GeoPix AI
We build AI software that transforms geological and reservoir data into simulation-ready geomodels. Our platform supports property prediction, reconstruction, and cross-property translation for energy, geothermal, carbon storage, and groundwater applications.
About
GeoPix AI turns complex reservoir data into practical outputs that modelers can use faster, with less manual interpretation.
Problem
Conventional geomodeling is slow, manual, and often struggles to preserve spatial continuity in heterogeneous reservoirs.
Solution
Our generative AI framework learns relationships between geological properties and produces consistent translated models.
Outcome
Shorter modeling cycles, reproducible workflows, and a path to scalable subsurface intelligence products.
Product
A modular platform for property-to-property translation and reconstruction.
Pix2Geomodel Engine
- Image-to-image translation for reservoir properties
- Masked property reconstruction
- Structured outputs for downstream interpretation
- Designed around a U-Net generator and PatchGAN discriminator
GeoPix Platform
- Dataset organization and preprocessing
- Model inference and batch processing
- Workflow automation for subsurface teams
- Extensible to geothermal and CCS projects
Proof
Research, patenting, and benchmarked workflows support the technical credibility of the startup.
Research foundation
The platform is grounded in published work on Pix2Geomodel and GeoPix, including reservoir property translation, dataset release, and robustness validation across reservoir settings.
Commercial foundation
The invention has been captured in a patent-oriented disclosure for geological reservoir data processing using conditional GANs, covering prediction, reconstruction, and translation workflows.
How it works
Simple, repeatable workflow from source data to modeled outputs.
Acquire reservoir data
Import facies and petrophysical grids, logs, or interpreted model slices.
Preprocess and augment
Normalize, resize, and expand training samples while preserving geological structure.
Train the model
Fit the Pix2Geomodel cGAN to learn cross-property relationships and reconstruction patterns.
Generate outputs
Predict missing properties or translate from one reservoir property to another.
Contact
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Next step
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