@inproceedings{b079d6ce18394fca9a9463484afd68cf,
title = "Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture",
abstract = "Pipe-sticking during drilling operations causes severe difficulties, including economic losses and safety issues. Therefore, real-time stuck-pipe predictions are an important tool to preempt this problem and avoid the aforementioned troubles. In this study, we have developed a prediction technique based on artificial intelligence, in collaboration with industry, the government, and academia. This technique was developed by combining an unsupervised learning model built using an encoder-decoder, long short-term memory architecture, with a relative error function. The model was trained with the time series data of normal drilling operations and based on an important hypothesis: reconstruction errors between observed and predicted values are higher around the time of pipe sticking than during normal drilling operations. An evaluation method of stuck-pipe possibilities using a relative error function reduced false predictors caused by large variations of drilling parameters. The prediction technique was then applied to 34 actual stuck-pipe events, where it was found that reconstruction errors calculated with the relative error function increased 0.5-10 hours prior to the pipe sticking for 17 out of 34 stuck-pipe events (thereby partly confirming our hypothesis).",
keywords = "neural network, machine learning, drilling operation, wellbore integrity, artificial intelligence, time series data, drilling parameter, reconstruction error, upstream oil & gas, normal drilling operation",
author = "Yujin Nakagawa and Tomoya Inoue and Hakan Bilen and Mopuri, {Konda R.} and Keisuke Miyoshi and Shungo Abe and Ryota Wada and Kouhei Kuroda and Masatoshi Nishi and Hiroyasu Ogasawara",
note = "Funding Information: This work was supported by the Japan Oil, Gas and Metals National Corporation. Publisher Copyright: {\textcopyright} Copyright 2021, Society of Petroleum Engineers; 2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021 ; Conference date: 15-11-2021 Through 18-11-2021",
year = "2021",
month = dec,
day = "9",
doi = "10.2118/207805-MS",
language = "English",
series = "Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021",
publisher = "Society of Petroleum Engineers (SPE)",
booktitle = "Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021",
address = "United States",
}