A plunger lifting optimization control method based on APSO-MPC for edge computing applications
Abstract 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 tra...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87726-w |
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| _version_ | 1823862180715429888 |
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| author | Zhi Qiu Lei Zhang He Zhang Haibo Liang Yinxian Li |
| author_facet | Zhi Qiu Lei Zhang He Zhang Haibo Liang Yinxian Li |
| author_sort | Zhi Qiu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-6c74f1baa6be459093df908f68835191 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6c74f1baa6be459093df908f688351912025-02-09T12:37:52ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-87726-wA plunger lifting optimization control method based on APSO-MPC for edge computing applicationsZhi Qiu0Lei Zhang1He Zhang2Haibo Liang3Yinxian Li4Southwest Petroleum UniversitySouthwest Petroleum UniversitySouthwest Petroleum UniversitySouthwest Petroleum UniversityPetroChina Changqing Oilfield Company Third Gas Production PlantAbstract 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.https://doi.org/10.1038/s41598-025-87726-wPlunger LiftModel Predictive ControlAdaptive Particle Swarm OptimizationEdge ComputingOptimizing Control |
| spellingShingle | Zhi Qiu Lei Zhang He Zhang Haibo Liang Yinxian Li A plunger lifting optimization control method based on APSO-MPC for edge computing applications Scientific Reports Plunger Lift Model Predictive Control Adaptive Particle Swarm Optimization Edge Computing Optimizing Control |
| title | A plunger lifting optimization control method based on APSO-MPC for edge computing applications |
| title_full | A plunger lifting optimization control method based on APSO-MPC for edge computing applications |
| title_fullStr | A plunger lifting optimization control method based on APSO-MPC for edge computing applications |
| title_full_unstemmed | A plunger lifting optimization control method based on APSO-MPC for edge computing applications |
| title_short | A plunger lifting optimization control method based on APSO-MPC for edge computing applications |
| title_sort | plunger lifting optimization control method based on apso mpc for edge computing applications |
| topic | Plunger Lift Model Predictive Control Adaptive Particle Swarm Optimization Edge Computing Optimizing Control |
| url | https://doi.org/10.1038/s41598-025-87726-w |
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