My research sits at the intersection of deep learning and static geological modeling, with the goal of improving reserves estimation, well placement, and overall field performance optimization. I am passionate about applying machine learning and data-driven approaches to integrated static-dynamic reservoir characterization and geological modeling. My work draws on robust, cross-disciplinary expertise across all subsurface and engineering upstream disciplines, spanning the full exploration and development lifecycle.

• Machine learning models for permeability prediction using petrophysical well logs: a novel approach, S. Alatwah, International Petroleum Technology Conference, IPTC-24764-EA, (2025)
• Well-log-based reservoir property estimation with machine learning: a contest summary, L. Fu, Y. Yu, C. Xu, M. Ashby, A. McDonald, W. Pan, T. Deng, I. Szabó, P.P. Hanzelik, C. Kalmár, S. Alatwah, Petrophysics, 65, 108-127, (2024)
• Automatic fracture identifications from image logs with machine-learning approaches: a contest summary, H. Lee, R. Zamani, L. Fu, S. Alatwah, et al., Petrophysics, 66, 894-914, (2025)

• BSc., Geoscience, University of Tulsa, Tulsa OK , USA 2013
• MSc., Geo-Energy Engineering, TU Delft, Delft, Netherlands, 2023

• 2013 - Present. Computational Geoscientist at Saudi Aramco , Dhahran Saudi Arabia

• European Association of Geoscientists and Engineers (EAGE)
• Society of Petroleum Engineers (SPE)
• American Association of Petroleum Geologists (AAPG)
• Society of Exploration Geophysicists (SEG)

• Physical Science and Engineering Division (PSE)