Gas-lifted wells often operate below their true potential due to changing reservoir conditions, fluctuating fluid properties, or mismatched surface parameters. Manual tuning of gas injection rates or bean sizes tends to be reactive and disconnected from evolving downhole dynamics, leading to inefficiencies like over-lifting, unstable flow, or underutilized gas.

The Gas Lift Optimization solution addresses this challenge by combining real-time field data with physics-informed AI models. It continuously ingests wellhead pressure, tubing pressure, flow rates, gas injection data, periodic well tests, and echo surveys that capture fluid levels and gas-liquid interfaces. These inputs are processed through surrogate or hybrid models that simulate well behavior and forecast how surface changes will impact production performance.

For each well, the system recommends specific, field-executable adjustments—such as increasing bean size to improve flow efficiency, fine-tuning injection rates, or adjusting lift cycles for intermittent producers. Recommendations are ranked based on projected uplift in production or gas efficiency.

Over time, the solution learns from field response and refines its logic, making optimization smarter and more adaptive. This transforms gas lift operations from manual trial-and-error to intelligent, proactive control—helping operators minimize gas waste, stabilize flow, and maximize net production with less operational overhead.