Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1785068847826468864 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10322849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhangwanxing parameterpredictionofcoiledtubingdrillingbasedonganlstm AT baikai parameterpredictionofcoiledtubingdrillingbasedonganlstm AT zhance parameterpredictionofcoiledtubingdrillingbasedonganlstm AT tubinrui parameterpredictionofcoiledtubingdrillingbasedonganlstm |