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Research Article | Open Access

Intelligent optimization for a full-sized PDC bit with composite percussive rock breaking drilling

Liping Li1Xuegang Liu1Yu Zhou2Chengshuai Qin1Zhihui Liu1Lige Wang1Chongqiang Zhu3Zizheng Sun1( )
School of Qilu Transportation, Shandong University, Jinan 250002, China
Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK
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Abstract

Intelligent drilling has obtained a lot of attention in mining engineering, oil and gas production, and tunnelling engineering. The optimization of drilling parameters is one of the most important technologies of intelligent drilling while determining the key values to improve the drilling efficiency is still a difficulty. In this paper, we established a composite percussive rock breaking model of a full-size polycrystalline diamond compact (PDC) bit based on the continuous–discontinuous element method (CDEM). The evolution of rock fragmentation under the action of composite impact is analysed by the rock breaking volume and the torque applied on the drill bit during drilling. Based on actual construction, two key parameters, axial impact velocity and impact frequency, affecting the process of composite impact rock breaking were selected, to further investigate their effect on composite impact rock breaking. The fitting curves obtained from the sensitivity analysis of the rock breaking effect under different construction parameters were proposed to guide the intelligent drilling. Furthermore, the construction parameters of a diversion inclined shaft in the #2 construction branch hole of Tiantai Mountain pumped storage power station in Zhejiang Province were collected, which provides validation data for the proposed optimizing theory of the drilling parameters.

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Journal of Intelligent Construction
Pages 9180021-9180021
Cite this article:
Li L, Liu X, Zhou Y, et al. Intelligent optimization for a full-sized PDC bit with composite percussive rock breaking drilling. Journal of Intelligent Construction, 2023, 1(4): 9180021. https://doi.org/10.26599/JIC.2023.9180021

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Received: 07 September 2023
Revised: 07 October 2023
Accepted: 10 October 2023
Published: 01 December 2023
© The Author(s) 2023. Published by Tsinghua University Press.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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