Dr. Ruizhi Zhong is an Advance Queensland Industry Research Fellow (AQIRF) at UQ. His research interests include energy resources, machine learning, cloud computing, hydraulic fracturing, geomechanics, and microfludics. His current work involves the application of machine learning and data analytics for the energy industry. 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 taught Drilling Engineering and Well Test Analysis at UQ. He has more than 30 publications (book chapters, journal articles, and conference proceedings).
Journal Article: Efficient implicit methods for wellbore shear failure analysis during drilling and production in coalbeds
Zhong, Ruizhi, Mitchell, Travis, Johnson Jr., Raymond and Leonardi, Christopher (2022). Efficient implicit methods for wellbore shear failure analysis during drilling and production in coalbeds. International Journal of Rock Mechanics and Mining Sciences, 155 105129, 105129. doi: 10.1016/j.ijrmms.2022.105129
Book Chapter: Wellbore strengthening
Zhong, Ruizhi (2022). Wellbore strengthening. Drilling engineering: advanced applications and technology. (pp. 327-344) edited by Stefan Z. Miska, Robert F. Mitchell and Evren M. Ozbayoglu. New York, NY, United States: McGraw-Hill.
Conference Publication: UCS prediction by group-based machine learning method
Li, Jimmy Xuekai, Tsang, Matt, Giese, Stephen, Zhong, Ruizhi, Esterle, Joan, Pirona, Claire, Rajabi, Mojtaba and Chen, Zhongwei (2022). UCS prediction by group-based machine learning method. Resource Operators Conference, Virtual, 10-11 February 2022. Wollongong, NSW, Australia: University of Wollongong and University of Southern Queensland.
Using artificial intelligence to increase gas supply
(2022–2024) Advance Queensland Industry Research Fellowships
Reducing the Subjectivity of CMRR Calculation Using Machine Learning
(2021–2022) Australian Coal Association Research Program
Senex Drilling - Machine Learning Project
(2021–2022) Senex Energy Limited
Stress-dependent flow behaviour in coal seam gas reservoirs: Implications for gas production and reservoir stimulation
Doctor Philosophy
Incorporating Machine Learning and Data Analysis to Predict Long Term Production from Coal Seam Gas Fields Based on Early Field Data
Doctor Philosophy
Using Machine Learning to Improve Well Integrity, Drilling Efficiencies and Safety Compliance in Queensland CSG Drilling Data
Doctor Philosophy
Zhong, Ruizhi (2022). Wellbore strengthening. Drilling engineering: advanced applications and technology. (pp. 327-344) edited by Stefan Z. Miska, Robert F. Mitchell and Evren M. Ozbayoglu. New York, NY, United States: McGraw-Hill.
Zhong, Ruizhi, Mitchell, Travis, Johnson Jr., Raymond and Leonardi, Christopher (2022). Efficient implicit methods for wellbore shear failure analysis during drilling and production in coalbeds. International Journal of Rock Mechanics and Mining Sciences, 155 105129, 105129. doi: 10.1016/j.ijrmms.2022.105129
Time-dependent coal permeability: impact of gas transport from coal cleats to matrices
Wang, Chunguang, Zhang, Jidong, Zang, Yuxiao, Zhong, Ruizhi, Wang, Jianguo, Wu, Yu, Jiang, Yujing and Chen, Zhongwei (2021). Time-dependent coal permeability: impact of gas transport from coal cleats to matrices. Journal of Natural Gas Science and Engineering, 88 103806, 103806. doi: 10.1016/j.jngse.2021.103806
Wang, Chunguang, Zhang, Jidong, Chen, Junguo, Zhong, Ruizhi, Cui, Guanglei, Jiang, Yujing, Liu, Weitao and Chen, Zhongwei (2021). Understanding competing effect between sorption swelling and mechanical compression on coal matrix deformation and its permeability. International Journal of Rock Mechanics and Mining Sciences, 138 104639, 104639. doi: 10.1016/j.ijrmms.2021.104639
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
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
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
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
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
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
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
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
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
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
UCS prediction by group-based machine learning method
Li, Jimmy Xuekai, Tsang, Matt, Giese, Stephen, Zhong, Ruizhi, Esterle, Joan, Pirona, Claire, Rajabi, Mojtaba and Chen, Zhongwei (2022). UCS prediction by group-based machine learning method. Resource Operators Conference, Virtual, 10-11 February 2022. Wollongong, NSW, Australia: University of Wollongong and University of Southern Queensland.
