Abstract
Rod pumping unit is the main equipment of oil exploitation. The automatic and intelligent of the management, control, running of pumping units are important goals for the construction of the smart oil field. The continuous development of network and deep learning technology provides strong support for the realization of intelligent pumping unit systems. The use of real-time collected pumping unit operating data for working condition supervision and intelligent analysis and decision-making has become an important part of the new generation of vertical pumping unit systems. This article is based on the collected operating data such as the dynamometer diagram of the pumping unit, using the deep learning technology, the intelligent working condition analysis, and the fault diagnosis model of the new generation vertical pumping unit are established. The training time of the model is short while the accuracy of identification and classification is high, which meets the practical application requirements well.
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References
Jufu, F., Jian, Y.: Foreword of the frontier progress of artificial intelligence. Comput. Res. Dev. 56(08), 1604 (2019)
Yaowei, Z., Li, Z.: Application and development of intelligent oil field and intelligent drilling and production technology. Low Carbon World (02), 71–72 (2019)
Changshan, L.: Intelligent management mode in petrochemical production process. Zhonghua Constr. 35(08), 90–91 (2019)
Liuzhu, Z.: Analysis of energy-saving and consumption-reducing measures at the oil production site. Cleaning World 35(05), 31–32 (2019)
Chunming, X.: Intelligent control system of vertical energy-saving pumping unit. Electromech. Eng. 31(04), 486–489 (2014)
Zhigang, T.: Research on fault dynamometer diagnosis method based on convolutional neural network. Xi’an University of Science and Technology, Xi’an (2019)
Shuguang, L., Chenhui, J., Yamin, T., et al.: An ART2-based pumping unit fault diagnosis. In: Chinese Control and Decision Conference 2015, Qingdao, pp. 2955–2958 (2015)
Zhidan, Z., Haojie, F., Penghui, L.: Application of CNN-SVM model in fault diagnosis of pumping unit wells. J. Henan Polytech. Univ. (04), 112–117 (2018)
Zhou, X., Zhao, C., Liu, X.: Application of cnn deep learning to well pump troubleshooting via power cards. In: Abu Dhabi International Petroleum Exhibition & Conference 2019, Abu Dhabi (2019)
Zhidan, Z., Penghui, L., Miaomiao, G.: Research on fault diagnosis of rod pumping unit in petroleum production. Comput. Simul. (02), 443–447 (2016)
Zhou, W., Li, X., Yi, J., et al.: A novel UKF-RBF method based on adaptive noise factor for fault diagnosis in pumping unit. IEEE Trans. Ind. Inform. 15(03), 1415–1424 (2018)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition 2015, Boston, Massachusetts, pp. 1–9 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Computer Vision and Pattern Recognition 2014, Columbus, Ohio (2014)
Tong, X.: Research on oilfield intelligent fault diagnosis system based on optimized RBF network. Xi’an Shiyou University, Xi’an (2019)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition 2016,Las Vegas, Nevada, pp. 770–778 (2016)
Huang, G., Liu, Z., Der Maaten, L.V., et al.: Densely connected convolutional networks. In: Computer Vision and Pattern Recognition 2017, Honolulu, Hawaii, pp. 2261–2269 (2017)
Hu J, Shen L, Albanie S, et al.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 7132–7141 (2018)
Gongpeng, W., Meng, D., Changyong, N.: Stochastic gradient descent algorithm based on convolutional neural network. Comput. Eng. Des. 39(02), 441–445 (2018)
Hui, Z.: Research and improvement of optimization algorithms in deep learning. Beijing University of Posts and Telecommunications, Beijing (2018)
Fuyu, H., Jingzhong, W., Linhao, Z.: Public opinion classification method of heterogeneous data based on CNN and LSTM. J. Comput. Syst. 28(06), 141–147 (2019)
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Duan, Y., Chang, C., Sun, Q., Du, C., Li, Z. (2021). Fault Diagnosis of Vertical Pumping Unit Based on Characteristic Recalibration Residual Convolutional Neural Network. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_44
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DOI: https://doi.org/10.1007/978-3-030-72795-6_44
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