Machine Learning and Flow Assurance in Oil and Gas Production
Lal, Bhajan, Bavoh, Cornelius Borecho, Sahith Sayani, Jai Krishna
- 出版商: Springer
- 出版日期: 2024-03-13
- 售價: $6,380
- 貴賓價: 9.5 折 $6,061
- 語言: 英文
- 頁數: 177
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031242335
- ISBN-13: 9783031242335
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
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 management are challenged with accuracy and precision. They are not also limited by several parametric assumptions. Recently, machine learning methods have gained much attention as best practices for predicting flow assurance issues. Examples of these machine learning models include conventional approaches such as artificial neural network, support vector machine (SVM), least square support vector machine (LSSVM), random forest (RF), and hybrid models. The use of machine learning in flow assurance is growing, and thus, relevant knowledge and guidelines on their application methods and effectiveness are needed for academic, industrial, and research purposes.
In this book, the authors focus on the use and abilities of various machine learning methods in flow assurance. Initially, basic definitions and use of machine learning in flow assurance are discussed in a broader scope within the oil and gas industry. The rest of the chapters discuss the use of machine learning in various flow assurance areas such as hydrates, wax, asphaltenes, scale, and corrosion. Also, the use of machine learning in practical field applications is discussed to understand the practical use of machine learning in flow assurance.
商品描述(中文翻譯)
本書對於流動保證工程師、學生以及希望成為二十一世紀石油和天然氣行業流動保證專家的產業人士非常有用。
最近，數位或人工智慧方法在流動保證中的使用有所增加，以有效地實現快速結果，而無需進行深入的訓練。一般而言，流動保證涵蓋了在石油產業的任何階段維持油氣流動所涉及的所有風險。石油和天然氣行業的流動保證包括在操作過程中對水合物、蠟、瀝青質、結垢和腐蝕的預測、限制和/或預防。流動保證挑戰通常會導致生產停滯或堵塞、管道或生產設施損壞、經濟損失，甚至在嚴重情況下引發噴發和人員傷亡。行業中通常使用多種化學和非化學技術的組合來防止流動保證問題。然而，建議使用模型來預測、限制和/或預防流動保證問題，這被認為是最佳和最合適的做法。現有的水合物、蠟、瀝青質、結垢和腐蝕管理的流動保證模型在準確性和精確性上面臨挑戰，且不僅限於幾個參數假設。最近，機器學習方法因其在預測流動保證問題方面的最佳實踐而受到廣泛關注。這些機器學習模型的例子包括傳統方法，如人工神經網絡、支持向量機（SVM）、最小二乘支持向量機（LSSVM）、隨機森林（RF）和混合模型。機器學習在流動保證中的使用正在增長，因此，對其應用方法和有效性的相關知識和指導對於學術、工業和研究目的來說是必要的。
在本書中，作者專注於各種機器學習方法在流動保證中的使用和能力。最初，將在石油和天然氣行業的更廣泛範疇內討論機器學習的基本定義和在流動保證中的應用。其餘章節將討論機器學習在水合物、蠟、瀝青質、結垢和腐蝕等各個流動保證領域的應用。此外，還將討論機器學習在實際現場應用中的使用，以了解機器學習在流動保證中的實際應用。
作者簡介
Dr. Bhajan Lal, chartered chemist, is a senior lecturer (2013-conti..) in Chemical Engineering Department and a core research member of CO2 Research Centre in Institute of Contaminant Management (ICM) at the Universiti Teknologi PETRONAS-Malaysia. After receiving M.Sc., Ph.D. degree (Physical Chemistry) in 2004 from JMI Central University, New Delhi, India, Dr. Lal worked as a postdoc fellow and research scientist in USA, Canada, South Africa, Turkey, and Malaysia (2004-2013). His main areas of research interests are CO2 hydrates and its application in CO2 capture and storage, desalination, and flow assurance. He graduated 6 M.Sc., 5 Ph.D., 2 postdocs, and more than 60 chemical engineering undergrads FYPI students since 2013. Dr. Lal has published 4 books related to oil and gas industry, 120 peer-reviewed journal papers, 50 conference papers, and 4 book chapters (H-index 36, i-10 index 93, no of being cited 3825). In addition, as a project leader, he has secured 18 gas hydrate-related research projects worth RM 3.4 million from oil and gas industries, UTP, and Malaysian Government. He achieved Gold Medal in ITEX2021, EREKA 2022 -Malaysia for his novel gas hydrate-based desalination product exhibition. He delivered customized online/face-to-face short courses in flow assurance, machine learning, and gas hydrate related to oil and gas industry.
