Advancements in Drilling and Formation Evaluation using Machine Learning and Data Science
Wednesday, 25 September
Room 228 - 230
Technical Session
With the advancement of machine learning (ML) and data science (DS), Oil and Gas industry is embracing the new technologies to enhance the decision making in drilling operations and formation evaluation. Interactions of ML/DS with the drilling operations and formation data analysis is the key focus of this session. It aims to shed light on innovative approaches to improve formation data interpretability and predictability, reduce modeling uncertainty, and drive high drilling operation efficiency.
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1400-1425 220708Improving Reliability Of Seismic Stratigraphy Prediction: Integration Of Uncertainty Quantification In Attention Mechanism Neural Network
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1425-1450 221058Chasing Remaining Potential In Mature East Nile Delta Development Blocks Using Machine Learning
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1450-1515 220950Reference Synthetic Dataset For Drilling Inventory Optimization
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1515-1540 220980Predicting Permeability In Real-time From LWD Resistivity And Gamma Ray Logs
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1540-1605 221000Refined Diffusion Posterior Sampling For Seismic Denoising And Interpolation
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1605-1630 220946Use Of Machine Learning In Microseismic Monitoring For Thermal Operations In Cold Lake, AB, Canada
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Alternate 220691Novel Method For Automated Advanced Mud Gas Extraction Efficiency Correction (EEC) Characterization In Near Real Time
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Alternate 220863Deploy Distributed Real-time Data Pipelines Across Cloud And Edge To Process Rig Data For Easy Data Fusion And Processing
