Solution

Geothermal Energy

Apply AI-powered seismic interpretation to geothermal reservoir characterization — identify heat anomalies, map fault networks, and optimize well placement.

Previously unmapped heat anomalies found in 2D survey reprocessing

3×

Faster fracture network mapping

< 15 min

Full structural interpretation

Blind well validation metrics

Geothermal-Specific Capabilities

The same ML pipeline. Calibrated for heat.

The peer-reviewed algorithms trusted by oil and gas operators, configured for the unique physics of geothermal reservoirs. Same rigour. Different parameters.

Anomaly Detection

Anomaly

Conv-VAE identifies thermal anomalies in 2D/3D surveys. Higgins beta-VAE disentangles thermal signal from structural noise, delivering confidence-weighted heat anomaly maps for drill-target ranking.

Fault Mapping

Structure

DTW + GCN maps conductive fault networks across the full survey volume. Stratigraphic ordering constraints enforce geological consistency. Critical for fracture permeability assessment in EGS systems.

Reservoir Characterisation

Rock Physics

Rock physics models adapted for geothermal parameters. Batzle-Wang fluid substitution, Hertz-Mindlin contact theory, and DEM effective medium theory calibrate seismic attributes to porosity and permeability.

Uncertainty Quantification

Monte Carlo P10/P50/P90 for resource estimation. MC Dropout (Gal & Ghahramani 2016) propagates model uncertainty. Blind well cross-validation quantifies spatial prediction accuracy.

Play Types

Three geothermal play types. One platform.

From high-enthalpy volcanic systems to low-enthalpy sedimentary aquifers, Seismic Swift AI adapts to the subsurface challenge.

Enhanced Geothermal Systems (EGS)

Map natural fracture networks and identify optimal stimulation zones. Pre-stimulation baseline surveys establish amplitude and velocity references. Post-stimulation 4D monitoring tracks induced fracture growth via NRMS repeatability analysis.

Fracture corridor mapping from amplitude coherence
Stimulation target ranking by fracture density
Post-stimulation 4D monitoring with NRMS QC

150–300+°C

Temperature

4–6 km

Target depth

Hydrothermal Convection Systems

Identify upflow zones, outflow channels, and reservoir boundaries. Velocity anomalies in denoised volumes indicate fluid-filled zones. AVO analysis distinguishes liquid-dominated from vapour-dominated reservoirs.

Upflow zone identification from velocity anomalies
Reservoir boundary mapping with automated horizons
Fluid phase discrimination via AVO attributes

180–350°C

Temperature

1–3 km

Target depth

Sedimentary Basin Geothermal

Characterise deep sedimentary aquifers at basin scale. Horizon tracking maps target formations across the basin. Rock physics inversion estimates porosity and permeability with blind well cross-validation.

Basin-wide horizon mapping at formation scale
Porosity and permeability estimation from rock physics
Leave-N-out blind well cross-validation

80–150°C

Temperature

2–5 km

Target depth

Case Study

"Identified 3 previously unmapped heat anomalies in 2D survey reprocessing for a geothermal developer."

A geothermal operator reprocessed legacy 2D seismic lines originally acquired for oil and gas exploration. The original interpretation identified zero viable targets. Seismic Swift AI's denoising stage recovered a 4 dB signal-to-noise improvement. Conv-VAE anomaly detection then identified three statistically distinct thermal anomalies consistent with geothermal upflow. Two of the three anomalies coincide with known surface thermal expressions not previously correlated with the seismic data.

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