IoT for Smart Operations in the Oil and Gas Industry elaborates on how the synergy between state-of-the-art computing platforms, such as Internet of Things (IOT), cloud computing, artificial intelligence, and, in particular, modern machine learning methods, can be harnessed to serve the purpose of a more efficient oil and gas industry. The reference explores the operations performed in each sector of the industry and then introduces the computing platforms and smart technologies that can enhance the operation, lower costs, and lower carbon footprint. Safety and security content is included, in particular, cybersecurity and potential threats to smart oil and gas solutions, focusing on adversarial effects of smart solutions and problems related to the interoperability of human-machine intelligence in the context of the oil and gas industry. Detailed case studies are included throughout to learn and research for further applications. Covering the latest topics and solutions, IoT for Smart Operations in the Oil and Gas Industry delivers a much-needed reference for the engineers and managers to understand modern computing paradigms for Industry 4.0 and the oil and gas industry.
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Razin Farhan Hussain is currently a researcher at the High-Performance Cloud Computing (HPCC) laboratory at the University of Louisiana at Lafayette. His research interest includes efficient utilization of fog computing for Industry 4.0 applications and Deep Neural Network models.
Ali Mokhtari is currently a researcher at the High-Performance Cloud Computing (HPCC) laboratory at the University of Louisiana at Lafayette. His research interest is in deploying Artificial Intelligence (AI) methods in Edge-Cloud systems.
Ali Ghalambor, P.E. is currently an international consultant with more than 45 years of industrial and academic experience. He served as the API Endowed Professor, Head of the Petroleum Engineering Department, and Director of the Energy Institute at the University of Louisiana at Lafayette.
Mohsen Amini Salehi is currently Associate Professor at the School of Computing and Informatics, University of Louisiana at Lafayette. He is the director of High-Performance Cloud Computing (HPCC) laboratory where researchers explore the applications of Cloud and Edge computing in Industry 4.0 use cases.
The oil and gas industry look for ways to utilize machine learning, AI, and more advanced computing technologies to improve their operations. IoT for Smart Operations in the Oil and Gas Industry elaborates on how the synergy between state-of-the-art computing platforms, such as Internet of Things (IOT), cloud computing and artificial intelligence, in particular modern machine learning methods, can be harnessed to serve the purpose of a more efficient oil and gas industry. The reference explores the operations performed in each sector of the industry and then introduces the computing platforms and advanced technologies that can enhance the operation, lower costs, and lower carbon footprint. Safety and security content is included, in particular, cybersecurity and potential threats to smart oil and gas solutions, focusing on adversarial effects of smart solutions, problems resulted from interference of smart solutions, and problems related to the interoperability of human-machine intelligence in the context of oil and gas industry. Detailed case studies are included throughout to learn and research for further application. Covering the latest topics and solutions, IoT for Smart Operations in the Oil and Gas Industry delivers a much-needed reference for the engineer and manager to understand upcoming computing technologies for the oil and gas industry.
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Taschenbuch. Zustand: Neu. IoT for Smart Operations in the Oil and Gas Industry | From Upstream to Downstream | Razin Farhan Hussain (u. a.) | Taschenbuch | Einband - fest (Hardcover) | Englisch | 2022 | Gulf Professional Publishing | EAN 9780323911511 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Artikel-Nr. 120967273
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