Learning and Hybrid Modeling Approaches for Operational Excellence
Tuesday, 24 September
Room 215 - 216
Technical Session
This session highlights the latest in machine learning and hybrid modeling, focusing on applications like seismic inversion, flare detection, drilling efficiency, and pipeline safety. It offers insights into innovative solutions that drive operational excellence and informed decision-making within the energy sector, making it crucial for industry professionals eager to explore the latest machine learning innovations and their practical applications in improving operational efficiency, safety, and decision-making processes.
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1400-1425 220762Determination Of Dispersion Coefficient Of Solvent In Heavy Oil/Bitumen Under Reservoir Conditions
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1425-1450 220898A Field Application Of Machine Learning For Sonic Log Prediction And In-situ Stress Estimation In Natural Buttes, Unita Basin, Utah
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1450-1515 220773A Novel Approach For Gas Delivery Optimization Of Complex Pipeline Network System Using Hybrid Physics-machine Learning Modeling
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1545-1610 220857Real Time Application Of Deep Learning Based Flare Smoke Detection
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1610-1635 221074Enhancing Real-time Drilling Efficiency: Mechanism Of Rop Prediction Models And Novel Optimization Strategies In Chinese Oilfields
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1635-1700 221070Estimating Time-varying Wavelets Using Deep Learning For Seismic Inversion
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Alternate 220931Novel ML Modeling Approach for Fatigue Failure of Hydrogen-Transporting Pipelines
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Alternate 220714Automated Welland Reservoir Management Using Hybrid Physics And Data-driven Models - Casestudy
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Alternate 220745Towards Robust And Automated Contamination Estimation During Downhole Fluid Sampling.
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