SR-05Frontier
Generative Inter-Well Modeling
Fills the rock between wells with geologically plausible detail instead of a smooth interpolation that no outcrop ever looked like.
Subsurface & Reservoir IntelligenceAI
SR-06FrontierWave 3AI
Deep Learning Seismic Interpretation
In one sentence
Picks faults and horizons across a full seismic volume in hours, leaving the interpreter to judge rather than to click.
The problem
Manual fault interpretation on a large 3D survey takes weeks and is interpreter-dependent to the point that two specialists produce visibly different fault frameworks from the same volume.
How it works
Convolutional segmentation networks trained on synthetic and operator-labelled volumes produce fault probability and horizon surfaces, which the interpreter edits rather than draws. Every automated pick carries a probability, and low-probability regions are highlighted for manual review.
Data required
- Post-stack or pre-stack migrated seismic volume in SEG-Y
- Existing interpreted faults and horizons for fine-tuning
- Well ties and checkshot or VSP data
- Processing history and acquisition parameters
What you get
- Fault probability volume and extracted fault surfaces
- Auto-tracked horizons with confidence
- Attribute volumes aligned to the interpreted framework
What it cannot do
Performance drops sharply on data with a different processing signature from the training set, and re-processing a survey can invalidate a tuned model. In low signal-to-noise zones — below salt, under gas clouds — it produces confident-looking artefacts, so those zones are masked and flagged rather than interpreted.
Every capability page states a real limitation. If a vendor cannot tell you where their product stops, you are not being told the truth about where it starts.
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Automated History Matching
Tunes the reservoir model until it reproduces what the field actually produced, automatically and with the uncertainty kept rather than discarded.
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