Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition

Case Studies and Code Examples
Erscheinungsjahr 2024
ISBN: 978-0-443-24010-2
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

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 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.

Autoren/Hrsg.

Ahmadi, Mohammadali

Fachgebiete

• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Ölförderung, Gasförderung
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Technik Allgemein Nachhaltigkeit, Grüne Technologien
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft Öl- und Gasindustrie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik

Inhaltsverzeichnis

1. Artificial Intelligence (AI) Overview
2. Machine Learning (ML)
3. Classification
4. Regression
5. Clustering
6. Semi Supervised Learning Methods
7. Modern Machine Learning Methods
8. Reinforcement Learning
9. Deep Learning
10. AI Applications in Energy Transition and Decarbonization
11. Future Trends

Appendices
A. Statistical Performance Indexes
B. Python Programming Introduction
C. Case Studies Data Base
D. Structured Query Language (SQL) Basics

Über Autor(innen)

Ahmadi, Mohammadali
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.