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
Deinar Agudelo Ortiz
Natural Motos sas, Colombia
Saim Memon
Sanyou London Pvt Ltd, United KingdomSubmit your abstract Today
Title : An integrated and scalable workflow for evaluating polymer flooding and conformance control strategies in heterogeneous waterflooded reservoirs
Farah Faiz, SLB Digital, Colombia
Title : A novel geomechanical workflow for well design optimization through a new approach in friction coefficient estimation, a case study from West Delta Deep Marine Concession, Mediterranean Sea, Egypt
Akram Sami Elsherbiny Ali Hussein, Rashid Petroleum Company, Egypt
Title : Development and validation of an in-house Python workflow for pressure transient analysis of oil wells against commercial software (KAPPA Saphir)
Waad Faraj Mohamed Elarbi, Waha Oil Company, Libya
Title : HALP system: An edge-AI and two-tier architecture for cost-effective structural health monitoring in extreme industrial environments
Menna Allah Abdelnabi Mohamed Ali, Higher Institute for Engineering in Belbeis, Egypt
Title : Multi-scale evaluation of mechanical properties of the geomaterials
Hem Bahadur Motra, University of Kiel, Germany