Zheng Zhang
- Courses2
- Reviews6
- School: Texas A&M University
- Campus: College Station
- Department: Mathematics
- Email address: Join to see
- Phone: Join to see
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Location:
College Station, TX - Dates at Texas A&M University: May 2016 - May 2018
- Office Hours: Join to see
N/A
Would take again: Yes
For Credit: Yes
0
0
Not Mandatory
Awesome
She is an incredible person with a clear anticipation, fair quizzes, and test. Just attend class and follow what he's doing and it;s an easy A. Try to do HW or at least understand how to solve the problem and your good.
Biography
Zheng Zhang is a/an Assistant Professor (Business/Economics/Engineerin in the University Of California department at University Of California
Texas A&M University College Station - Mathematics
Experience
Texas A&M University
Research Assistant, website coordinator of MCERI
- Dissertation: History Matching and Optimization Using Stochastic Methods: Applications to Chemical Flooding (Advisor: Dr. Akhil Datta-Gupta)
·Identified first level and second level key parameters in ASP flooding through sensitivity study
·Constructed proxy using experimental design and response surface method
·Calibrated parameters hierarchically conditioned to observed data using genetic algorithm with proxy check
·Optimized ASP flooding by Pareto-based multi-objective method
- Other Projects:
·Wrote a Matlab 3D reservoir simulator using fully implicit formulation by automatic differentiation
·Upscaled fine permeability model to coarsened mesh by flow-based algorithms
·Visualized well-pairs and well rate allocation factors for each time step by tracing streamlines
·History matched water-cut using streamline-based analytical sensitivity and generalized travel time inversion (GTTI) method
·Improved model calibration software S3D-INV for waterflooding management by integrating GTTI methodQRI
Senior advisor in Reservoir engineering
·Applied and developed fit-for-purpose capabilities in QRI inhouse tool SpeedWise® in client assets on field performance analysis, well test analysis, decline curve analysis, material balance, production forecast and economic evaluation, etc.
- Developed 5 offshore assets covering 1 conventional, 2 GOM deep-water, 2 gas condensate reservoirs
- Delivered quality solutions before deadline and presented in client workshop
·Evaluated development strategies, and proposed new well and workover opportunities that maximize client field oil recoveries.TOTAL
Reservoir Engineer Intern
- Natural Fracture Identification and Reservoir Modeling in Utica Shale, Ohio
·Identified natural fractures based on rock mechanical properties, relationship between stress anisotropy and well productivity, FMI, After-Closure-Analysis on DFIT data, and flowback data analysis
·Built simulation model for multi-stage fractured horizontal well in naturally fractured shale reservoir and calibrated the model to integrate dynamic production data
·Forecast EUR with uncertainty analysisBHP Billiton
Reservoir Engineering Intern
- Uncertainty Analysis and Assisted History Matching Workflows in Unconventional Shale Oil Reservoirs
·Identified key parameters by sensitivity analysis and studied well/reservoir behavior of a horizontal well in Eagle Ford
·Established two workflows of model calibration under multiple geological scenarios
·Forecast EUR under two workflows and compared with EUR from DCAConocoPhillips
Senior Reservoir Engineer
Project lead responsible to coordinate among business unit, lab, third party contractors, and JIP meetings
·Evaluated the performance of reservoir management strategies in four Permian fields of ConocoPhillips
·Developed effective workflows and tools to monitor pattern performance, provide WAG management and surveillance for East Vacuum Grayburg San Andres Unit, Permian
·Optimized CO2 WAG scheduling to maximize sweep efficiency and improve recovery
·Identified candidate wells with conformance issues and proposed solutions based on reservoir and wellbore understanding in Permian fields
·Participated the execution of MARCIT gel and foamed cement squeeze to a well with high GOR
·Evaluated and predicted foam performance for Kuparuk, Alaska by building empirical foam model using CMG-GEM
Education
China University of Petroleum (Beijing)
Bachelor's degree
Petroleum Engineering
• B.S. Thesis: Experimental and Numerical Studies of Polymer Flooding in Reservoir of Ultra High Water Cut StageChina University of Petroleum (Beijing)
Master's degree
Petroleum Engineering
• M.S. Thesis: Reservoir Characterization and Development in Wu420 Block of Jiyuan OilfieldTexas A&M University
PhD candidate
Petroleum Engineering
• Dissertation: History Matching and Optimization Using Stochastic Methods: Applications to Chemical FloodingTexas A&M University
Research Assistant, website coordinator of MCERI
- Dissertation: History Matching and Optimization Using Stochastic Methods: Applications to Chemical Flooding (Advisor: Dr. Akhil Datta-Gupta) ·Identified first level and second level key parameters in ASP flooding through sensitivity study ·Constructed proxy using experimental design and response surface method ·Calibrated parameters hierarchically conditioned to observed data using genetic algorithm with proxy check ·Optimized ASP flooding by Pareto-based multi-objective method - Other Projects: ·Wrote a Matlab 3D reservoir simulator using fully implicit formulation by automatic differentiation ·Upscaled fine permeability model to coarsened mesh by flow-based algorithms ·Visualized well-pairs and well rate allocation factors for each time step by tracing streamlines ·History matched water-cut using streamline-based analytical sensitivity and generalized travel time inversion (GTTI) method ·Improved model calibration software S3D-INV for waterflooding management by integrating GTTI method
Publications
Uncertainty Analysis and Assisted History Matching Workflow in Shale Oil Reservoirs
SPE Unconventional Resources Technology Conference
In this paper, we present two workflows to utilize a stochastic history matching method to a multi-fracture horizontal well in Eagle Ford shale oil reservoir. First, we discuss the impact of reservoir properties, hydraulic fractures, microfracs, phase behavior and rock characteristics on production behavior using sensitivity analysis. Next, we use the key uncertainties to calibrate the model against historical data using genetic algorithms. Three different geo-models were considered in all cases. However, in one workflow, they were evolved separately while in another one, they were evolved as a group. Production forecasting based on updated models from both workflows were categorized into several groups using cluster analysis. Then, the suggested workflows were compared according to their advantages and limitations. The results indicated that for workflow I, inaccuracy in uncertainty ranges could results in an incomplete set of updated models during evolution. For workflow II, reasonable probability must be provided; otherwise good model for certain geo-models may be ignored because the results could be constrained by less-probable geo-models. For unconventional reservoirs with very short limited static and dynamic data, our proposed workflows provide a flexible framework for capturing key uncertainties. Thus, they can be applied flexibly for long-term production forecasting or for identifying key areas for further data acquisition.
