Sustainable In-Situ Heavy Oil and Bitumen Recovery

Sustainable In-Situ Heavy Oil and Bitumen Recovery: Techniques, Case Studies, and Environmental Considerations delivers a critical reference for today’s energy engineers who want to gain an accurate understanding of anticipated GHG emissions in heavy oil recovery. Structured to break down every method with introductions, case studies, technical limitations and summaries, this reference gives engineers a look at the latest hybrid approaches needed to tackle heavy oil recoveries while calculating carbon footprints. Starting from basic definitions and rounding out with future challenges, this book will help energy engineers collectively evolve heavy oil recovery with sustainability applications in mind.

• Explains environmental footprint considerations within each recovery method
• Includes the latest hybrid methods such as Hybrid of Air-CO2N2 and Cyclic Steam Stimulation (CSS)
• Bridges practical knowledge through case studies, summaries and remaining technical challenges

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.