Technical Session 19: AI-Driven Digital Twin Framework for Subsurface

Thursday, 15 October

0900 - 1030

Room B1, Boulevard Level

Technical Session

0900-0920 234570
Data Driven And Interpretable Prediction Of Hydrogen Rich Gas Brine Interfacial Tension For Subsurface Storage Applications

A. Tatar, M. Hajeer, Adelaide University; A. Russo, Flinders University; A. Zeinijahromi, M. Haghighi, Adelaide University
0920-0940 234358
Outlier Detection For Shale Gas Production Data: An Integrated Approach Of Enhanced Clustering And Domain Knowledge

W. Ren, Y. Duan, Y. Ren, G. Jianchun, J. Zeng, S. Xu, S. Zhu, T. Wu, Southwest Petroleum University
0940-1000 234341
Geological Outcrop Digital Twin For Hydrocarbon Exploration: Enhancing Reservoir Characterization With Multi-block Modeling

J. Zhu, Vertechs Group
Alternate 234380
Assessing The Potential Of Masked Autoencoder Foundation Models In Predicting Downhole Metrics From Surface Drilling Data

A. Berezowski, University of Calgary
Alternate 234442
An Integrated Surface-to-subsurface Digital Twin Platform For Fracturing Optimization And Real-time Risk Mitigation

J. Zhu, Vertechs Group
Alternate 234430
Hata: Multi-agent Ontology Framework For High-quality Log Interpretation Data Reconstruct In Existed Wells Re-assessment

H. Chen, China Natl. Petroleum Corp.
Alternate 234630
Attention-enhanced Deeplabv3 Semantic Segmentation For Identification Of Coal Macerals In Petrographic Images

Y. Pan, China University of Petroleum Beijing

Technical Programme