Case Study and Sensitivity Analysis of Borehole Breakout in the Cooper Basin, Australia
Zhong, Ruizhi, Azman, Aideel, Johnson, Ray, You, Zhenjiang and Nguyen, Lan (2021). Case Study and Sensitivity Analysis of Borehole Breakout in the Cooper Basin, Australia. SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, Online, 16-18 November 2021. Tulsa, OK United States: Unconventional Resources Technology Conference. doi: 10.15530/ap-urtec-2021-208373
Leonardi, C. R., Zhong, R., Mitchell, T. R., Chen, Z., Johnson, R. L., O'Hagan, D. and Flottmann, T. (2021). Analytical and numerical assessment of the influence of bulk coal strength on drilling operations in unconventional coal seam gas wells. 55th US Rock Mechanics/Geomechanics Symposium, Houston, TX USA, 20-23 June 2021. Alexandria, VA USA: American Rock Mechanics Association.
Improving estimation of rock mechanical properties using machine learning
Zhong, Ruizhi, Tsang, Matt, Makusha, Gift, Yang, Ben and Chen, Zhongwei (2021). Improving estimation of rock mechanical properties using machine learning. Resource Operators Conference, Virtual, 11-12 February 2021. Wollongong, Australia: University of Wollongong/University of Southern Queensland.
Mitchell, Travis R., Zhong, Ruizhi, Johnson, Ray and Leonardi, Christopher R. (2021). Numerical evaluation of bulk geomechanical properties of fractured coal with changing net effective stress conditions. SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, Virtual, 16-18 November 2021. Tulsa, OK, United States: Unconventional Resources Technology Conference. doi: 10.15530/ap-urtec-2021-208346
Using machine learning for geosteering during in-seam drilling
Zhong, Ruizhi, Johnson Jr, Ray L. and Chen, Zhongwei (2021). Using machine learning for geosteering during in-seam drilling. SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, Virtual, 16–18 November, 2021. Tulsa, OK, United States: Unconventional Resources Technology Conference. doi: 10.15530/ap-urtec-2021-208298
Wellbore stability analysis of horizontal drilling in Bowen and Surat coal seam gas wells
Zhong, Ruizhi, Leonardi, Christopher R., Mitchell, Travis R. and Johnson, Ray L. (2021). Wellbore stability analysis of horizontal drilling in Bowen and Surat coal seam gas wells. SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, Virtual, 16-18 November 2021. Tulsa, OK, United States: Unconventional Resources Technology Conference. doi: 10.15530/ap-urtec-2021-208399
Estimating Coal Permeability Using Machine Learning Methods
Salehi, Cyrus, Zhong, Ruizhi, Ganpule, Sameer, Dewar, Steven, Johnson, Raymond and Chen, Zhongwei (2020). Estimating Coal Permeability Using Machine Learning Methods. SPE Asia Pacific Oil & Gas Conference and Exhibition, Online, 17-19 November 2020. Richardson, TX United States: Society of Petroleum Engineers. doi: 10.2118/202271-ms
Parametric study of in-situ stresses in depleted reservoirs
Li, Zhonghui, Zhong, Ruizhi, Lou, Yishan, Lu, Jun and Ni, Yafei (2019). Parametric study of in-situ stresses in depleted reservoirs. International Conference and Exhibition on Computational Biology and Bioinformatics , Taipei, Taiwan, 1-2 December 2019. Chichester, West Sussex, United Kingdom: Wiley-Blackwell.
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
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
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
A leak-off model for critical permeability in wellbore strengthening applications
Zhong, Ruizhi, Miska, Stefan, Yu, Mengjiao, Ozbayoglu, Evren, Zhang, Jianguo and Majidi, Reza (2017). A leak-off model for critical permeability in wellbore strengthening applications. AADE National Technical Conference and Exhibition, Houston, TX, United States, 11-12 April 2017.
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).
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). doi: 10.2118/171035-ms
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
Using artificial intelligence to increase gas supply
(2022–2024) Advance Queensland Industry Research Fellowships
Reducing the Subjectivity of CMRR Calculation Using Machine Learning
(2021–2022) Australian Coal Association Research Program
Senex Drilling - Machine Learning Project
(2021–2022) Senex Energy Limited
Evaluating the bulk geomechanical properties of coals under changing net effective stress conditions
(2020–2022) Arrow Energy Pty Ltd
(2020–2022) Arrow Energy Pty Ltd
Development of Rock Properties (RP) using existing field data and real-time drilling data
(2018–2021) Santos Limited
Stress-dependent flow behaviour in coal seam gas reservoirs: Implications for gas production and reservoir stimulation
Doctor Philosophy — Associate Advisor
Other advisors:
Incorporating Machine Learning and Data Analysis to Predict Long Term Production from Coal Seam Gas Fields Based on Early Field Data
Doctor Philosophy — Associate Advisor
Other advisors:
Using Machine Learning to Improve Well Integrity, Drilling Efficiencies and Safety Compliance in Queensland CSG Drilling Data
Doctor Philosophy — Associate Advisor
Other advisors:
Coal seam gas reservoir characterisation with machine learning
Doctor Philosophy — Associate Advisor
Other advisors:
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: