Hierarchical Building and Conditioning of Geological Models with Machine Learning Parameterized Templates and Methods for Using the Same

A hierarchical conditioning methodology for building and conditioning a geological model is disclosed. In particular, the hierarchical conditioning may include separate levels of conditioning of template instances using larger-scale data (such as conditioning using large-scale data and conditioning...

Full description

Saved in:
Bibliographic Details
Main Authors SAIN, Ratnanabha, EL-BAKRY, Amr S, FOROUZANFAR, Fahim, HARRIS, Matthew W, IMHOF, Matthias G, CHENG, Mulin, WU, Xiao-Hui
Format Patent
LanguageEnglish
Published 23.03.2023
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
Online AccessGet full text

Cover

More Information
Summary:A hierarchical conditioning methodology for building and conditioning a geological model is disclosed. In particular, the hierarchical conditioning may include separate levels of conditioning of template instances using larger-scale data (such as conditioning using large-scale data and conditioning using medium-scale data) and using smaller-scale data (such as fine-scale data). Further, one or more templates, to be instantiated to generate the geological bodies in the model, may be selected from currently available templates and/or machine-learned templates. For example, the templates may be generated using unsupervised or supervised learning to re-parameterize the functional form parameters, or may be generated using statistical generative modeling.
Bibliography:Application Number: US202017756278