Modern refineries are leveraging digital transformation to enhance operational efficiency and sustainability. Process optimization & automation in refineries utilize AI, IoT, and cloud-based monitoring systems to streamline refining processes. Predictive analytics and real-time data integration help reduce energy consumption, optimize production rates, and minimize emissions. Automation technologies are enhancing precision in fuel blending, reducing waste, and improving safety standards. Robotics and remote monitoring systems are also increasing reliability while lowering maintenance costs. As refineries face evolving regulatory demands and fluctuating market conditions, continuous process optimization remains essential for maintaining competitive and sustainable operations in the oil and gas sector.
Selim Sanad Shaker
Geopressure Analysis Services, United States
Saim Memon
Sanyou London Pvt Ltd, United Kingdom
Nur Hassan
Central Queensland University, Australia
Selim Sanad Shaker
Geopressure Analysis Services, United States
Nur Hassan
Central Queensland University, Australia
Matthew Godfrey
Enventure Global Technology, United States
Barbara Diaz
Automa Srl, ItalySubmit 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