AI Geothermal Resource Discovery

Combines geoscience data (seismic, MT, well logs, remote sensing) with AI to identify and rank prospective geothermal resources.

The Problem

“Reduce geothermal drilling risk and discovery time”

Organizations face these key challenges:

High cost of subsurface uncertainty: a single non-productive exploration well can consume $5–12M and materially delay project schedules

Fragmented and inconsistent data (legacy well logs, MT/seismic, geochemistry, remote sensing) makes integration slow and heavily dependent on scarce expert interpretive capacity

Low signal-to-noise in geothermal indicators and complex geology (faulted systems, variable permeability) leads to poor target ranking and high dry/low-permeability well rates

Impact When Solved

Reduce failed exploration wells by ~10–20% via probabilistic prospect ranking and uncertainty-aware well targetingCut exploration-to-confirmation timelines by ~3–9 months through faster data integration and iterative model updates after each new wellLower early-stage exploration spend by ~10–25% by prioritizing the highest-value surveys and focusing drilling on the most likely productive zones

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

Artificial Intelligence for Energy Systems

Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.