Postdoctoral Research Fellow
Overview
Dr. Ruizhi Zhong is a postdoctoral research fellow at The University of Queensland (UQ). His research interests include drilling, machine learning, hydraulic fracturing, and geomechanics. His current work involves the application of machine learning for drilling applications. He holds a PhD degree in petroleum engineering from the University of Tulsa, a master's degree in petroleum and natural gas engineering and a master's degree in mechanical engineering from West Virginia University, and a bachelor's degree in theoretical and applied mechanics from Beijing Institute of Technology. He is a member of Society of Petroleum Engineers (SPE).
Dr. Zhong teaches Drilling Engineering and Well Test Analysis at UQ.
Qualifications
- Doctor of Philosophy, Tulsa
- Master of Science, Tulsa
Publications
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Journal Article: Prediction of methane adsorption in shale: classical models and machine learning based models
Meng, Meng, Zhong, Ruizhi and Wei, Zhili (2020). Prediction of methane adsorption in shale: classical models and machine learning based models. Fuel, 278 118358, 118358. doi: 10.1016/j.fuel.2020.118358
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Journal Article: Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting (XGBoost)
Zhong, Ruizhi, Johnson Jr, Raymond and Chen, Zhongwei (2020). Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting (XGBoost). International Journal of Coal Geology, 220 103416, 1-13. doi: 10.1016/j.coal.2020.103416
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Journal Article: Using machine learning methods to identify coal pay zones from drilling and Logging-While-Drilling (LWD) data
Zhong, Ruizhi, Johnson, Raymond L. and Chen, Zhongwei (2020). Using machine learning methods to identify coal pay zones from drilling and Logging-While-Drilling (LWD) data. SPE Journal, 25 (03), 1241-1258. doi: 10.2118/198288-pa
Grants
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Evaluating the bulk geomechanical properties of coals under changing net effective stress conditions
(2020–2021) Arrow Energy Pty Ltd
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Development of Rock Properties (RP) using existing field data and real-time drilling data
(2018–2021) Santos Limited
Supervision
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Dynamic modelling of coal seam gas production: Understanding how individual coal seams contribute to production profiles.
Doctor Philosophy
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Application of machine learning in horizontal drilling operations- drilling parameters optimization to reduce uncertainty or occurrence of undesirable downhole events
Doctor Philosophy
Available Projects
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Using machine learning models to determine rock properties from drilling data
This project aims to develop rock properties from drilling data and some LWD data. Machine learning models (ANN, SVM, random forest, XGBoost, etc.) are used. The conventional wireline log data will be used for evaluation.
Publications
Journal Article
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Prediction of methane adsorption in shale: classical models and machine learning based models
Meng, Meng, Zhong, Ruizhi and Wei, Zhili (2020). Prediction of methane adsorption in shale: classical models and machine learning based models. Fuel, 278 118358, 118358. doi: 10.1016/j.fuel.2020.118358
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Zhong, Ruizhi, Johnson Jr, Raymond and Chen, Zhongwei (2020). Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting (XGBoost). International Journal of Coal Geology, 220 103416, 1-13. doi: 10.1016/j.coal.2020.103416
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Zhong, Ruizhi, Johnson, Raymond L. and Chen, Zhongwei (2020). Using machine learning methods to identify coal pay zones from drilling and Logging-While-Drilling (LWD) data. SPE Journal, 25 (03), 1241-1258. doi: 10.2118/198288-pa
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Meng, Meng, Qiu, Zhengsong, Zhong, Ruizhi, Liu, Zhenguang, Liu, Yunfeng and Chen, Pengju (2019). Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach. Chemical Engineering Journal, 368, 847-864. doi: 10.1016/j.cej.2019.03.008
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Zhong, Ruizhi, Miska, Stefan, Yu, Mengjiao, Meng, Meng, Ozbayoglu, Evren and Takach, Nicholas (2019). Experimental investigation of fracture-based wellbore strengthening using a large-scale true triaxial cell. Journal of Petroleum Science and Engineering, 178, 691-699. doi: 10.1016/j.petrol.2019.03.081
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Johnson Jr, Raymond, Zhong, Ruizhi and Nguyen, Lan (2019). Results of hydraulic fracturing design improvements and changes in execution strategies for unconventional tight gas targets in the Cooper Basin, Australia. APPEA Journal, 59 (1), 244-259. doi: 10.1071/aj18185
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Coal identification using neural networks with real-time coalbed methane drilling data
