Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions
Jun Zhou, Zichen Li, Shitao Liu, Chengyu Li, Yunxiang Zhao, Zonghang Zhou, Guangchuan Liang
Abstract
The surface injection and production system (SIPS) is a critical component for effective injection and production processes in underground natural gas storage. As a vital channel, the rational design of the surface injection and production (SIP) pipeline significantly impacts efficiency. This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects. An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model. This paper proposes a hybrid genetic algorithm generalized reduced gradient (HGA-GRG) method, and compares it with the traditional genetic algorithm (GA) in a practical case study. The HGA-GRG demonstrated significant advantages in optimization outcomes, reducing the initial cost by 345.371 × 10 4 CNY compared to the GA, validating the effectiveness of the model. By adjusting algorithm parameters, the optimal iterative results of the HGA-GRG were obtained, providing new research insights for the optimal design of a SIPS.