Data-Driven Autonomous Gas-Lift Optimization: Leveraging IoT, Edge Computing, and Machine Learning
Abstract
Objective/Scope The primary objective of this research is to demonstrate how autonomous gas-lift optimization—powered by Industrial Internet of Things (IoT), edge computing, and machine learning (ML)—can significantly enhance oil and gas production efficiency. The study aims to validate real-time, intelligent control systems can reduce manual intervention, improve safety, and deliver measurable cost and time savings, aligning with the broader goals of Production 4.0. Methods, Procedures, Process The autonomous gas-lift optimization system integrates several advanced engineering components: Results, Observations, Conclusions Field trials of the autonomous gas-lift system yielded the following key performance indicators (KPIs): Novel/Additive Information This research highlights a paradigm shift in gas-lift operations, where autonomous, intelligent systems replace traditional reactive workflows. By leveraging edge computing and ML, operators can achieve real-time optimization, predictive control, and secure data exchange—hallmarks of Production 4.0. The approach not only improves operational KPIs but also sets the foundation for scalable, self-learning systems across upstream assets.
