Hewei Tang
Assistant Professor
Research Areas
Machine/Deep Learning
Geologic Carbon Storage
Geothermal Energy
Unconventional Resources
Integrated Reservoir Characterization
Drilling and Completions
Reservoir Simulation
Educational Qualifications
PhD, Petroleum Engineering, Texas A&M University, 2019.
MS, Petroleum Engineering, Texas A&M University, 2016.
BS, Chemical and Biomolecular Engineering, Tsinghua University, 2014.
PGE Courses
PGE 379: Subsurface Machine Learning
PGE 338: Data Analytics and Geostatistics
Research
Dr. Tang conducts numerical simulation and experimental study of coupled thermal-hydro-mechanical-chemical (THMC) processes of subsurface energy systems, including geothermal energy systems, geological carbon storage, and produced water management. She leverages machine/deep learning methods for monitoring data assimilation, dynamic reservoir characterization, and applications in drilling and completions. Before joining the UT PGE faculty in August 2024, Dr. Tang was a staff scientist at Lawrence Livermore National Laboratory.
Awards & Honors
Lawrence Livermore National Laboratory Physical and Life Sciences Directorate Award, 2023.
Lawrence Livermore National Laboratory Physical and Life Sciences Directorate Spot Award, 2021.
Executive Editor, Geoenergy Science and Engineering.
Associate Editor, Journal of Petroleum Science and Engineering.
Co-Chair, Technical Committee on Artificial Intelligence and Data (TCAID) of ARMA.
Highlighted Publications and Google Scholar Profile
Tang, H., Kong, Q., Morris, Joe. 2024. Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon Storage. Journal of Hydrology, 629, p.130641.
Tang, H., Fu, P., Jo, H., et al. 2022. Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR. International Journal of Greenhouse Gas Control 120: 103765.
Tang, H., Fu, P., Sherman, C.S., et al. 2021. A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage. International Journal of Greenhouse Gas Control 112: 103488
Tang, H., Zhang, S., Zhang, F. et al., 2019. Time Series Data Analysis for Automatic Real-Time Flow Influx Detection during Drilling. Journal of Petroleum Science and Engineering 172: 1103-1111.
Tang, H., Xu, B., A. Rashid., et al., 2019. Modeling Wellbore Heat Exchangers: Fully Numerical to Fully Analytical Solutions. Renewable Energy 133: 1124-1135.
Tang, H., Bailey, W. J., Stone, T., & Killough, J. 2019. A Unified Gas/Liquid Drift-Flux Model for All Wellbore Inclinations. SPE Journal 24(06):2911-2928. SPE-197068-PA.
Tang, H., Yan, B., Chai, Z.et al, 2019. Analyzing the Well Interference Phenomenon in Eagle Ford Shale – Austin Chalk Production System with a Comprehensive Compositional Reservoir Model. SPE Reservoir Evaluation & Engineering 22 (03): 827-841. SPE-191381-PA.