AI Gas Processing Optimization

Machine learning for natural gas processing plant optimization

The Problem

“Optimize gas processing yields under variable feed”

Organizations face these key challenges:

Highly variable inlet gas composition and flow cause frequent shifts in optimal setpoints and make static operating envelopes overly conservative

Competing objectives (NGL recovery vs. residue gas specs vs. energy use vs. equipment limits) are difficult to balance in real time, leading to margin loss or off-spec risk

Limited visibility into constraint proximity (compressor surge, column flooding, exchanger fouling, solvent loading) results in reactive interventions, trips, and flaring

Impact When Solved

Increase NGL recovery by 0.5–2.0% while maintaining residue gas quality (dew point/Wobbe/BTU) within pipeline specsReduce fuel gas and power consumption by 2–6% through optimized refrigeration, reboiler duty, and compressor loadingLower flaring/off-spec events by 10–30% and improve availability by 1–3% via predictive constraint management and early warning

Real-World Use Cases

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.

Time-SeriesEmerging Standard

8.5

AI-Driven Optimization for Hydrogen Production

We use smart computers to help make hydrogen energy more efficiently and reliably.

optimizationemerging

7.0