Preventing hydrate formation by using artificial intelligence; Recent Achievements and Prospects of Innovations and Technologies; Iss. 3 : Proceedings of the XIII All-Russian Research-to-Practice Conference of Students, Postgraduates and Young Scientists, Kerch, April 22, 2024
| Parent link: | Recent Achievements and Prospects of Innovations and Technologies=Достижения и перспективы инноваций и технологий: научный журнал.— .— Керчь: КГМТУ.— 2712-908X Iss. 3 : Proceedings of the XIII All-Russian Research-to-Practice Conference of Students, Postgraduates and Young Scientists, Kerch, April 22, 2024.— 2024.— Р. 152-158 |
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| Riassunto: | In the oil and gas industry, the prevention of hydrate formation is paramount to ensuring the safety, efficiency, and reliability of production and transportation processes. Hydrate formation, a complex phenomenon influenced by various environmental and fluid properties, poses significant operational challenges and safety risks. To address these challenges, the integration of machine learning techniques has emerged as a promising approach. Machine learning algorithms analyze vast datasets encompassing temperature, pressure, fluid composition, and geological characteristics to predict hydrate formation conditions. By leveraging advanced analytics, operators can proactively identify and mitigate the risk of hydrate formation, optimize production processes, and enhance operational safety. This article explores the application of machine learning in predicting hydrate formation parameters and discusses its implications for the oil and gas industry Текстовый файл |
| Lingua: | inglese |
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2024
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| Accesso online: | https://www.kgmtu.ru/wp-content/uploads/2024/05/22.04.2024_april_Kerch-SevastopolMaketEnglish-Conf.pdf#page=152 |
| Natura: | Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=679782 |
| Riassunto: | In the oil and gas industry, the prevention of hydrate formation is paramount to ensuring the safety, efficiency, and reliability of production and transportation processes. Hydrate formation, a complex phenomenon influenced by various environmental and fluid properties, poses significant operational challenges and safety risks. To address these challenges, the integration of machine learning techniques has emerged as a promising approach. Machine learning algorithms analyze vast datasets encompassing temperature, pressure, fluid composition, and geological characteristics to predict hydrate formation conditions. By leveraging advanced analytics, operators can proactively identify and mitigate the risk of hydrate formation, optimize production processes, and enhance operational safety. This article explores the application of machine learning in predicting hydrate formation parameters and discusses its implications for the oil and gas industry Текстовый файл |
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