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
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