Maximizing Ultimate Recovery: AI/ML Advancements in Unconventional Fracturing and Well Diagnostics
Thursday, 22 October
Room 361 BECF
Technical Session
This session showcases how advanced AI/ML techniques are revolutionizing unconventional fracture design, characterization, and optimization to drastically improve stimulation efficiency. Attendees will learn about real-time multistage fracture optimization using reinforcement learning, fracture propagation modeling via graph neural networks, and AI-driven frac hit assessment and restoration. Join us to catch up on the latest data science and engineering analytics use cases driving the industry toward doubling ultimate recovery.
Session Chairpersons
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0830-0855 234183Multi-Modal Data-Driven Fracture Response Surrogate: Integrating Development History and Prior Knowledge for Efficient Co2 Storage Assessment
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0855-0920 234065Rollout-Aware Graph Neural Network-Based Simulator for Hydraulic Fracture
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0920-0945 233938Real-Time Fracture Geometry Inversion, Geomechanical Updating, and Hydraulic Fracturing Design Optimization Through AI Surrogate Modeling and Data Assimilation
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1015-1040 233950A Conditional Variational Autoencoder Surrogate for High-Fidelity Spatiotemporal Prediction of Hydraulic Fracturing Dynamics and Proppant Distribution
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1040-1105 233910Physics-Simulation-Informed Conditional Surrogate for Reconstruction of Multi-Cluster Hydraulic-Fracture States
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1105-1130 233903A Machine Learning Based Workflow For Frac Hit Assessment, Pattern Recognition, And Production Restoration
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Alternate 234062A High-Fidelity Digital Twin Framework for Proppant Transport: Integrating GPU-accelerated Barracuda Virtual Reactor MP-PIC with Graph Neural Networks
