HIERARCHICAL BUILDING AND CONDITIONING OF GEOLOGICAL MODELS WITH MACHINE LEARNING PARAMETERIZED TEMPLATES AND METHODS FOR USING THE SAME

摘要

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.