Cornelius Borecho Bavoh is a researcher and academician. He worked at the Phase Separation Laboratory of the Research Center for CO2 Capture (RCCO2C) in the Chemical Engineering Department of Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia. He has more than 5 years of experience in tutoring and research at the Universiti Teknologi PETRONAS, Malaysia. He has worked on several industrial and fundamental projects related to drilling, gas hydrate, and CO2 capture and separation, and published more than 30 scientific papers in peer-reviewed journals, conferences, and book chapters. Bavoh holds Ph.D. and M.Sc. degrees in chemical engineering and B.Sc. (Hons) degree in petroleum engineering. His research expertise includes thermodynamics and kinetics of reservoir fluid phase modeling and characterization, gas hydrate in flow assurance, production of naturally deposited methane hydrates, and application of gas hydrate-based technology in CO2 capture and natural gas storage and transportation, drilling fluid technology, rheology, and cuttings transport. Bavoh is a member of the Society of Petroleum Engineers (SPE).
Dr. Jai Krishna Sahith graduated in mechanical engineering department from University Technology PETRONAS, Malaysia, and currently doing postdoc in University College Dublin (UCD). Previously, he completed his masters from Jawaharlal Nehru Technological University Kakinada, India. He filed the IP and patent on gas hydrate-related applications. At present, he is working on gas hydrate-based desalination process. He has published hisresearch work in peer-reviewed journals and book chapters.
作者簡介(中文翻譯)
Dr. Bhajan Lal，特許化學家，現任馬來西亞國立石油科技大學（Universiti Teknologi PETRONAS）化學工程系的高級講師（2013年至今）及污染物管理研究所（Institute of Contaminant Management, ICM）CO2研究中心的核心研究成員。2004年，他在印度新德里的JMI中央大學獲得物理化學碩士及博士學位後，曾在美國、加拿大、南非、土耳其及馬來西亞擔任博士後研究員及研究科學家（2004-2013）。他的主要研究興趣包括CO2水合物及其在CO2捕集與儲存、海水淡化及流動保障中的應用。自2013年以來，他指導了6名碩士生、5名博士生、2名博士後及超過60名化學工程本科生。Dr. Lal已出版4本與石油和天然氣行業相關的書籍，發表了120篇同行評審的期刊論文、50篇會議論文及4章書籍（H指數36，i-10指數93，被引用次數3825）。此外，作為項目負責人，他從石油和天然氣行業、UTP及馬來西亞政府獲得了價值340萬馬幣的18個與氣體水合物相關的研究項目。他在ITEX2021及EREKA 2022 -馬來西亞的展覽中獲得金獎，展示了其新穎的氣體水合物基海水淡化產品。他還提供了與石油和天然氣行業相關的流動保障、機器學習及氣體水合物的定制線上/面對面短期課程。
Cornelius Borecho Bavoh是一名研究人員及學者。他在馬來西亞霹靂州Seri Iskandar的國立石油科技大學（Universiti Teknologi PETRONAS, UTP）化學工程系的CO2捕集研究中心（RCCO2C）相分離實驗室工作，擁有超過5年的教學及研究經驗。他參與了多個與鑽探、氣體水合物及CO2捕集與分離相關的工業及基礎項目，並在同行評審的期刊、會議及書籍中發表了超過30篇科學論文。Bavoh擁有化學工程的博士及碩士學位，以及石油工程的榮譽學士學位。他的研究專長包括儲層流體相建模及特徵的熱力學及動力學、流動保障中的氣體水合物、自然沉積甲烷水合物的生產，以及氣體水合物技術在CO2捕集及天然氣儲存與運輸、鑽井液技術、流變學及切削物運輸中的應用。Bavoh是石油工程師學會（Society of Petroleum Engineers, SPE）的成員。
Dr. Jai Krishna Sahith畢業於馬來西亞國立石油科技大學的機械工程系，目前在都柏林大學（University College Dublin, UCD）進行博士後研究。他之前在印度的Jawaharlal Nehru科技大學Kakinada完成碩士學位。他已就氣體水合物相關應用申請了知識產權及專利。目前，他正在研究基於氣體水合物的海水淡化過程，並已在同行評審的期刊及書籍中發表了他的研究成果。
