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Parameter prediction of coiled tubing drilling based on GAN–LSTM

With the increasing development of coiled tubing drilling technology, the advantages of coiled tubing drilling technology are becoming more and more obvious. In the operation process of coiled tubing, Due to various different drilling parameters, manufacturing defects, and improper human handling, t...

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Detalles Bibliográficos
Autores principales: Zhang, Wanxing, Bai, Kai, Zhan, Ce, Tu, Binrui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322849/
https://www.ncbi.nlm.nih.gov/pubmed/37407667
http://dx.doi.org/10.1038/s41598-023-37960-x
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author Zhang, Wanxing
Bai, Kai
Zhan, Ce
Tu, Binrui
author_facet Zhang, Wanxing
Bai, Kai
Zhan, Ce
Tu, Binrui
author_sort Zhang, Wanxing
collection PubMed
description With the increasing development of coiled tubing drilling technology, the advantages of coiled tubing drilling technology are becoming more and more obvious. In the operation process of coiled tubing, Due to various different drilling parameters, manufacturing defects, and improper human handling, the coiled tubing can curl up and cause stuck drilling or shortened service life problems. Circulation pressure, wellhead pressure, and total weight have an important influence on the working period of coiled tubing. For production safety, this paper predicts circulation pressure, ROP, wellhead pressure, and finger weight using GAN–LSTM after studying drilling engineering theory and analyzing a large amount of downhole data. Experimental results show that GAN–LSTM can predict the parameters of circulation pressure, wellhead pressure ROP and total weight to a certain extent. After much training, the accuracy is about 90%, which is about 17% higher than that of the GAN and LSTM. It has a certain guiding significance for coiled tubing operation, increasing operational safety and drilling efficiency, thus reducing production costs.
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spelling pubmed-103228492023-07-07 Parameter prediction of coiled tubing drilling based on GAN–LSTM Zhang, Wanxing Bai, Kai Zhan, Ce Tu, Binrui Sci Rep Article With the increasing development of coiled tubing drilling technology, the advantages of coiled tubing drilling technology are becoming more and more obvious. In the operation process of coiled tubing, Due to various different drilling parameters, manufacturing defects, and improper human handling, the coiled tubing can curl up and cause stuck drilling or shortened service life problems. Circulation pressure, wellhead pressure, and total weight have an important influence on the working period of coiled tubing. For production safety, this paper predicts circulation pressure, ROP, wellhead pressure, and finger weight using GAN–LSTM after studying drilling engineering theory and analyzing a large amount of downhole data. Experimental results show that GAN–LSTM can predict the parameters of circulation pressure, wellhead pressure ROP and total weight to a certain extent. After much training, the accuracy is about 90%, which is about 17% higher than that of the GAN and LSTM. It has a certain guiding significance for coiled tubing operation, increasing operational safety and drilling efficiency, thus reducing production costs. Nature Publishing Group UK 2023-07-05 /pmc/articles/PMC10322849/ /pubmed/37407667 http://dx.doi.org/10.1038/s41598-023-37960-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhang, Wanxing
Bai, Kai
Zhan, Ce
Tu, Binrui
Parameter prediction of coiled tubing drilling based on GAN–LSTM
title Parameter prediction of coiled tubing drilling based on GAN–LSTM
title_full Parameter prediction of coiled tubing drilling based on GAN–LSTM
title_fullStr Parameter prediction of coiled tubing drilling based on GAN–LSTM
title_full_unstemmed Parameter prediction of coiled tubing drilling based on GAN–LSTM
title_short Parameter prediction of coiled tubing drilling based on GAN–LSTM
title_sort parameter prediction of coiled tubing drilling based on gan–lstm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322849/
https://www.ncbi.nlm.nih.gov/pubmed/37407667
http://dx.doi.org/10.1038/s41598-023-37960-x
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