AI Geothermal Field Agent

Uses an AI agent to monitor geothermal field data, recommend operational actions, and automate routine diagnostics and reporting.

The Problem

“Slow, inconsistent geothermal field decisions increase risk”

Organizations face these key challenges:

Data silos and inconsistent data quality across SCADA, well tests, chemistry, and geoscience interpretations slow decision-making and increase the chance of missed early-warning signals

Reactive maintenance and interventions driven by alarms or production losses lead to avoidable downtime, repeat scaling/corrosion events, and higher O&M costs

Limited specialist time makes it hard to run frequent integrated reservoir and surface-network optimization, resulting in suboptimal injection allocation and accelerated thermal decline

Impact When Solved

10–25% reduction in unplanned downtime via predictive diagnostics and earlier intervention planning5–15% reduction in drilling/workover non-productive time through automated reporting intelligence, anomaly detection, and best-action recommendations1–3 years of reservoir life extension potential by optimizing injection strategy and detecting thermal breakthrough earlier

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.