Digital Transformation and AI: Innovative Machine Learning Applications in the Energy Sector and Reservoir Management
Wednesday, 11 June
Hall D2
Technical Session
This session explores the integration of AI and machine learning in energy sector applications. Topics include self-supervised learning for rock image segmentation, production time-series monitoring with vision-language models, and data-driven reservoir performance forecasting. We'll also discuss predicting liquid loading using hybrid models, synthetic data-enhanced retrieval for energy-specific tasks, time-to-event analysis for shale reservoir performance, and AI-based anomaly detection in surface logging data. Join us to discover these cutting-edge advancements in AI and machine learning for energy.
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1525-1550 225609Self-supervised Learning Using Vision Transformer Architecture For Rock Image Segmentation
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1550-1615 225545Production Time Series Monitoring Using Vision-Language Models
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1615-1640 225507Data-Driven Reservoir Performance Forecasting: Leveraging Machine Learning for Complex Reservoirs
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1640-1705 225515Prediction of Liquid Loading Using Physics-based and Data Driven Machine Learning Model for a German Gas Field
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Alternate 225535Time-to-event Analysis Of Shale Reservoir Performance For Identification Of Key Performance Drivers
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Alternate 225579Detecting Low-quality Intervals in Surface Logging Data Using AI-based Anomaly Detection