Data-driven insights to well operations: Drilling and geoscience applications
Wednesday, 22 October
Room 332AD
Technical Session
From use-cases on real-time drilling dysfunction, hydraulic fracturing designs, predictive maintenance, causal inference methodologies, extracting insights from drilling data with LLMs, analysis of wave-induced heave effects on drilling dynamics, to compositional fluid analysis, our technical session will show a diverse set of insights on data-driven solutions for well operations. Examples of how data science and machine learning applied to both well drilling and geoscience workflows with diverse methodologies can effectively give insights to wells, geoscience and operational decision making.
-
0830-0855 227965Real-time Drilling Dysfunction Monitoring: A Dual-dimensional Graph Attention Network With Multivariate Time-series Data
-
0855-0920 228241Advanced Conversational Drilling Data Analytics: On-Premise Large Language Models Drive Bits-to-Bytes Insights
-
0920-0945 227954Heave Effects On Drilling Dynamics Observed Through High-frequency Measurements
-
1015-1040 227859Beyond Static Near-wellbore Data: Optimization Of Hydraulic Fracturing Parameters Under Co-evolution Constraints Of Fracture Networks And Geological Spatial Fields
-
1040-1105 228051A Causal Inference Pipeline For Injection-induced Seismicity: Open-source Methodology And Implementation
-
1105-1130 228186An Integrated Geology And Engineering Workflow For Optimal Hydraulic Fracturing Design In Deep Coal Coalbed Methane Multiwell Pads
-
Alternate 228194Stop Using Convolutional Neural Networks: Knowledge Distillation For An Interpretable And Lightweight Decision Tree In Rod Pump Working Condition Diagnosis
-
Alternate 228015Machine Learning Approaches For Compositional Fluid Analysis In Logging While Drilling Using Near-infrared Data
-
Alternate 228061Exploring Modern Feature Extraction Techniques For Improved Offshore Fault Detection In Oil And Gas Operations
