基于APSO-MPC的边缘计算柱塞举升优化控制方法。

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-02-05 DOI:10.1038/s41598-025-87726-w

Zhi Qiu, Lei Zhang, He Zhang, Haibo Liang, Yinxian Li

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引用次数: 0

摘要

在页岩气开采中，井底液体负荷降低了气井效率。传统的基于时间的柱塞举升方法利用油藏能量来去除液体，但基于模型的优化方法已经出现。然而，这些部署在远程服务器上的方法会导致低效的数据传输和高服务器负载。本研究提出了一种自适应粒子群优化模型预测控制（APSO-MPC），用于柱塞举升优化，通过边缘计算实现。APSO动态调整惯性权重和学习因子，而基于微处理器的边缘架构将计算定位在控制器上，消除了传输延迟并降低了服务器负载。模拟结果表明，与传统方法相比，APSO-MPC可提高18%的产气量，而边缘计算可提高24%的数据传输，减少83%的数据包丢失，并降低服务器内存和计算延迟。

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A plunger lifting optimization control method based on APSO-MPC for edge computing applications.

In shale gas extraction, bottomhole liquid loading reduces gas well efficiency. Traditional time-based plunger lift methods use reservoir energy to remove liquid, but model-based optimization has since emerged. However, these methods, deployed on remote servers, lead to inefficient data transfer and high server loads. This study proposes an Adaptive Particle Swarm Optimization Model Predictive Control (APSO-MPC) for plunger lift optimization, implemented via edge computing. APSO dynamically adjusts inertia weights and learning factors, while a microprocessor-based edge architecture localizes computations at the controller, eliminating transmission delays and reducing server load. Simulations show APSO-MPC improves gas production by 18% compared to traditional methods, while edge computing increases data transmission by 24%, reduces packet loss by 83%, and lowers server memory and computational delays.

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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