• Title
    Research Scientist
  • Email
    tang39@llnl.gov
  • Phone
    (925) 422-0713
  • Organization
    Not Available

Research Interests

  • Data Assimilation and Uncertainty Quantification
  • Surrogate Modeling with Machine Learning / Deep Learning
  • Multi-Physics Numerical Simulation

Ph.D., Petroleum Engineering, Texas A&M University, 2019

M.Sc., Petroleum Engineering, Texas A&M Univeristy, 2016

B.S., Chemical Engineering, Tsinghua University, 2014

Google Scholar Page

  • Tang, H., Fu, P., Sherman, C.S., Zhang, J., Ju, X., Hamon, F., Azzolina, N.A., Burton-Kelly, M. and Morris, J.P., 2021. A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage. International Journal of Greenhouse Gas Control, 112, p.103488.
  • Tang, H., Zhang, S., Zhang, F. and Venugopal, S., 2019. Time series data analysis for automatic flow influx detection during drilling. Journal of Petroleum Science and Engineering, 172, pp.1103-1111.
  • Tang, H., Bailey, W.J., Stone, T. and Killough, J., 2019. A Unified Gas/Liquid Drift-Flux Model for All Wellbore Inclinations. SPE Journal.
  • Tang, H., Yan, B., Chai, Z., Zuo, L., Killough, J. and Sun, Z., 2019. Analyzing the well-interference phenomenon in the eagle ford shale/austin chalk production system with a comprehensive compositional reservoir model. SPE Reservoir Evaluation & Engineering, 22(03), pp.827-841.
  • Tang, H., Hasan, A.R. and Killough, J., 2018. Development and application of a fully implicitly coupled wellbore/reservoir simulator to characterize the transient liquid loading in horizontal gas wells. SPE Journal, 23(05), pp.1-615.
  • LLNL Physical and Life Sciences (PLS) Directorate Spot Award (2021)
  • LLNL Atmosphere Earth & Energy Division (AEED) Stand Out Award (2021)

Professional Services

  • Associate Editor, Journal of Petroleum Science and Engineering, 2019 – Present
  • Member of SPE Annual Technical Conference and Exhibition (ATCE) Program Committee, 2019 - Present
  • Chair of Machine Learning and Artificial Intelligence Applications for Petroleum Reservoirs Session at the 56th Rock Mechanics and Geomechanics Symposium (ARMA) 2020