This paper provides the technical details of our latest progress of the hydraulic fracturing ball seat event recognition model. In our previous work (Shen et al., 2020), the U-Net architecture was used to build models to recognize the ball seat event. In this paper, we train the Convolutional Neural Network (CNN) to solve the same problem. Comparing with U-Net approach, the CNN model requires less computation resource during training phase while it has slight better performance.

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