Bidirectional translation + spatial continuity validation

Capturing cross-property correlations while preserving spatial continuity is essential for reliable reservoir characterization, especially in heterogeneous reservoirs where facies architecture controls petrophysical variability. In this study, we evaluate Pix2Geomodel.v2 as a bidirectional image-to-image translation framework that learns mappings between facies and petrophysical properties using paired 2D slices exported from a reference reservoir model. To reduce data demands while maintaining geological complexity, the workflow operates on a streamlined grid of 54 vertical layers, enabling efficient training and rapid experimentation without removing key stratigraphic and facies patterns. The approach is based on a conditional generative adversarial learning strategy. A U-Net generator is trained to synthesize target facies or property maps from input images, while a PatchGAN discriminator encourages locally realistic textures and geologically plausible transitions. The paired-slice formulation allows the model to learn both large-scale structural organization and fine-scale heterogeneity directly from examples. We investigate two complementary directions: (i) facies-to-property translation, where facies maps are used to predict continuous property fields such as porosity and permeability, and (ii) property-to-facies translation, where petrophysical images are used to reconstruct discrete facies distributions. Beyond conventional forward mapping, the reverse translation experiments are particularly informative because they test whether the model captures meaningful cross-property dependencies rather than superficial patterns. The reconstructed facies maps recover coherent large-scale facies trends and geologically consistent connectivity, indicating that the learned representation encodes relationships between depositional architecture and petrophysical response. Spatial realism is further examined using experimental variograms, providing a continuity-based check that generated outputs qualitatively align with the reference model in terms of spatial correlation structure. Overall, the results suggest a data-efficient route to robust forward and reverse translations that can support faster reservoir model prototyping, property population guided by facies, and consistency checking between facies and petrophysical interpretations.