Early sign detection for the stuck pipe scenarios using unsupervised deep learning

Mopuri, Konda Reddy; Bilen, Hakan; Tsuchihashi, Naoki; Wada, Ryota; Inoue, Tomoya; Kusanagi, Kazuya; Nishiyama, Tazuru; Tamamura, Hitoshi

doi:10.1016/j.petrol.2021.109489

Cited by 20 publications

(1 citation statement)

References 21 publications

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“…Li Tong et al [12] used drilling time, speed, torque, pump pressure, and drill pressure as neural network input parameters, optimizing them using the PSO algorithm and significantly improving the prediction accuracy of the stuck pipe model. Reddy K M et al [13] proposed a new method for detecting stuck events in drilling using autoencoder in deep learning by constructing an autoencoder model on a recurrent neural network to model normal drilling activities and identify anomalous activities as stuck events. Experimental results show promising performance in detecting stuck signs in actual data from multiple drilling sources.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of Stuck Pipe Based on Data Augmentation and ATT-LSTM

Zhang,

Dong,

Yang

et al. 2024

Processes

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Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM neural network model with an attention mechanism (ATT-LSTM) is introduced. By applying the attention mechanism in each time step, the model can dynamically adjust the degree of attention to different parts of the sequence and better capture the key information in the data, which improve the accuracy of the recognition and the generalization ability of the model. By testing the trained model on field data, the test results show that the method achieves more significant performance improvement on the stuck pipe recognition task, and the prediction accuracy of the intelligent model increases by 21.31% after data enhancement.

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Section: Introductionmentioning

confidence: 99%