Data-driven decision-making is reshaping artificial intelligence & big data analytics in oil & gas, optimizing operations and minimizing environmental impact. Machine learning algorithms are enhancing seismic data interpretation, improving reservoir management, and predicting equipment failures before breakdowns occur. Predictive analytics is reducing downtime in refineries and drilling operations, while AI-driven process control is improving efficiency and reducing energy consumption. Big data integration is facilitating real-time monitoring of pipelines and offshore platforms, ensuring safety and regulatory compliance. The integration of cloud computing and edge analytics is further enhancing operational efficiency. AI-based automation is expected to play a pivotal role in optimizing oilfield production while reducing emissions.
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