Deciphering Rod Pump Anomalies: A Deep Learning Autoencoder Approach
Abstract
:1. Introduction
2. Methodology
2.1. Rod Pump Data
2.2. Autoencoder Model
2.3. Dynamic Threshold Calculation Method
3. Case Study
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Case | Anomaly Detection Model Prediction Time | True Failure Time |
---|---|---|
Well A | 20 July 2023 08:30 | 21 July 2023 10:00 |
Well B | 14 August 2023 14:15 | 15 August 2023 16:00 |
Well C | 10 September 2023 20:00 | 11 September 2023 01:30 |
Well D | 02 November 2023 18:00 | 05 November 2023 15:20 |
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Wang, C.; Ma, H.; Zhang, X.; Xiang, X.; Shi, J.; Liang, X.; Zhao, R.; Han, G. Deciphering Rod Pump Anomalies: A Deep Learning Autoencoder Approach. Processes 2024, 12, 1845. https://doi.org/10.3390/pr12091845
Wang C, Ma H, Zhang X, Xiang X, Shi J, Liang X, Zhao R, Han G. Deciphering Rod Pump Anomalies: A Deep Learning Autoencoder Approach. Processes. 2024; 12(9):1845. https://doi.org/10.3390/pr12091845
Chicago/Turabian StyleWang, Cai, He Ma, Xishun Zhang, Xiaolong Xiang, Junfeng Shi, Xingyuan Liang, Ruidong Zhao, and Guoqing Han. 2024. "Deciphering Rod Pump Anomalies: A Deep Learning Autoencoder Approach" Processes 12, no. 9: 1845. https://doi.org/10.3390/pr12091845