AI-Driven Reservoir Modeling & Optimization
Wednesday, 21 October
Room 370 ADBE
Technical Session
This session highlights the latest advances in artificial intelligence and machine learning for reservoir modeling and optimization, including physics-informed approaches, thermodynamic property prediction, data-driven forecasting, and integrated subsurface–surface workflows. Contributions demonstrate how autonomous and intelligent systems can enhance decision-making, improve prediction accuracy, and enable more efficient reservoir management across a range of applications, from geosteering to CO2 EOR and field-scale production optimization.
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1400-1425 234123Reinforcement Learning Driven Adaptive Spatial Inference And Multi-objective Optimization For Autonomous Geosteering
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1425-1450 233993Data Analytic Coupled Machine Learning Applications For Active Reservoir Management: Co2 Eor Use Case
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1450-1515 234206AI-Driven Reservoir Modeling For Field-Scale Production Optimization In A Mature Offshore Oil Field
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1545-1610 234179Multilayer Reservoir Graph Networks Coupled With Surface Production Networks For Rapid Field-scale Forecasting And Optimization
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1610-1635 233852Scaling The Expert Eye: Automating Subsurface Modeling Via Reservoir Engineering Inspired AI Agents
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1635-1700 234136A Conversational User Interface For Autonomous Reservoir Simulation Deck Generation And Execution
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Alternate 233876Accelerating Agent Driven Insights In Exploration And Production: Introducing The Visualization Agent
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Alternate 234049Data Augmentation And EUR Prediction In Tight Gas Fields Via MLP-enhanced GANs
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Alternate 233918A Thermodynamic Cross-attention Transformer With Interpretable Gray-box Correlation For Co₂ Solubility In Ionic Liquids