Postdoctoral Research Fellow
Overview
Dr. Ruizhi Zhong is a postdoctoral research fellow at The University of Queensland. 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 in petroleum engineering from the University of Tulsa and an MS degree in petroleum and natural gas engineering from West Virginia University. He is a member of Society of Petroleum Engineers (SPE).
Qualifications
- Doctor of Philosophy, Tulsa
- Master of Science, Tulsa
Publications
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Journal Article: 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 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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Journal Article: Experimental investigation of fracture-based wellbore strengthening using a large-scale true triaxial cell
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
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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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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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. In: IADC/SPE Drilling Conference and Exhibition. IADC/SPE Drilling Conference and Exhibition, Fort Worth, TX, United States, (). 6-8 March 2018. 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. In: Society of Petroleum Engineers - SPE Hydraulic Fracturing Technology Conference and Exhibition 2019, HFTC 2019. SPE Hydraulic Fracturing Technology Conference and Exhibition 2019, HFTC 2019, The Woodlands, TX United States, (). 5 - 7 February 2019. 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. In: 50th US Rock Mechanics / Geomechanics Symposium 2016. 50th US Rock Mechanics / Geomechanics Symposium 2016, Houston, TX, United States, (499-508). June 26-June 29 2016.
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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. In: SPE Eastern Regional Meeting, SPE Eastern Regional Meeting, (418-429). SPE Eastern Regional Meeting.
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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. In: Sensors Based on Fluorescence, SERS, SPR, and Photoelectrochemistry, Boston MA, USA, (11-16). 9-14 October 2011. doi:10.1149/1.3697855
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.
