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Real-time kinematic analysis of beam pumping unit: a deep learning approach

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Abstract

A kinematics analysis model for the beam pumping unit (BPU) based on deep learning is proposed in this study. It takes the real-time BPU video stream as the input, and outputs the sequence of kinematic parameters. The model is composed of two parts. The first is the motion detection model based on modified Yolov4, which can output the components classes and positions of the targeted BPU in the input video. Experimental results on the test dataset reveal that the average precision of the modified model is superior to original Yolov4 and other methods. The second part is the BPU kinematics analysis model based on motion detection, which can analyze and output the kinematics parameters of BPU in real time according to the input motion detection results. Finally, we deployed this model on mobile inspection robot and run in oilfield. The experiment results show that the approach proposed in this study can help achieve accurate kinematics analysis on targeted BPU in real time. In general, this research provides a novel approach for BPU kinematics analysis, which is a very important part of the intelligent oilfield construction.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Junjiao Sun & Zhiqing Huang

  2. School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yue Zhu

  3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Yanxin Zhang

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  1. Junjiao Sun
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  2. Zhiqing Huang
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  4. Yanxin Zhang
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Correspondence to Zhiqing Huang.

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Sun, J., Huang, Z., Zhu, Y. et al. Real-time kinematic analysis of beam pumping unit: a deep learning approach. Neural Comput & Applic 34, 7157–7171 (2022). https://doi.org/10.1007/s00521-021-06783-0

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