2020
DOI: 10.2118/204462-pa
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Early Stuck Pipe Sign Detection with Depth-Domain 3D Convolutional Neural Network Using Actual Drilling Data

Abstract: 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-hi… Show more

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Cited by 23 publications
(1 citation statement)
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“…Qodirov and Shestakov [7] proposed a neural network model with a sliding window for predicting sticking. Tsuchihashi et al [8] edited drilling data concerning the experience and knowledge of drilling engineers and used convolutional neural networks (CNN) to predict sticking accidents during the drilling process. Liu et al [9] used an adaptive genetic algorithm to optimize the weights and thresholds of the back propagation neural network (BP) and thus established the sticking prediction model.…”
Section: Introductionmentioning
confidence: 99%
“…Qodirov and Shestakov [7] proposed a neural network model with a sliding window for predicting sticking. Tsuchihashi et al [8] edited drilling data concerning the experience and knowledge of drilling engineers and used convolutional neural networks (CNN) to predict sticking accidents during the drilling process. Liu et al [9] used an adaptive genetic algorithm to optimize the weights and thresholds of the back propagation neural network (BP) and thus established the sticking prediction model.…”
Section: Introductionmentioning
confidence: 99%