AI Methane Leak Detection

Detects, quantifies, and prioritizes methane leaks using AI on sensor, aerial, and satellite data to reduce emissions and safety risk.

The Problem

“Undetected methane leaks drive emissions and losses”

Organizations face these key challenges:

Sparse, periodic inspections miss intermittent or small leaks, allowing high-emitters to persist for weeks

Manual leak localization and verification require multiple site visits, driving high labor and vehicle costs

High false-alarm rates from single-sensor thresholds create alert fatigue and slow response to true leaks

Impact When Solved

30–60% reduction in methane emissions through faster detection and repair prioritization20–40% fewer field dispatches by improving triage and pinpointing likely leak sources<24–72 hour time-to-detect versus ~10–30 days with traditional LDAR-only programs

The Shift

Real-World Use Cases

IoT-Powered Predictive Maintenance for Oilfield Efficiency

This is like putting smart fitness trackers on every critical machine in an oilfield so you can see problems coming before anything breaks, instead of waiting for a breakdown and then sending a repair crew.

Time-SeriesEmerging Standard

8.5

Digital twins and AI for oil and gas energy systems

This is about building detailed “virtual power plants and pipelines” for the oil and gas sector, then using AI to watch how they behave, predict problems before they happen, and suggest how to run them cheaper and safer.