We work across the full lifecycle of a geothermal field — from screening a country for undiscovered resource, to the well log that confirms a producer is on target.
We fuse public geological, geophysical, geochemical and remote-sensing data with machine learning to rank geothermal favorability across entire regions — surfacing undrilled prospects across Latin America before a single exploration well is spudded.
Field status and figures from public sources (operator/press releases, academic country updates) as of 2026 — not derived from our own analysis. The favorability shading itself is illustrative.
3D structural and conceptual geological models built in Leapfrog® and GemPy, integrating surface geology, well data and regional structure into a shared framework for the reservoir team.
Interpretation of resistivity, gravity, seismic and fluid-chemistry surveys, integrated directly into the conceptual and numerical reservoir models.
Candidate well locations and trajectories ranked against the geologic and numerical models, balancing resource temperature, permeability and drilling risk.
Trajectory, casing and completion design for exploration, production and injection wells, matched to the expected downhole pressure and temperature regime.
Physics-based natural-state and production simulation with HPC-scale uncertainty quantification — our founding discipline.
See methodology →Design and testing of downhole and surface well-test equipment for flow, injectivity and completion testing programs.
Pressure-temperature-spinner (PTS) and acoustic borehole imaging (ABI) logging services to characterise feed zones and confirm wellbore integrity.