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- Title
基于遗传算法的盐穴储气库注气能力方案优化.
- Authors
鲁宇涛; 张引弟; 徐刘伟; 黄孝红; 张海鹏; 王城景
- Abstract
The salt cavern gas storage facility, as one of the crucial means for natural gas peak shaving in our country, is tasked with a strategic reserve role. In order to enhance the operational optimization of the Sichuan-to-East transmission pipeline network in this region, suitable gas injection and production schemes were proposed for Jintan salt cavern gas storage facility, with the primary objective at this stage being the reduction of compressor energy consumption. Following an analysis of various factors including geographical environment, pipeline parameters, gas storage well parameters, and compressor energy consumption of Jintan salt cavern gas storage facility, the injection process of ten wells in the region is taken as an example. The energy consumption analysis of the distribution scheme was established as the main goal. While adhering to constraints on the total gas storage capacity and individual well injection, a genetic algorithm was employed to optimize the injection task scheme. After evaluating the optimization process using five sets of genetic algorithm iterations, the results indicate that convergence of total energy consumption is achieved at around 25 iterations, with an average runtime of 4. 57 seconds per iteration. Further analysis reveals the trend of gas injection volumes for each individual well during the optimization process. In compliance with on-site conditions and injection requirements, the final simulated and optimized production scheme demonstrates a 33% reduction in compressor energy consumption compared to the original scheme. The actual on-site reduction in compressor energy consumption generally falls between 30% and 35%, significantly mitigating energy wastage in compressor operation. This holds substantial significance for practical production applications.
- Publication
Science Technology & Engineering, 2024, Vol 24, Issue 11, p4472
- ISSN
1671-1815
- Publication type
Article
- DOI
10.12404/j.issn.1671-1815.2305043