Early Sign Detection for the Stuck Pipe Scenarios using Unsupervised Deep Learning

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper we present a novel approach for detecting early signs for the stuck events in drilling using Deep Learning. Specifically, we adapt neural network based unsupervised learning tool called Autoencoder for anticipating the ‘stuck’ events during the drilling process. We build Autoencoders on Recurrent Neural Networks (RNNs) to model the normal drilling activity, thereby detecting the stuck incidents as anomalous activity. We conduct experiments on the actual drilling data collected from 30 field wells operated by multiple drilling sources with diverse well profiles and demonstrate that our approach obtains promising results for the stuck sign detection. Furthermore towards explaining the trained model’s prediction, we present reconstruction analysis on the individual drilling parameters.
Original languageEnglish
Article number109489
Number of pages12
JournalJournal of Petroleum Science and Engineering
Issue numberPart C
Early online date25 Sep 2021
Publication statusPublished - 1 Jan 2022


  • Stuck prediction
  • Field drilling data
  • Unsupervised machine learning
  • Deep learning
  • Autoencoder


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