Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture

Yujin Nakagawa, Tomoya Inoue, Hakan Bilen, Konda R. Mopuri, Keisuke Miyoshi, Shungo Abe, Ryota Wada, Kouhei Kuroda, Masatoshi Nishi, Hiroyasu Ogasawara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021
PublisherSociety of Petroleum Engineers (SPE)
ISBN (Electronic)978-1-61399-834-2
DOIs
Publication statusPublished - 9 Dec 2021
Event2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021 - Abu Dhabi, United Arab Emirates
Duration: 15 Nov 202118 Nov 2021

Publication series

NameSociety of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021

Conference

Conference2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period15/11/2118/11/21

Keywords / Materials (for Non-textual outputs)

  • neural network
  • machine learning
  • drilling operation
  • wellbore integrity
  • artificial intelligence
  • time series data
  • drilling parameter
  • reconstruction error
  • upstream oil & gas
  • normal drilling operation

Fingerprint

Dive into the research topics of 'Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture'. Together they form a unique fingerprint.

Cite this