate inhibi...
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| 主要な著者: | , |
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| フォーマット: | 図書 |
| 出版事項: |
Springer Nature
2023
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| オンライン･アクセス: | http://scholars.utp.edu.my/id/eprint/38044/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174775060&doi=10.1007%2f978-3-031-24231-1_9&partnerID=40&md5=c19804eb70890243ee386974b579db7b |
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| 要約: | The issue of gas hydrates is one of the major concerns in flow assurance industry. In order to study the gas hydrates theroretically, reserachers have developed different thermodynamic and kinetic models to predict the hydrate formation parameÂters. The use of machine learning in gas hydrate inhibition prediction and analysis has become a well-established field of study as computational capability has increased in recent years. In the literature, both supervised and unsupervised learning methods have been applied to predict the gas hydrate parameters. This chapter has discussed the conventional modeling approaches in gas hydrate parameters prediction and the use of machine learning techniques in this field of study. Four different case studies involving the use of machine learning in gas hydrates prediction have also been presented. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. |
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