Summary A real-time stuck pipe prediction using the deep-learning approach is studied in this paper. Early signs of stuck pipe, hereinafter called stuck, are assumed to show common patterns in the monitored data set, and designing a data clip that well captures these features is critical for efficient prediction. With the valuable input from drilling engineers, we propose a 3D-convolutional neural network (CNN) approach with depth-domain data clip. The clip illustrates depth-domain data in 2D-histogram images with unique abstraction of the time domain. Thirty field well data prepared in multivariate time series are used in this study—20 for training and 10 for validation. The validation data include six stuck incidents, and the 3D-CNN model has successfully detected early signs of stuck in three cases before the occurrence. The portion of the data clip contributing to anomaly detection is indicated by gradient-weighted class activation map (grad-CAM), providing physical explanation of the black box model. We consider such explanation inevitable for the drilling engineers to interpret the signs for rational decision-making.

We present a method for contactless manipulation of multiple small objects in a plane using multiple air jets. When the objects are initially clustered as a group, the group's center of gravity is used as a representative point and the entire group is manipulated. When the objects are initially scattered, the object farthest from the goal is selected and individually controlled. Four jets allowing control of airflow rate and angle are used for a position manipulation experiment with five small balls, and the efficacy of the proposed method is compared and discussed.

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