A benchmark data set for meandering reservoir facie modelling published online and now available to download.

The GAN River-I data set is designed to provide a stern test for machine learning and geostatistical tools that wish to recreate the complex geometries of realistic facies distributions in subsurface reservoirs. It provides more complex, non-stationary facies distributions than earlier open data sets,  and is generated with FLUMY process-based algorithm.

It has been puiblished on-line in Elseview Data in brief: Chao Sun, Vasily Demyanov, Daniel Arnold (2022) GAN River-I: A process-based low NTG meandering reservoir model dataset for machine learning studies, Data in Brief, Elsevier.
and the data is available to download from GitHub