Articles citing this paper
Coal identification using neural networks with real-time coalbed methane drilling data
Ruizhi Zhong A C , Raymond Johnson
+ Author Affiliations
- Author Affiliations
A School of Chemical Engineering, University of Queensland, Brisbane, Qld 4072, Australia.
B School of Mechanical and Mining Engineering, University of Queensland, Brisbane, Qld 4072, Australia.
C Corresponding author. Email: r.zhong@uq.edu.au
The APPEA Journal 59(1) 319-327 https://doi.org/10.1071/AJ18091
Submitted: 7 December 2018 Accepted: 28 January 2019 Published: 17 June 2019
11 articles found in Crossref database.
Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies
All Days (2024)
Estimation of Rock Permeability Scores Using Machine Learning Methods
Day 2 Tue, October 18, 2022 (2022)
Automatic coal mine roof rating calculation using machine learning
Li Jimmy Xuekai,
Tsang Matt,
Zhong Ruizhi,
Esterle Joan,
Pirona Claire,
Rajabi Mojtaba, Chen Zhongwei
International Journal of Coal Geology. 2023 274 p.104292
Using Machine Learning Methods To Identify Coal Pay Zones from Drilling and Logging-While-Drilling (LWD) Data
Zhong Ruizhi ,
Johnson Raymond L., Chen Zhongwei
SPE Journal. 2020 25(03). p.1241
A novel workflow based on physics-informed machine learning to determine the permeability profile of fractured coal seams using downhole geophysical logs
Lv Adelina,
Cheng Lei,
Aghighi Mohammad Ali,
Masoumi Hossein, Roshan Hamid
Marine and Petroleum Geology. 2021 131 p.105171
A data-driven field-scale approach to estimate the permeability of fractured rocks
Li Danqi,
Shirani Faradonbeh Roohollah,
Lv Adelina,
Wang Xiangyu, Roshan Hamid
International Journal of Mining, Reclamation and Environment. 2022 36(10). p.671
Automated Well-Log Processing and Lithology Classification by Identifying Optimal Features Through Unsupervised and Supervised Machine-Learning Algorithms
Singh Harpreet ,
Seol Yongkoo , Myshakin Evgeniy M.
SPE Journal. 2020 25(05). p.2778
Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review
Yan Yongliang,
Borhani Tohid N.,
Subraveti Sai Gokul,
Pai Kasturi Nagesh,
Prasad Vinay,
Rajendran Arvind,
Nkulikiyinka Paula,
Asibor Jude Odianosen,
Zhang Zhien,
Shao Ding,
Wang Lijuan,
Zhang Wenbiao,
Yan Yong,
Ampomah William,
You Junyu,
Wang Meihong,
Anthony Edward J.,
Manovic Vasilije, Clough Peter T.
Energy & Environmental Science. 2021 14(12). p.6122
Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach
Meng Meng,
Qiu Zhengsong,
Zhong Ruizhi,
Liu Zhenguang,
Liu Yunfeng, Chen Pengju
Chemical Engineering Journal. 2019 368 p.847
Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting (XGBoost)
Zhong Ruizhi,
Johnson Raymond, Chen Zhongwei
International Journal of Coal Geology. 2020 220 p.103416
A Coarse-to-Fine Approach for Intelligent Logging Lithology Identification with Extremely Randomized Trees
Xie Yunxin,
Zhu Chenyang,
Hu Runshan, Zhu Zhengwei
Mathematical Geosciences. 2021 53(5). p.859
