Artificial Intelligence in the Energy Industry: 500 Case Studies, One Complete Reference
Artificial intelligence is no longer a future technology for the energy sector — it is an operational one. Seismic data is being interpreted by neural networks. Production forecasting is running on ML models. Predictive maintenance is preventing well failures before they occur. And the same companies managing conventional reservoirs are using AI to optimise their renewable energy assets and smart grid integration.
What has been missing until now is a single, comprehensive reference that covers all of this — with the theory to understand it and the case studies to implement it.
Artificial Intelligence in the Energy Industry: Theory, Case Studies, and Applications (CRC Press, 2026), edited by Cenk Temizel, Salih Tutun, and a team of specialists from Saudi Aramco, national oil companies, and leading engineering institutions, is that reference.
What the Book Delivers
This is not a conceptual survey. With 500 international case studies and A-to-Z practical workflows, the book is structured for direct application by engineers, data scientists, and technical managers working in the energy sector. Coverage spans the full value chain:
- Upstream: seismic interpretation, reservoir characterisation, drilling optimisation, well placement, and production forecasting using AI and ML
- Reservoir management: history matching, decline curve analysis, digital twin development, and physics-informed machine learning
- Operations: predictive maintenance, equipment failure detection, and real-time decision support using large language models
- Renewables and transition: AI integration in renewable energy systems, smart grid management, and the role of AI in supporting decarbonisation strategies
Why This Book in 2026
The energy industry is at an inflection point. The Middle East crisis of early 2026 has refocused attention on production efficiency, reserve optimisation, and operational resilience. Meanwhile, the energy transition continues to demand faster integration of renewables into existing infrastructure. AI is central to both imperatives — and this volume documents how companies worldwide are deploying it, what works, and where the gaps remain.
Who Should Read It
Technical libraries serving petroleum engineering, energy research, and computer science faculties will find this an essential acquisition. It is equally relevant for reservoir engineers, production engineers, data scientists in energy operations, and managers implementing AI at field level. Energy transition researchers and professionals working on the AI-renewable integration will also find specific chapters directly applicable.
Q&A
What makes this book different from other AI in energy publications?
Scale and scope. 500 case studies from real operations worldwide, covering the full energy value chain from upstream to renewables. Most AI-in-energy books are either theoretical or limited to a single application area. This volume covers all of them, with practical workflows.
Does the book address the energy transition?
Yes. Specific chapters cover AI applications in renewable energy integration, smart grid management, and decarbonisation strategy — alongside traditional oil and gas applications.
Who are the editors?
Led by Cenk Temizel (20 years in the energy industry across Saudi Aramco, Halliburton, and Schlumberger; Stanford-trained; SPE Award recipient) and Salih Tutun (AI faculty, Washington University in St. Louis), with contributions from engineers and researchers across national oil companies, engineering schools, and AI research institutions.
Is this suitable for a technical library?
Yes. Designed as a comprehensive reference for petroleum engineering faculties, energy research institutions, and technical libraries serving the oil and gas sector.
Where can this be ordered?
Use the Order Now! button below for direct purchase — credit card or PayPal, price includes worldwide shipping. For institutional purchase orders or invoicing, use the Request a Quote button beside it.