| A critical review of physics-informed machine learning applications in subsurface energy systems A Latrach, ML Malki, M Morales, M Mehana, M Rabiei Geoenergy Science and Engineering, 212938, 2024 | 9 | 2024 |
| Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning S Mao, B Chen, M Malki, F Chen, M Morales, Z Ma, M Mehana Applied Energy 361, 122914, 2024 | 7 | 2024 |
| Cushion gas effects on hydrogen storage in porous rocks: Insights from reservoir simulation and deep learning S Mao, B Chen, M Morales, M Malki, M Mehana International Journal of Hydrogen Energy 68, 1033-1047, 2024 | 1 | 2024 |
| Best Practices in Automatic Permeability Estimation: Machine-Learning Methods vs. Conventional Petrophysical Models O Raheem, W Pan, C Torres-Verdín, MM Morales SPWLA Annual Logging Symposium, D041S015R001, 2023 | 1 | 2023 |
| Assimilation of Geophysics-Derived Spatial Data for Model Calibration in Geologic CO2 Sequestration B Chen, MM Morales, Z Ma, Q Kang, RJ Pawar SPE Journal, 1-10, 2024 | | 2024 |
| Boundary element method for the Dirichlet problem for Laplace's equation on a disk MM Morales, S Pomeranz arXiv preprint arXiv:2401.11616, 2024 | | 2024 |
| Estimation of Hydrogen Storage Performance in Porous Rocks Integrating Deep Learning and Reservoir Simulation S Mao, B Chen, M Morales, ML Malki, M Ma, M Mehana AGU23, 2023 | | 2023 |
| Machine Learning Training Images Repository (1.0.0) MM Morales, M Pyrcz https://doi.org/10.5281/zenodo.7702128, 2023 | | 2023 |
Mohamed MehanaLos Alamos National Lab在 lanl.gov 的电子邮件经过验证
