Intelligent Collaborative Optimization Method for Multi-Well Plunger Gas Lifting Process on Platform

The current plunger gas lift production process still relies on the traditional ‘one-to-one’ control configuration, where one controller manages a single gas well. This approach does not fulfil platform requirements for centralized, efficient, and unified coordination and management of multiple wells. To increase production, improve efficiency, and mitigate safety risks, this article offers an intelligent optimization method for a collaborative plunger gas lift in multi-objective, multi-well platforms. The method integrates mechanistic modeling and data-driven approaches to develop a collaborative model for multiple wells on the platform, accounting for inter-well pressure interference and pipeline backpressure. A particle swarm optimization algorithm is implemented to solve the model, with a composite fitness function balancing maximum daily gas production and minimum production fluctuations. A case study on the XXX Platform shows that the method enhances total gas production, reduces production fluctuations, and lowers system backpressure compared to the current operating schedule. Implemented via a localized edge computing architecture, it supports real-time scheduling, providing technical references for shale gas development.