Prof. Xuhai Tang

My research focuses on the physical-mechanical behaviour of geomaterials in deep reservoirs and deep space, as well as related microscale experiments and simulations. We develop advanced microscale experimental system to understand the properties of rock-forming minerals in unconventional samples such as deep-reservoir drill cuttings, meteorites, and lunar regolith. We also advance generative physical AI to establish high-fidelity digital rocks, for discovering new knowledge of rock mechanics under extreme conditions. Our research contributes to advancements in petroleum extraction and extraterrestrial in-situ resource utilization.

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undefinedxuhaitangtech@gmail.com

Biography

Employment

2017–Present       Professor, Wuhan University

2017                     Visiting Professor, Lawrence Berkeley National Laboratory

2016–2017           Research Fellow, Wuhan University

2016                     Visiting Professor, Monash University, Australia

2014–2016           Research Fellow, Princeton University, USA


Education

2010–2014           Ph.D., Imperial College London, UK
2006–2010           M.Sc., Sichuan University
2002–2006           B.Sc., Sichuan University


Academic and Professional ServiceEditorial Board Member, International Journal of Rock Mechanics and Mining Sciences
Guest Editor, Rock Mechanics and Rock Engineering
Guest Editor, Engineering Analysis with Boundary Elements
Associate Editor, Intelligent Geoengineering

Mineral-Rock Physics & Planetary Rock Mechanics

We developed a microscale rock mechanics experiment (micro-RME) to test the physical properties of rock-forming minerals under hydraulic, mechanical, thermal, and chemical loading. Based on the results of micro-RME, we then developed advanced upscaling methods to determine the macroscale physical and mechanical parameters of terrestrial and planetary geomaterials. This system is particularly useful for testing non-standard rock and soil samples, such as fracture fill, irregularly shaped cuttings, and samples from deep space.

AiFrac - Combining Generative AI Physical Modelling

With the combination of physical simulation and machine learning, the Aifrac simulator is developed to create the digital twin of reservoirs according to monitoring data. Advanced numerical algorithms, such as FEMM and phase field method, are developed to model the hydraulic fracturing. This achievement contributes to smarter energy oil/gas production and space exploitation.