Facies modelling in fluvial environments with {Self}-{Attention} {Generative} {Adversarial} {Networks} conditioned to well data

@inproceedings{goldite_facies_RM2023,
 abstract = {Subsurface reservoir modelling is routinely used in various contexts dealing with the underground environment: energy resources production, subsurface storage, water resources management... Simulating the spatial distribution of geological facies is then a cornerstone piece in most reservoir modelling workflows, as this distribution directly impacts key physical variables such as the porosity, permeability and strength of the underground rocks. However, modelling facies heterogeneities can be particularly challenging for channelized sedimentary environments, and more generally for any environment where the facies distribution is driven by specific genetic units or architectural elements, often called “geobodies”. Indeed, classical geostatistical approaches imply a difficult trade-off between conditioning the model to the available hard data and simulating geobodies which are geologically realistic.},
 author = {Goldité, Valentin and Bouziat, Antoine and Lecomte, Jean-François and Faney, Thibault},
 booktitle = {2023 {RING} meeting},
 language = {en},
 pages = {7},
 publisher = {ASGA},
 title = {Facies modelling in fluvial environments with {Self}-{Attention} {Generative} {Adversarial} {Networks} conditioned to well data},
 year = {2023}
}