Comparative Analysis and Suggestions for Sucker Rod Pump Working Condition Diagnosis Using Machine Learning Techniques

Abstract This study explores the application of six state-of-the-art image classification models—ResNet, MobileNet, EfficientNet, ViT, EfficientViT, and MobileViT—in diagnosing sucker rod pump faults using dynamometer cards. The models, fine-tuned from pre-trained models developed on ImageNet-1k, are evaluated on a dataset of 11,941 dynamometer cards. Our findings demonstrate a significant inverse correlation between model accuracy and the number of trainable parameters, notably with EfficientViT achieving the highest accuracy (95.73%) despite having fewer parameters. This suggests that simpler, but more efficient models can outperform more complex architectures in specific tasks like SRP diagnosis. Through Grad-CAM visualization, we analyzed model predictions to identify critical features, highlighting ResNet's superior generalization ability and robustness. This work provides insights into model selection and data preparation, aiming to enhance the reliability and efficiency of SRP working condition diagnosis models.

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Published
2024-10-11
DOI
https://doi.org/10.2118/221330-ms
Citations
1
Primary Topic
Oil and Gas Production Techniques
Type
article
Field-Weighted Citation Impact
0.34
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article

Comparative Analysis and Suggestions for Sucker Rod Pump Working Condition Diagnosis Using Machine Learning Techniques

Yuan‐Xin Li, Rui Zhao, Jingjing Yang, Xuan Zeng et al.
1 citations
Oil and Gas Production Techniques
0.34
article

Comparative Analysis and Suggestions for Sucker Rod Pump Working Condition Diagnosis Using Machine Learning Techniques

Yuan‐Xin Li, Rui Zhao, Jingjing Yang, Xuan Zeng, Xiaole Zhang, Dewen Hu, Jie Shao, Meng Jiang, Shuang Wang, Haipeng Wang, Jun Shi, Yuping Sun, Yang Yu, Xiaofeng Zhang, Chun‐Da Liao, Jianfeng He, Gengyu Ma, Chunyu Wang
article en
1 citations

Abstract

Abstract This study explores the application of six state-of-the-art image classification models—ResNet, MobileNet, EfficientNet, ViT, EfficientViT, and MobileViT—in diagnosing sucker rod pump faults using dynamometer cards. The models, fine-tuned from pre-trained models developed on ImageNet-1k, are evaluated on a dataset of 11,941 dynamometer cards. Our findings demonstrate a significant inverse correlation between model accuracy and the number of trainable parameters, notably with EfficientViT achieving the highest accuracy (95.73%) despite having fewer parameters. This suggests that simpler, but more efficient models can outperform more complex architectures in specific tasks like SRP diagnosis. Through Grad-CAM visualization, we analyzed model predictions to identify critical features, highlighting ResNet's superior generalization ability and robustness. This work provides insights into model selection and data preparation, aiming to enhance the reliability and efficiency of SRP working condition diagnosis models.

Tarim University (CN), Research Institute of Petroleum Exploration and Development (CN), Second Hospital of Liaohe Oilfield (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 40%
Oil and Gas Production Techniques
0.34
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