This paper presents the deployment of an artificial-intelligence (AI)-enabled autonomous gas lift optimization system, integrating real-time centralized advanced process control (APC) with cloud-based analytics to enhance artificial gas lift performance in producer wells. The paper emphasizes work-process transformation, legacy-system integration, and cross-functional collaboration to deliver a scalable, adaptive, and production-optimized control solution in complex field environments.
Introduction
With access to continuous data streams and computational tools, it is now possible to achieve real-time optimization of gas lifted wells. The highlighted autonomous control system exemplifies this shift by introducing a scalable, adaptive, and intelligent system that operates autonomously, adhering to operational and safety constraints. Physics-based models combined with AI algorithms can manage multiple variables dynamically to stabilize production, optimize gas lift usage, and minimize human intervention across complex production environments.
Field and System Overview
Abu Dhabi Digital Oilfield Context. The case study is based on a mature onshore oil field in Abu Dhabi, consisting of more than 200 producing wells using gas lift.