Zhong, Ruizhi, Johnson Jr, Raymond, Chen, Zhongwei and Chand, Nathaniel (2019). Coal identification using neural networks with real-time coalbed methane drilling data. The APPEA Journal, 59 (1), 319-327. doi: 10.1071/aj18091
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An integrated fluid flow and fracture mechanics model for wellbore strengthening
Zhong, Ruizhi, Miska, Stefan, Yu, Mengjiao, Ozbayoglu, Evren and Takach, Nicholas (2018). An integrated fluid flow and fracture mechanics model for wellbore strengthening. Journal of Petroleum Science and Engineering, 167, 702-715. doi: 10.1016/j.petrol.2018.04.052
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Parametric study of controllable parameters in fracture-based wellbore strengthening
Zhong, Ruizhi, Miska, Stefan and Yu, Mengjiao (2017). Parametric study of controllable parameters in fracture-based wellbore strengthening. Journal of Natural Gas Science and Engineering, 43, 13-21. doi: 10.1016/j.jngse.2017.03.018
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Modeling of near-wellbore fracturing for wellbore strengthening
Zhong, Ruizhi, Miska, Stefan and Yu, Mengjiao (2017). Modeling of near-wellbore fracturing for wellbore strengthening. Journal of Natural Gas Science and Engineering, 38, 475-484. doi: 10.1016/j.jngse.2017.01.009
Conference Publication
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Using machine learning methods to identify coals from drilling and logging-while-drilling LWD data
Zhong, Ruizhi, Johnson Jr., Raymond L. and Chen, Zhongwei (2019). Using machine learning methods to identify coals from drilling and logging-while-drilling LWD data. SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, Brisbane, QLD, Australia, 18-19 November 2019. Tulsa, OK, United States: Unconventional Resources Technology Conference. doi: 10.15530/ap-urtec-2019-198288
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Investigation of fracture reopening pressure in wellbore strengthening
Zhong, Ruizhi, Miska, Stefan, Yu, Mengjiao, Ozbayoglu, Evren and Takach, Nicholas (2018). Investigation of fracture reopening pressure in wellbore strengthening. IADC/SPE Drilling Conference and Exhibition, Fort Worth, TX, United States, 6-8 March 2018. Richardson, TX, United States: Society of Petroleum Engineers. doi: 10.2118/189575-ms
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Johnson, Raymond L., Zhong, Ruizhi and Nguyen, Lan (2018). Well trajectory, completion and fracture design changes improve execution for deep unconventional tight gas targets in the cooper basin, Australia. SPE Hydraulic Fracturing Technology Conference and Exhibition 2019, HFTC 2019, The Woodlands, TX United States, 5 - 7 February 2019. Society of Petroleum Engineers. doi: 10.2118/194366-MS
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Numerical modeling of land subsidence resulting from oil production
Zhang, S., Zhong, R. and Liu, Y. (2016). Numerical modeling of land subsidence resulting from oil production. 50th US Rock Mechanics / Geomechanics Symposium 2016, Houston, TX, United States, June 26-June 29 2016. American Rock Mechanics Association (ARMA).
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Zhong, Ruizhi, Bao, Jinqing and Fathi, Ebrahim (2014). Fully coupled finite element model to study fault reactivation during multiple hydraulic fracturing in heterogeneous tight formations. SPE Eastern Regional Meeting, SPE Eastern Regional Meeting, SPE Eastern Regional Meeting. Richardson, TX, USA: Society of Petroleum Engineers (SPE).
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Microfluidic human blood plasma separation for lab on chip based heavy metal detections
Zhong, Ruizhi, Wu, Nianqiang and Liu, Yuxin (2012). Microfluidic human blood plasma separation for lab on chip based heavy metal detections. Sensors Based on Fluorescence, SERS, SPR, and Photoelectrochemistry, Boston MA, USA, 9-14 October 2011. doi: 10.1149/1.3697855
Grants (Administered at UQ)
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Evaluating the bulk geomechanical properties of coals under changing net effective stress conditions
(2020–2021) Arrow Energy Pty Ltd
-
Development of Rock Properties (RP) using existing field data and real-time drilling data
(2018–2021) Santos Limited
PhD and MPhil Supervision
Current Supervision
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Dynamic modelling of coal seam gas production: Understanding how individual coal seams contribute to production profiles.
Doctor Philosophy — Associate Advisor
Other advisors:
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Application of machine learning in horizontal drilling operations- drilling parameters optimization to reduce uncertainty or occurrence of undesirable downhole events
Doctor Philosophy — Associate Advisor
Other advisors:
Possible Research Projects
Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.
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Using machine learning models to determine rock properties from drilling data
This project aims to develop rock properties from drilling data and some LWD data. Machine learning models (ANN, SVM, random forest, XGBoost, etc.) are used. The conventional wireline log data will be used for evaluation.
