Uncertainty in {AI}-based {Reservoir} {Modelling} {Workflows}

Vasily Demyanov and Daniel Arnold and Quentin Corlay and Athanasios Nathanail and Chao Sun. ( 2024 )
in: Proc. 2024 RING Meeting, pages 8, ASGA

Abstract

Handling uncertainty in reservoir modelling workflows remains a challenge while data science methods find wider applications to subsurface problems. Reservoir modelling workflows are subject to large uncertainties on every step starting from interpretation of exploration data, coming up with a reservoir modelling concept, describing reservoir characteristics and property distribution, integration of dynamic data and model calibration and update as new data become available. AI methods have already proved their successful use in many subsurface modelling tasks.

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BibTeX Reference

@inproceedings{demyanov_uncertainty_RM2024,
 abstract = {Handling uncertainty in reservoir modelling workflows remains a challenge while data science methods find wider applications to subsurface problems. Reservoir modelling workflows are subject to large uncertainties on every step starting from interpretation of exploration data, coming up with a reservoir modelling concept, describing reservoir characteristics and property distribution, integration of dynamic data and model calibration and update as new data become available. AI methods have already proved their successful use in many subsurface modelling tasks.},
 author = {Demyanov, Vasily and Arnold, Daniel and Corlay, Quentin and Nathanail, Athanasios and Sun, Chao},
 booktitle = {Proc. 2024 RING Meeting},
 language = {en},
 pages = {8},
 publisher = {ASGA},
 title = {Uncertainty in {AI}-based {Reservoir} {Modelling} {Workflows}},
 year = {2024}
}