Risk-taking behavior of drilling workers: A study based on the structural equation model

Shi, Jingdong; Sun, Yaning; Su, Haijun; Wang, Yiru; Huang, Zijun; Gao, Lintao

doi:10.1016/j.ergon.2021.103219

Cited by 7 publications

(1 citation statement)

References 51 publications

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“…Therefore, a recurrent neural network model can be used to predict drilling parameters. The disadvantage of recurrent neural networks is that they are prone to the problems of gradient disappearance and gradient explosion, resulting in poor generalization of the model 11 – 13 . The properties of LSTM can compensate for the problems of recurrent neural networks in terms of a gradient.…”

Section: Gan and Lstm Fusionmentioning

confidence: 99%

Parameter prediction of coiled tubing drilling based on GAN–LSTM

Zhang

Kai

Chen

et al. 2023

Sci Rep

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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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Section: Gan and Lstm Fusionmentioning

confidence: 99%