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Proceedings Papers

Enhancing Rate of Penetration Predictions in the Surat Basin Using Supervised Machine Learning: A Comparative Study of ANN and XGBoost Models

Mu Chang, Ruizhi Zhong, Ray Johnson, Zhongwei Chen
Paper presented at the Mediterranean Offshore Conference, October 20–22, 2024
Paper Number: SPE-223344-MS
https://doi.org/10.2118/223344-MS
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View Paper titled, Enhancing Rate of Penetration Predictions in the Surat Basin Using Supervised Machine Learning: A Comparative Study of ANN and XGBoost Models
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Proceedings Papers

Estimation of Rock Permeability Scores Using Machine Learning Methods

Roohullah Qalandari, Ruizhi Zhong, Cyrus Salehi, Nathaniel Chand, Raymond Leslie Johnson, Gonzalo Vazquez, Jack Mclean-Hodgson, Joel Zimmerman
Paper presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, October 17–19, 2022
Paper Number: SPE-210711-MS
https://doi.org/10.2118/210711-MS
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Proceedings Papers

Study on the Labyrinth Screw Pump for Heavy Oil Production

Zhuoyu Wang, Jianjun Zhu, Guangqiang Cao, Hongwu Zhu, Ruizhi Zhong, Haiwen Zhu
Paper presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, October 17–19, 2022
Paper Number: SPE-210687-MS
https://doi.org/10.2118/210687-MS
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Proceedings Papers

Real Time, Automatic Injecting of Foaming Agent for Gas Deliquification in CQQK Gas Field, China: A Case Study

Guangqiang Cao, Nan Li, Haoyu Wang, Ruizhi Zhong, Tao Shi
Paper presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, October 17–19, 2022
Paper Number: SPE-210756-MS
https://doi.org/10.2118/210756-MS
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View Paper titled, Real Time, Automatic Injecting of Foaming Agent for Gas Deliquification in CQQK Gas Field, China: A Case Study
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Proceedings Papers

Using Machine Learning for Geosteering During In-Seam Drilling

Ruizhi Zhong, Ray L. Johnson Jr, Zhongwei Chen
Paper presented at the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, November 16–18, 2021
Paper Number: URTEC-208298-MS
https://doi.org/10.15530/AP-URTEC-2021-208298
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View Paper titled, Using Machine Learning for Geosteering During In-Seam Drilling
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Proceedings Papers

Numerical Evaluation of Bulk Geomechanical Properties of Fractured Coal with Changing Net Effective Stress Conditions

Travis R. Mitchell, Ruizhi Zhong, Ray Johnson, Christopher R. Leonardi
Paper presented at the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, November 16–18, 2021
Paper Number: URTEC-208346-MS
https://doi.org/10.15530/AP-URTEC-2021-208346
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View Paper titled, Numerical Evaluation of Bulk Geomechanical Properties of Fractured Coal with Changing Net Effective Stress Conditions
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Proceedings Papers

Case Study and Sensitivity Analysis of Borehole Breakout in the Cooper Basin, Australia

Ruizhi Zhong, Aideel Azman, Ray Johnson, Zhenjiang You, Lan Nguyen
Paper presented at the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, November 16–18, 2021
Paper Number: URTEC-208373-MS
https://doi.org/10.15530/AP-URTEC-2021-208373
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View Paper titled, Case Study and Sensitivity Analysis of Borehole Breakout in the Cooper Basin, Australia
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Proceedings Papers

Wellbore Stability Analysis of Horizontal Drilling in Bowen and Surat Coal Seam Gas Wells

Ruizhi Zhong, Christopher R. Leonardi, Travis R. Mitchell, Ray L. Johnson
Paper presented at the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, November 16–18, 2021
Paper Number: URTEC-208399-MS
https://doi.org/10.15530/AP-URTEC-2021-208399
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View Paper titled, Wellbore Stability Analysis of Horizontal Drilling in Bowen and Surat Coal Seam Gas Wells
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Proceedings Papers

Estimating Coal Permeability Using Machine Learning Methods

Cyrus Salehi, Ruizhi Zhong, Sameer Ganpule, Steven Dewar, Raymond Johnson, Zhongwei Chen
Paper presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, November 17–19, 2020
Paper Number: SPE-202271-MS
https://doi.org/10.2118/202271-MS
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Journal Articles

Using Machine Learning Methods To Identify Coal Pay Zones from Drilling and Logging-While-Drilling (LWD) Data

Ruizhi Zhong, Raymond L. Johnson, Jr., Zhongwei Chen
Journal: SPE Journal
Publisher: Society of Petroleum Engineers (SPE)
SPE J. 25 (03): 1241–1258.
Paper Number: SPE-198288-PA
https://doi.org/10.2118/198288-PA
Published: 11 June 2020
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View article titled, Using Machine Learning Methods To Identify Coal Pay Zones from Drilling and Logging-While-Drilling (LWD) Data
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Proceedings Papers

Using Machine Learning Methods to Identify Coals from Drilling and Logging-While-Drilling LWD Data

Ruizhi Zhong, Raymond L. Johnson, Zhongwei Chen
Paper presented at the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, November 18–19, 2019
Paper Number: URTEC-198288-MS
https://doi.org/10.15530/AP-URTEC-2019-198288
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View Paper titled, Using Machine Learning Methods to Identify Coals from Drilling and Logging-While-Drilling LWD Data
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Proceedings Papers

Well Trajectory, Completion and Fracture Design Changes Improve Execution for Deep Unconventional Tight Gas Targets in the Cooper Basin, Australia

Raymond L. Johnson, Ruizhi Zhong, Lan Nguyen
Paper presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, February 5–7, 2019
Paper Number: SPE-194366-MS
https://doi.org/10.2118/194366-MS
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View Paper titled, Well Trajectory, Completion and Fracture Design Changes Improve Execution for Deep Unconventional Tight Gas Targets in the Cooper Basin, Australia
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Proceedings Papers

Investigation of Fracture Reopening Pressure in Wellbore Strengthening

Ruizhi Zhong, Stefan Miska, Mengjiao Yu, Evren Ozbayoglu, Nicholas Takach
Paper presented at the IADC/SPE Drilling Conference and Exhibition, March 6–8, 2018
Paper Number: SPE-189575-MS
https://doi.org/10.2118/189575-MS
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View Paper titled, Investigation of Fracture Reopening Pressure in Wellbore Strengthening
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Proceedings Papers

Fully Coupled Finite Element Model to Study Fault Reactivation during Multiple Hydraulic Fracturing in Heterogeneous Tight Formations

Ruizhi Zhong, Jinqing Bao, Ebrahim Fathi
Paper presented at the SPE Eastern Regional Meeting, October 21–23, 2014
Paper Number: SPE-171035-MS
https://doi.org/10.2118/171035-MS
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View Paper titled, Fully Coupled Finite Element Model to Study Fault Reactivation during Multiple Hydraulic Fracturing in Heterogeneous Tight Formations
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