معرفی
Zeinab Zargar is a Full-time Lecturer at the Department of Petroleum Engineering, University of Houston, affiliated with the Cullen College of Engineering. Her expertise spans reservoir simulation, enhanced oil recovery (EOR), and application of machine learning in reservoir management. She holds a Ph.D. in Petroleum Engineering from the University of Calgary (2017), an M.S. in Mathematics of Scientific and Industrial Computation from the University of Reading (2013), and earlier degrees from Iranian institutions.
Her research focuses on analytical and numerical modeling of reservoir processes, including SAGD optimization, CO2 sequestration, and data-driven reservoir management. Recent work emphasizes integrating machine learning frameworks for injector optimization and reservoir connectivity analysis. She has contributed to case studies on poorly performing reservoirs and CO2-EOR integration.
Dr. Zargar's publications highlight advancements in SAGD well spacing, solvent co-injection strategies, and reservoir simulation validation. Her work bridges traditional reservoir engineering with modern computational methods, addressing challenges in thermal recovery and carbon storage.
Teaching responsibilities include Petroleum Capstone Projects, Deterministic Reserves Estimation, and Origin of Oil and Gas Reservoirs courses. She actively collaborates on industry-relevant projects, emphasizing practical solutions for reservoir performance enhancement.
