在物联网环境中使用混合优化算法的新气体升降机分配方法
Mehdi Darbandi1, Maytham N Meqdad2, Ahmad Hammoud3,4
1Pôle Universitaire Léonard de Vinci, Paris, France.
Scientific reports
|December 27, 2024
概括
这项研究优化了油井的气体注入,使用混合粒子集群优化和原子搜索优化算法与物联网数据. 该方法提高了能源效率和气体利用率,降低了成本.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 随着水库的枯竭,水井的生产率下降,需要人工提升技术,如注气.
- 气体稀缺需要优化分配,以平衡石油生产与最小的天然气使用.
- 传统的天然气分配策略与现实世界的非线性约束和天然气供应限制作斗争.
研究的目的:
- 开发一个创新的,优化的天然气分配战略,用于石油开采.
- 利用物联网 (IoT) 技术进行实时数据采集和自适应优化.
- 为了提高石油回收效率,同时最大限度地减少天然气消耗和能源使用.
主要方法:
- 一种混合优化算法,结合了粒子群优化 (PSO) 和原子搜索优化 (ASO).
- 整合物联网用于实时数据采集和处理.
- 实现动态参数和适应力常数,以加强勘探和开发.
主要成果:
- 在能源效率 (12.12%的减少) 和天然气利用 (18.05%的减少) 方面取得了显著的改进.
- 与现有方法相比,证明了电池寿命的提高 (7.67%) 和成本的降低 (9.48%).
- 混合PSO-ASO方法为气体分配提供了精确和自适应的优化.
结论:
- 拟议的混合优化技术有效地解决了石油开采中的天然气稀缺问题.
- 物联网集成可实现适应性和高效的实时天然气分配策略.
- 该方法通过改善资源管理,提供了实质性的经济和运营效益.
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