Research Scientist | Oak Ridge National Laboratory | United States
Dr. Ming Fan is a Research Scientist at Oak Ridge National Laboratory (ORNL), where he leads cutting-edge research at the intersection of computational science, machine learning, and sustainable energy systems. He earned his Ph.D. in Geoenergy Engineering from Virginia Tech, after completing an M.S. in Petroleum and Natural Gas Engineering at West Virginia University and a B.S. in Resources Exploration Engineering at the China University of Mining and Technology. Professionally, Dr. Fan has developed an impressive portfolio of research spanning machine learning, deep learning, explainable AI, uncertainty quantification, and energy system modeling, with applications in climate prediction, water resource management, CO₂ and hydrogen storage, and geothermal energy. His expertise lies in advancing both theory and practical applications, integrating data-driven models with large-scale simulations to address critical challenges in energy transition and climate science. His research skills include high-performance computing, uncertainty-aware modeling, advanced geoscientific simulations, and AI-enabled decision support, which he has demonstrated in projects funded by the U.S. Department of Energy. Dr. Fan’s professional contributions extend beyond research through his roles as an active reviewer for leading journals, guest editor, NSF proposal panelist, and session organizer at major conferences such as AGU, ICDM, NeurIPS, and ICLR. His achievements have earned him prestigious recognitions, including being a Finalist for the ACM Gordon Bell Climate Modeling Prize and receiving the HPCwire Top Supercomputing Achievement Award. These awards highlight his ability to push the boundaries of computational geoscience while making tangible impacts on real-world energy and climate challenges. Dr. Fan’s academic impact is further reflected in his growing recognition with 640 citations, 38 documents, and an h-index of 15, demonstrating his influential role in advancing computational science, energy systems modeling, and sustainable resource management.
Profiles: Google Scholar | Scopus | ORCID | ResearchGate | LinkedIn
Featured Publications
1. Fan, M., McClure, J., Han, Y., Li, Z., & Chen, C. (2018). Interaction between proppant compaction and single-/multiphase flows in a hydraulic fracture. SPE Journal, 23(4), 1290–1303. Cited by: 67
2. Wang, H., Dalton, L., Fan, M., Guo, R., McClure, J., Crandall, D., & Chen, C. (2022). Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM. Journal of Petroleum Science and Engineering, 215, 110596. Cited by: 61
3. Guo, R., Dalton, L. E., Fan, M., McClure, J., Zeng, L., Crandall, D., & Chen, C. (2020). The role of the spatial heterogeneity and correlation length of surface wettability on two-phase flow in a CO₂-water-rock system. Advances in Water Resources, 146, 103763. Cited by: 60
4. Fan, M., McClure, J., Han, Y., Ripepi, N., Westman, E., Gu, M., & Chen, C. (2019). Using an experiment/simulation-integrated approach to investigate fracture-conductivity evolution and non-Darcy flow in a proppant-supported hydraulic fracture. SPE Journal, 24(4), 1912–1928. Cited by: 57
5. Fan, M., Li, Z., Han, Y., Teng, Y., & Chen, C. (2021). Experimental and numerical investigations of the role of proppant embedment on fracture conductivity in narrow fractures (includes associated errata). SPE Journal, 26(1), 324–341. Cited by: 50