Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA-Reference-Cited by-同舟云学术

Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA

Published:2022-01-04 Issue:5 Volume:40 Page:534-555
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Shahani Niaz Muhammad¹²^ORCID,Kamran Muhammad³,Zheng Xigui¹²⁴⁵^ORCID,Liu Cancan¹²

Affiliation:

1. School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu Province, China

2. The State Key Laboratory for Geo Mechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou, China

3. Institut Teknologi Bandung, Department of Mining Engineering, Bandung Institute of Technology, Bandung, Indonesia

4. School of Mines and Civil Engineering, Liupanshui Normal University, Liupanshui, China

5. Guizhou Guineng Investment Co., Ltd, Liupanshui, China

Funder

the Science and Technology Innovation Project of Guizhou Province (Qiankehe Platform Talent

Publisher

Informa UK Limited

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2021.2003386

Reference46 articles.

1. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

2. Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

3. Machine learning methods applied to drilling rate of penetration prediction and optimization - A review

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