E-Book, Englisch, 266 Seiten
Farhan Hussain / Mokhtari / Ghalambor IoT for Smart Operations in the Oil and Gas Industry
1. Auflage 2022
ISBN: 978-0-323-99844-4
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark
From Upstream to Downstream
E-Book, Englisch, 266 Seiten
ISBN: 978-0-323-99844-4
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark
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. - Follows a systematic and categorical taxonomy of the upstream, midstream, and downstream processes paired with cutting-edge technologies, which benefit computer scientists and engineers - Understands advanced computing technologies reducing the costs of existing operations and carbon footprint - Deeply dives into case studies that cover the entire oil and gas spectrum and explain bridges into applications
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.
