Data-efficient enhanced Pix2Geomodel.v2 for complex facies settings

Reservoir modelling in heterogeneous carbonate systems is often constrained by sparse well control and labor-intensive interpretation, which increases uncertainty when extrapolating between wells. We present an enhanced Pix2Geomodel.v2 workflow that reframes facies and petrophysical modelling as paired image-to-image translation. Facies and petrophysical properties are exported from a reference reservoir model, converted into paired 2D training images, and used to train a Pix2Pix-style conditional generative adversarial network (cGAN). The architecture couples a U-Net generator with a PatchGAN discriminator, enabling the model to learn spatial relationships directly from examples. To reduce data requirements while retaining geological heterogeneity, the workflow operates on a streamlined grid of 54 vertical layers and targets complex facies distributions. Preliminary results show stable training and predictions that reproduce the main geological patterns of the reference data. In facies-to-property translation, the network learns meaningful mappings to porosity, permeability, and volume of shale.