Adewale Amosu
Assistant Professor of Subsurface Energy Systems in Geology
Categorized As
Energy, Carbon Sequestration, Geothermal Resources, and AI Applications in Geosciences and Engineering - Dr. Amosu’s research focuses on developing energy solutions through an integrated understanding of the subsurface. His work encompasses subsurface energy systems, conventional reservoirs, unconventional shale systems, geological carbon sequestration, geothermal resources, and related subsurface processes that can help meet growing energy demands while minimizing environmental impacts.
Research:
Dr. Amosu integrates exploration geophysics, rock physics, petrophysics, and reservoir characterization in his research. This multidisciplinary approach improves subsurface imaging, resource evaluation, and monitoring across spatial scales. He also develops and applies machine learning and data driven methods to accelerate geological interpretation and improve predictive models of subsurface systems. In addition, he is interested in integrating robotics, autonomous sensing, and intelligent decision making into geological and geophysical exploration.
Representative Papers:
Amosu, A., & Sun, Y. (2017). WheelerLab: An interactive program for sequence stratigraphic analysis of seismic sections, outcrops and well sections and the generation of chronostratigraphic sections and dynamic chronostratigraphic sections. SoftwareX, 6, 19–24. https://doi.org/10.1016/j.softx.2016.12.003
Amosu, A., Imsalem, M., & Sun, Y. (2021). Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale. Journal of Applied Geophysics, 188. https://doi.org/10.1016/j.jappgeo.2021.104311
Amosu, A., & Sun, Y. (2021). Identification of thermally mature total organic carbon-rich layers in shale formations using an effective machine-learning approach. Interpretation, 9(3). https://doi.org/10.1190/INT-2020-0184.1
Koray, A., Bui, D., Kubi, E. A., Ampomah, W., & Amosu, A. (2024). Machine learning based reservoir characterization and numerical modeling from integrated well log and core data. Geoenergy Science and Engineering, 243. https://doi.org/10.1016/j.geoen.2024.213296
McCormack, K. L., Bratton, T., Amosu, A., Huang, L., Li, D., Burghardt, J., & Ampomah, W. (2025). A comparative analysis of states of stress for analyzing fault slip potential. International Journal of Greenhouse Gas Control, 147, Article 104493.
Huang, L., Li, D., Gao, K., Amosu, A., El-Kaseeh, G., Bratton, T., Zheng, Y., et al. (2026). Seismic-scale fault characterization for geologic carbon storage using legacy data and machine learning. International Journal of Greenhouse Gas Control, 155.
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