Data-Driven Insights and AI Applications in Well Performance and Fracture Characterization
Wednesday, 22 October
Room 320C
Technical Session
Data-driven approaches have been developed and applied to wells and assets for more than a decade. Recent advancements strive to improve predictive capability and characterization by injecting physics. This session showcases innovative hybrid approaches that integrate physical laws, full-physics model/simulation and/or domain expertise with data analytics and artificial intelligence applied to production prediction & optimization, fracture characterization, completion optimization, wellbore flow profiling and pipe flow prediction.
Session Chairpersons
-
1400-1425 228143Integrating Data-driven Insights With Domain Expertise Using Agentic Conversational Analytics For Well Completions Optimization
-
1425-1450 227897A Computer Vision-based Intelligent Quantification Method For Wellbore Flow Rate
-
1450-1515 228139Physics-Constrained Production Forecasting With Direct Fracture Characterization By Field Data
-
1515-1540 227950Reliable Production Optimization Using Hybrid Physics-based Models And Data-driven Techniques
-
1540-1605 228096Deep Learning-driven Inversion Of Natural Fractures From Fiber Optic Data In Hydraulic Fracturing
-
1605-1630 228180Harnessing Physics-regularized Neural Networks For Accurate And Scalable Multiphase Flow Forecasting In Pipes
-
Alternate 228259Cnn-lstm For Coalbed Methane Production Prediction Considering Multiple Factors
-
Alternate 228112Inversion Of The Three-dimensional In-situ Stress Field And Characterization Of Hydraulic Fracture Geometry In Shale Plays Using A Tabular Prior-data Fitted Network-assisted Data Assimilation Approach
-
Alternate 227967Data-driven Forecasting Of Well Production Considering Re-fracturing In Shale Reservoirs
