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

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

Bibliografiset tiedot
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
Päätekijä: Terkina A. K. Angelina Konstantinovna
Muut tekijät: Aksenova N. V. Nataliya Valerievna
Yhteenveto: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
Kieli:englanti
Julkaistu: 2024
Aiheet:
oil and gas production
neural networks
artificial intelligence
machine learning
hydrate formation
temperature, pressure
inhibitor
электронный ресурс
труды учёных ТПУ
Linkit:https://www.kgmtu.ru/wp-content/uploads/2024/05/22.04.2024_april_Kerch-SevastopolMaketEnglish-Conf.pdf#page=152
Aineistotyyppi: Elektroninen Kirjan osa
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=679782

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