Enhancing Offshore Gas Reservoir Performance Utilizing AI-Powered Smart Gas Lift Device for Optimized Efficiency and Control

Abstract The efficient extraction of natural gas is persistently challenged by downhole obstructions, such as mineral scale and sand accumulation, which constrict flow paths and lead to significant production declines. Traditional diagnostic methods, like production logging tools, are operationally intensive, costly, and require production halts. This paper presents an autonomous, data-driven framework leveraging non-invasive ultrasonic sensing and a sophisticated dual-model Machine Learning approach for continuous downhole obstruction monitoring. The methodology employs a two-stage Random Forest architecture: a classifier first determines the binary presence of an obstruction with high accuracy, and a subsequent regressor quantifies its severity by estimating the fractional area blocked. Trained and validated on a comprehensive historical dataset of ultrasonic logs correlated with physical ground truth, the model demonstrates exceptional performance. The classifier achieves near-perfect accuracy (validation: 0.9943, testing: 0.9725), while the regressor provides precise quantitative estimates, with a Mean Absolute Error of ~1.3% and an R-squared value exceeding 0.99 on the test set. This robust, end-to-end framework successfully deciphers the complex, non-linear relationships between high-dimensional acoustic data and physical obstructions. It enables a paradigm shift from reactive interventions to proactive, predictive well management, thereby optimizing operational planning, reducing costs, and enhancing production reliability. Future work will focus on multi-class classification for obstruction-type identification and temporal modeling for forecasting blockage evolution.