AI Driven Autonomous Gas-Lift Optimization: Enabling Production Enhancement Through Digital Transformation

This paper presents a pioneering approach to autonomous gas-lift optimization using AI-enabled Multivariable Predictive Control (MPC), aligned with the upstream oil and gas sector's digital transformation strategy. The solution integrates data-driven and machine learning models to enable real-time adaptation and optimization of oil-producing wells, targeting enhanced production with reduced gas-lift energy consumption. Automated workflows support cross-functional collaboration and streamline operations. Dynamic linear well models were developed using step test data from selected wells. A clustering technique based on choke orifice, productivity index among other parameters (Hernandez et al., 2024 [1]) enabled model parameter generalization across similar wells, minimizing the need for individual step tests. Inferential models were deployed to estimate bottom-hole pressure and oil rate in the absence of direct measurements. Advance Process Control (APC) strategies were implemented on a centralized Level 3 control server, integrated with the Distributed Control System (DCS), while cloud-hosted workflows managed model updates and operating envelope adjustments. The system-maintained gas-lift at the inflection point or up to a customizable economic limit (GAIN FACTOR) in terms of BLS per MMSCF, achieving maximum energy efficiency and up to 1–2% increase in overall production. A supervisory cloud layer continuously tracked soft sensor outputs and field controller data, triggering automatic recalibration workflows when deviations occurred. Digital dashboards and decision support tools provided real-time insights and well-specific recommendations, enhancing operational agility and reducing downtime. This study validates the potential of AI-driven optimization to automate complex field operations, reduce human intervention, and enable scalable, closed-loop control across gas-lifted assets demonstrating a tangible pathway for digital transformation in upstream oil and gas operations.