Understanding reservoir behavior is crucial for maximizing hydrocarbon extraction while maintaining economic and environmental balance. Reservoir engineering & enhanced oil recovery techniques focus on optimizing production through advanced modeling, fluid dynamics, and innovative recovery strategies. Methods such as chemical injection, microbial EOR, and thermal recovery are enhancing yields from declining fields. Digital twins and real-time monitoring allow for predictive decision-making, improving efficiency and reducing operational risks. The integration of artificial intelligence and machine learning is revolutionizing subsurface analysis, enabling more precise recovery strategies. As energy demand grows, advancements in reservoir engineering continue to play a vital role in extending the life of existing fields while ensuring sustainable production.
Selim Sanad Shaker
Geopressure Analysis Services, United States
Saim Memon
Sanyou London Pvt Ltd, United Kingdom
Cleveland M Jones
Fronteira Energia Ltda, Brazil
Jia Min Jin
Shanghai Research Institute of Materials, China
Matthew Godfrey
Enventure Global Technology, United States
Javier Garrigos
Tecnicas Reunidas, Spain
Rukiye Oztekin
Dokuz Eylül University, TurkeySubmit your abstract Today
Title : The Vacuum Insulated Heatable Curtain (VIHC): From conceptual invention to market deployment as a cost-effective dual solution for window heat loss reduction and localised radiant comfort
Saim Memon, Sanyou London Pvt Ltd, United Kingdom
Title : Hydrogen production from depleted or unproductive oil and gas reservoirs
Cleveland M Jones, Fronteira Energia Ltda, Brazil
Title : Predicting drilling challenges and hazards due to subsurface pressure’s drifting
Selim Sanad Shaker, Geopressure Analysis Services, United States
Title : Transforming waste plastic into hydrogen: Progress, challenges, and future directions in pyrolysis-based integrated pathways
Nur Hassan, Central Queensland University, Australia
Title : Novel expandable liner hanger platform for advanced liner drilling and reaming
Matthew Godfrey, Enventure Global Technology, United States
Title : From empirical decline to intelligent forecasting: A hybrid deep learning framework embedding arps physics for unconventional tight-gas reservoir production prediction
Emmanuel Chibueze Obasi, University of Wyoming, United States