AI Gas Flare Reduction
AI systems for minimizing gas flaring and venting operations
The Problem
“Reduce routine gas flaring through predictive optimization”
Organizations face these key challenges:
Limited early warning: flare events are often detected after onset, when mitigation options are fewer and more disruptive
Siloed operations: wells, compression, processing, and pipeline nominations are coordinated manually, causing mismatch between supply and takeaway during transients
High variability and data complexity: sensor noise, missing tags, shifting operating modes, and maintenance activities make static rules unreliable and drive inconsistent operator response
Impact When Solved
Real-World Use Cases
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.
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.