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Artificial Intelligence in the Energy Industry: Theory, Case Studies, and Applications

Artificial Intelligence in the Energy Industry: Theory, Case Studies, and Applications


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About the Book

This comprehensive resource bridges the knowledge gap in applying modern artificial intelligence and machine learning methods to the petroleum sector. It explores how AI, ML, and data analytics enhance efficiency, safety, and productivity in operations across various segments of the oil and gas value chain, including exploration, drilling, production, reservoir management, and renewables integration. • Covers theoretical and practical aspects of AI/ML applications, from foundational concepts to advanced techniques. • Features examples and A-to-Z practical workflows, empowering readers to apply what they learn directly to real-world challenges. • Includes 500 international case studies, highlighting real-world successes, challenges, and lessons learned from a global perspective. • Offers insights into emerging technologies like Industry 4.0, digital twins, smart fields, and IoT applications, as well as more traditional areas such as drilling optimization and enhanced recovery operations. • Provides readers with downloadable Python codes for several chapters. With the growing importance of advanced data-driven approaches, this book provides value to both technical professionals looking for hands-on solutions and academics seeking a well-structured textbook for advanced studies.

Table of Contents:
0. Front Matter. 1. Theory and Background on Artificial Intelligences and Machine Learning Methods. 2. AI Applications in Reservoir Characterization, Geology, and Geophysics. 3. AI Applications in Drilling Engineering. 4. AI Applications in Reservoir Engineering Management. 5. AI Applications in Well Completion, Production, Stimulation. 6. AL Applications in Enhanced Recovery. 7. AI Applications in Facilities, Pipelines, Metering. 8. AI Applications in Unconventionals. 9. AI Applications in Fourth Industry Revolution. 10. AI Applications in Renewable Resources. 11. Future Directions in AI Applications in the Energy Industry.

About the Author :
Cenk Temizel is an energy professional with 20 years of international experience in the oil and gas industry. He was a research assistant at Stanford University before joining the industry and is the recipient of the SPE Regional Reservoir Description and Dynamics Award. He received his MSc in Petroleum Engineering from the University of Southern California and BSc from Middle East Technical University - Ankara. Salih Tutun is an AI researcher and faculty member at the Olin Business School, Washington University in St. Louis. His research on Networked Pattern Recognition has been featured by global news organizations. Dr. Tutun also serves as a senior AI consultant on projects on IT artifacts and products for business decision-making and has developed several products, including MDScan, XScanner, NEPAR, SASX, DIA, and DemansCheck. He is an External Advisory Board Member of the SkillMeet project funded under the Horizon Europe program. Celal Hakan Canbaz is a Senior Energy Expert with deep expertise in SCAL analysis, reservoir engineering, and well-testing, gained through both academic and industrial work. He is the co-founder of Climpach Consulting LLC and has led or contributed to over 30 projects, 50 publications, and 3 books. Canbaz has delivered 50+ technical courses on energy, project finance, and sustainability, and holds a U.S. patent in reservoir wettability. Recognized among Azerbaijan’s top 100 scientists, he holds M.Sc. and B.Sc. degrees from Istanbul Technical University and is active in SPE and EAGE. Ekrem Alagoz is a drilling supervisor at Turkish Petroleum Corporation (TPAO). He earned a BS degree from Iskenderun Technical University and an MS degree from The University of Texas at Austin, both in petroleum engineering. He previously worked as a hydraulic fracturing research engineer in the R&D department of TPAO. Emre Can Dundar earned a BS degree from Bülent Ecevit University in Mining Engineering and an MS degree from The West Virginia University in Petroleum and Natural Gas Engineering Department. He is currently the head of the Hydraulic Fracturing Department at Turkish Petroleum Corporation (TPAO), where he is responsible for performing frac design and execution operations. Mufrettin Murat Sari is a Chemistry Professor in the Department of Chemistry & Biochemistry, Texas Tech University, USA. He has more than 25 years of experience in the fields of Materials Chemistry, Applied Biochemistry, Biotechnology, and Nanotechnology. He received a Ph.D. degree from Hacettepe University, Turkey in 2005. He has published around 40 scientific articles, proceedings, books, book chapters, and other publications with hundreds of citations. Previously, he was a teaching assistant at Hacettepe University, researcher at Criminal Department Research Center, Ankara, Turkey, professor at Turkish Military Academy & National Defense University, and visiting professor at Texas A&M University, College Station. Luigi Saputelli is a seasoned Reservoir Engineering Expert Advisor, having worked as a reservoir, drilling, and production engineer for PDVSA, ADNOC, Hess, and Halliburton. Dr. Saputelli is a researcher, lecturer, and active volunteer in the Society of Petroleum Engineers. He holds a PhD in Chemical Engineering from University of Houston. Mehmet Melih Oskay earned his PhD degree in Petroleum Engineering from The University of Texas. He has taught at University of Texas, Louisiana Tech University, Middle East Technical University and King Fahd University of Petroleum and Minerals. His industrial experience covers Shell, Kuwait Oil Company and Kuwait Gulf Oil Company. He has represented the latter two companies at joint operating committees including government representatives for Saudi Arabia and Kuwait.


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Product Details
  • ISBN-13: 9781040441701
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: CRC Press
  • Language: English
  • ISBN-10: 104044170X
  • Publisher Date: 22 Jan 2026
  • Binding: Digital (delivered electronically)
  • Sub Title: Theory, Case Studies, and Applications


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