Integrated Data Mining and Automation for Gas Lift Design Optimization

Abstract This paper presents an innovative approach that integrates data mining and automation to enhance gas lift design across the entire lifespan of a well, leveraging the capabilities of Industrial Revolution (IR) 4.0 technologies. The primary goal is to streamline the design process through advanced analytics, ensuring optimal performance in the increasingly digitalized oil and gas sector. The methodology draws on reservoir data, PVT analysis, well completion details and surface facilities information. By combining data mining with automation tools, the entire process is automated to provide a bespoke gas lift design. A digital twin of the well is created using industry standard production engineering software, integrated with advanced data visualization and analytics platforms. This system efficiently processes data, enabling seamless modelling and optimization utilizing key gas lift design parameters such as reservoir pressure, productivity index (PI), gas liquid ratio, casing heads pressure, injection depth, lift gas gravity, kill fluid gradient and flowing wellhead pressure. The proposed method has been successfully applied using pre-existing data. Through over 100 simulation scenarios, the approach optimizes valve selection, size and spacing. Advanced analytics guide the selection of multiple valve depth combinations for further evaluation, ensuring the most effective gas lift design over well's life cycle. This approach also identifies optimal times for valve replacement or upgrades. By automating this process, design time is reduced from one day to under an hour, while maintaining a transparent and auditable decision-making framework. This methodology represents a significant step in the digital transformation of the oil and gas industry, combining data mining, automation and IR 4.0 concepts. By incorporating advanced analytics and automation, this approach not only enhances gas lift design but also improves overall operational efficiency, reduces costs and optimizes production through the well's lifecycle. The work highlights how data driven decision making and automation can lead to more efficient and cost-effective operations in today's digitally driven industry.