Ying Da Wang

Dr. Ying Da Wang is a Lecturer in the School of Minerals and Energy Resources Engineering within the Faculty of Engineering at the University of New South Wales. She is based in the Tyree Energy Technologies Building and specializes in the intersection of artificial intelligence and petroleum engineering, with a focus on digital rock analysis and energy transition technologies.

• PhD: 2020, UNSW, Machine Learning Methods and Computationally Efficient Techniques in Digital Rock Analysis and micro-CT Imaging

Dr. Wang's research centers on applying machine learning techniques to solve complex problems in digital petrophysics and energy systems. Her work spans three primary areas: Machine Learning Methods for Digital Petrophysics, Efficient and Massive Computations of Pore Scale Flow, and Underground Hydrogen Storage. She has developed expertise in using computational fluid dynamics, particularly lattice Boltzmann and pore network modeling approaches, to analyze multiphase flow in porous media. Her research directly addresses critical challenges in carbon capture and storage, hydrogen storage, and enhanced oil recovery, contributing to Australia's energy transition and climate change mitigation strategies.

Analysis of Dr. Wang's publication record reveals a clear trajectory focusing on the integration of artificial intelligence with traditional petroleum engineering methods. Her recent work shows increasing emphasis on energy transition technologies, particularly carbon sequestration and hydrogen storage, reflecting the global shift toward sustainable energy solutions. The interdisciplinary nature of her research bridges computational science, machine learning, and subsurface engineering, creating novel approaches to longstanding challenges in the energy sector.

Dr. Wang actively supervises research projects with keywords including hydrogen storage, carbon capture and storage, artificial intelligence, artificial neural networks, reservoir simulations, multiphase flow, computational fluid dynamics, lattice Boltzmann, pore network, porous media, and x-ray CT image processing. She teaches courses in Enhanced Oil Recovery (2021), Production Engineering (2021), and Business Practices in the Petroleum Industry (2020, 2021), preparing the next generation of engineers for both traditional and emerging energy sectors.