Postdoctoral Fellow at The University of Texas at Austin (UT Austin), United States
Weichen Zhan is a Postdoctoral Fellow at the Formation Evaluation Research Consortium within the Hildebrand Department of Petroleum and Geosystems Engineering at the University of Texas at Austin. His work centers on advancing computational geosciences with a focus on electromagnetic and acoustic inversion techniques, data-driven modeling, and reservoir characterization. Zhan has developed innovative numerical methods and algorithms that enhance interpretation accuracy of well logging data, driving forward the evaluation of subsurface formations. His interdisciplinary expertise merges geophysics, applied mathematics, and computer science, underpinned by extensive international academic and research experience.
Profile
Scopus
Education
Zhan earned his Ph.D. in Electromagnetic Field and Microwave Technology from Xiamen University, where he specialized in advanced numerical modeling of electromagnetic and seismic waves in porous media. His doctoral research, conducted under the supervision of Prof. Qing Huo Liu, focused on forward and inverse modeling of poroelastic waves using spectral element methods in the frequency domain, culminating in a thesis that proposed novel spectral element techniques for multiphase media. During his Ph.D., he was also a visiting student at the University of Alberta’s Signal Analysis and Imaging Group, where he contributed to the development of joint inversion algorithms integrating petrophysical and structural data. Earlier, he completed a double major bachelor’s degree in Geophysics and English at China University of Petroleum (East China), providing him a strong foundation in seismic exploration, signal processing, and geophysical inversion. His educational path included a formative period at the Norwegian University of Science and Technology, where he refined his skills in signal processing through adaptive local iterative filtering models.
Experience
Currently, Zhan serves as a core postdoctoral researcher within the Formation Evaluation Research Consortium at UT Austin, collaborating with leading experts to enhance electromagnetic and acoustic inversion methodologies for reservoir characterization. He has significantly contributed to the development of the 3DUTAPWeLS software, improving its ability to accurately interpret resistivity and sonic logging data. His practical experience is complemented by an internship at the Bureau of Geophysical Prospecting (BGP) of China National Petroleum Corporation, where he engaged with physical and chemical exploration techniques. These roles reflect a consistent trajectory of research excellence combined with hands-on applications in geophysical data analysis and reservoir evaluation.
Research Interests
Zhan’s research spans computational geosciences with a special emphasis on multiphysics forward simulations and joint inversion techniques that integrate diverse geophysical datasets. He applies deep learning and data-driven models to improve petrophysical property prediction and formation evaluation. His work focuses on electromagnetic and acoustic wave modeling, reservoir characterization, and well logging interpretation, all geared towards advancing the precision of formation evaluation. The integration of physics-based modeling with AI-driven approaches forms the core of his innovative contributions, addressing complex challenges in subsurface imaging and characterization.
Awards
Throughout his academic career, Zhan has received numerous honors reflecting his scholarly excellence. These include recognition as an Outstanding Graduate of Shandong Province and multiple prestigious scholarships such as those awarded by the China Scholarship Council for both undergraduate and doctoral studies. He also earned the First Prize Scholarship for Outstanding Learning and received honorable mention in interdisciplinary modeling contests. His academic achievements have been recognized at provincial mathematical competitions and through awards from the China National Petroleum Corporation. These accolades underscore his commitment to advancing geophysical research through rigorous scholarship and innovation.
Publications
Zhan has authored several influential publications in high-impact journals, contributing to the fields of geophysics and computational modeling. His key papers include:
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“Frequency domain spectral element method for modelling poroelastic waves in 3-D anisotropic, heterogeneous and attenuative porous media” (Geophysical Journal International, 2021), which has been cited widely for its novel approach to wave propagation in complex media.
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“Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints” (Journal of Applied Geophysics, 2024), integrating AI techniques for formation evaluation.
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“A hybrid implicit-explicit discontinuous Galerkin spectral element time domain (DG-SETD) method for computational elastodynamics” (Geophysical Journal International, 2023).
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“An efficient thin layer equivalent technique of SETD method for thermo-mechanical multi-physics analysis of electronic devices” (International Journal of Heat and Mass Transfer, 2022).
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“Shale pore pressure seismic prediction based on the Hydrogen generation and compaction-based rock physics model and Bayesian Hamiltonian Monte Carlo inversion method” (Geophysics, 2024).
These publications highlight his blend of theoretical advancements and practical applications in geophysical inversion and modeling.
Conclusion
Weichen Zhan stands out as a rising expert in the field of computational geosciences, combining deep theoretical knowledge with practical algorithm development for reservoir characterization and formation evaluation. His interdisciplinary background and extensive experience in numerical modeling, machine learning, and geophysical data interpretation position him to make lasting contributions to the oil and gas industry and academic research. Zhan’s innovative approaches and commitment to advancing the understanding of subsurface properties continue to drive impactful discoveries in electromagnetic and acoustic wave modeling.