| 书目名称 | Machine Learning and Flow Assurance in Oil and Gas Production | | 编辑 | Bhajan Lal,Cornelius Borecho Bavoh,Jai Krishna Sah | | 视频video | | | 概述 | Reviews and recommends the use of machine learning in flow assurance models.Provides state-of-the-art knowledge on the use of machine learning in flow assurance industry.Includes in-depth discussion o | | 图书封面 |  | | 描述 | .This book is useful to flow assurance engineers, students, and industries who wish to be flow assurance authorities in the twenty-first-century oil and gas industry..The use of digital or artificial intelligence methods in flow assurance has increased recently to achieve fast results without any thorough training effectively. Generally, flow assurance covers all risks associated with maintaining the flow of oil and gas during any stage in the petroleum industry. Flow assurance in the oil and gas industry covers the anticipation, limitation, and/or prevention of hydrates, wax, asphaltenes, scale, and corrosion during operation. Flow assurance challenges mostly lead to stoppage of production or plugs, damage to pipelines or production facilities, economic losses, and in severe cases blowouts and loss of human lives. A combination of several chemical and non-chemical techniques is mostly used to prevent flow assurance issues in the industry. However, the use of models to anticipate, limit, and/or prevent flow assurance problems is recommended as the best and most suitable practice. The existing proposed flow assurance models on hydrates, wax, asphaltenes, scale, and corrosion managem | | 出版日期 | Book 2023 | | 关键词 | Machine Learning; Flow Assurance; Artificial Intelligence; Oil and Gas; Neural Networks | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-24231-1 | | isbn_softcover | 978-3-031-24233-5 | | isbn_ebook | 978-3-031-24231-1 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 16:12:45
