Real-time monitoring and predictive analytics are transforming digital twin & smart oilfields for sustainable operations, improving efficiency and reducing environmental impact. Digital twin technology integrates AI, IoT, and cloud computing to create virtual replicas of oilfield assets, enabling predictive maintenance and optimized production planning. Smart oilfields utilize automated data collection from sensors and drones to enhance safety and reduce downtime. AI-driven analytics are identifying energy savings and emission reduction opportunities. The increasing reliance on real-time data and machine learning is streamlining decision-making. By enhancing asset performance and minimizing operational risks, digitalization is ensuring the long-term sustainability of hydrocarbon 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