Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition
Case Studies and Code Examples
- 1st Edition - July 13, 2024
- Imprint: Elsevier
- Author: Mohammadali Ahmadi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 1 0 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 1 1 - 9
Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition: Case Studies and Code Examples presents a package for academic researchers and indus… Read more
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Request a sales quoteArtificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition: Case Studies and Code Examples presents a package for academic researchers and industries working on water resources and carbon capture and storage. This book contains fundamental knowledge on artificial intelligence related to oil and gas sustainability and the industry’s pivot to support the energy transition and provides practical applications through case studies and coding flowcharts, addressing gaps and questions raised by academic and industrial partners, including energy engineers, geologists, and environmental scientists. This timely publication provides fundamental and extensive information on advanced AI applications geared to support sustainability and the energy transition for the oil and gas industry.
- Reviews the use and applications of AI in energy transition of the oil and gas sectors
- Provides fundamental knowledge and academic background of artificial intelligence, including practical applications with real-world examples and coding flowcharts
- Showcases the successful implementation of AI in the industry (including geothermal energy)
Researchers, industry professionals, environmental scientists, academicians, and students in fields related to sustainable oil and gas industries and energy transition
- Cover image
- Title page
- Table of Contents
- Copyright
- Chapter 1. Artificial intelligence (AI) overview
- Abstract
- 1.1 Introduction
- 1.2 Types of AI in terms of autonomy level and capabilities
- 1.3 Types of AI in terms of applications
- 1.4 Opportunities and challenges
- 1.5 Summary
- Disclosure
- References
- Chapter 2. Machine learning
- Abstract
- 2.1 Introduction
- 2.2 Types of machine learning
- 2.3 Challenges of ML development in the oil and gas industry
- 2.4 Performance indicators of ML models
- 2.5 Summary
- AI disclosure
- References
- Chapter 3. Classification
- Abstract
- 3.1 Introduction
- 3.2 Statistical methods
- 3.3 Rule-based methods
- 3.4 Instance-based methods
- 3.5 Neural network methods
- 3.6 Ensemble methods
- 3.7 Case studies
- AI disclosure
- References
- Chapter 4. Regression
- Abstract
- 4.1 Introduction
- 4.2 Regression types
- 4.3 Case studies
- 4.4 Current and future challenges
- Disclosure
- References
- Chapter 5. Clustering
- Abstract
- 5.1 Introduction
- 5.2 Types of clustering
- 5.3 Challenges and limitations
- 5.4 Case studies
- AI disclosure
- References
- Chapter 6. Semisupervised learning methods
- Abstract
- 6.1 Introduction
- 6.2 Concepts
- 6.3 Semisupervised learning types
- 6.4 Technical limitations
- 6.5 Case studies
- AI disclosure
- References
- Chapter 7. Artificial neural networks
- Abstract
- 7.1 Introduction
- 7.2 Structure of artificial neural networks
- 7.3 Types of artificial neural networks
- 7.4 Challenges and limitations
- 7.5 Case studies
- AI Disclosure
- References
- Chapter 8. Reinforcement learning
- Abstract
- 8.1 Introduction
- 8.2 Fundamentals of reinforcement learning
- 8.3 Reinforcement learning workflow
- 8.4 Technical limitations
- 8.5 Case studies
- AI disclosure
- References
- Chapter 9. Deep learning
- Abstract
- 9.1 Introduction
- 9.2 Theory of deep learning
- 9.3 Technical limitations
- 9.4 Case studies
- AI disclosure
- References
- Chapter 10. AI applications in energy transition and decarbonization
- Abstract
- 10.1 Introduction
- 10.2 Carbon capture and sequestration
- 10.3 AI applications in oil and gas sustainability
- 10.4 AI in geothermal sustainability
- AI disclosure
- References
- Chapter 11. Future trends
- Abstract
- 11.1 Advancements of artificial intelligence in a more sustainable fossil fuel
- 11.2 Digital Twin
- 11.3 Potential developments in oil and gas industry
- Artificial intelligence disclosure
- References
- Index
- Edition: 1
- Published: July 13, 2024
- No. of pages (Paperback): 516
- No. of pages (eBook): 450
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780443240102
- eBook ISBN: 9780443240119
MA
Mohammadali Ahmadi
Dr. Mohammadali Ahmadi holds a BSc with distinction (Petroleum University of Technology), an MSc (Petroleum University of Technology) in Petroleum Engineering, and an MEng (Memorial University of Newfoundland) in Process Engineering, as well as a Ph.D. in Chemical and Petroleum Engineering from the University of Calgary. He published more than 160 papers in highly-ranked ISI journals and served as an invited speaker and session chair for various conferences worldwide. According to a database published in Elsevier publishing group in collaboration with Stanford University, he was named as a top 2% of the most cited scientists from 2018 to 2022. He was the recipient of multiple prestigious awards and scholarships, such as the Vanier scholarship, Izaak Walton Killam Doctoral Scholarship, the Alberta Innovates Graduate Student Scholarship, and the J. B. Hyne Research Innovation Award. As an Associate Editor, Editorial Board Member, and Advisory Board Member, he has served several international chemical engineering and energy-related journals. His research interests include molecular dynamics (MD) simulation, mathematical modeling, enhanced oil recovery (EOR), thermodynamics, and artificial intelligence and machine learning application in the oil and gas industry.
Affiliations and expertise
Vanier Scholar/Killam Laureate, Reservoir Simulation Research Group, Chemical and Petroleum Engineering Department, Schulich School of Engineering, University of Calgary, CanadaRead Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition on ScienceDirect