  The characterization of subsurface models relies on the accuracy of
subsurface models which request integrating a large number of information
across different sources through model conditioning, such as data conditioning
and geological concepts conditioning. Conventional geostatistical models have a
trade-off between honoring geological conditioning (i.e., qualitative
geological concepts) and data conditioning (i.e., quantitative static data and
dynamic data). To resolve this limit, generative AI methods, such as Generative
adversarial network (GAN), have been widely applied for subsurface modeling due
to their ability to reproduce complex geological patterns. However, the current
practices of data conditioning in GANs conduct quality assessment through
ocular inspection to check model plausibility or some preliminary quantitative
analysis of the distribution of property of interests. We propose the
generative AI realization minimum acceptance criteria for data conditioning,
demonstrated with single image GAN. Our conditioning checks include static
small-scale local and large-scale exhaustive data conditioning checks, local
uncertainty, and spatial nonstationarity reproduction checks. We also check
conditioning to geological concepts through multiscale spatial distribution,
the number of connected geobodies, the spatial continuity check, and the model
facies proportion reproduction check. Our proposed workflow provides guidance
on the conditioning of deep learning methods for subsurface modeling and
enhanced model conditioning checking essential for applying these models to
support uncertainty characterization and decision making.
