| Hybrid derivative-free technique and effective machine learning surrogate for nonlinear constrained well placement and production optimization Y Nasir, W Yu, K Sepehrnoori Journal of Petroleum Science and Engineering 186, 106726, 2020 | 38 | 2020 |
| Deep reinforcement learning for generalizable field development optimization J He, M Tang, C Hu, S Tanaka, K Wang, XH Wen, Y Nasir SPE Journal 27 (01), 226-245, 2022 | 33 | 2022 |
| Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology Y Nasir, LJ Durlofsky Journal of Computational Physics 477, 111945, 2023 | 19 | 2023 |
| Deep reinforcement learning for constrained field development optimization in subsurface two-phase flow Y Nasir, J He, C Hu, S Tanaka, K Wang, XH Wen Frontiers in Applied Mathematics and Statistics 7, 689934, 2021 | 18 | 2021 |
| A two-stage optimization strategy for large-scale oil field development Y Nasir, O Volkov, LJ Durlofsky Optimization and Engineering 23, 361-395, 2022 | 17 | 2022 |
| Multi-asset closed-loop reservoir management using deep reinforcement learning Y Nasir, LJ Durlofsky Computational Geosciences 28 (1), 23-42, 2024 | 2 | 2024 |
| Practical Closed-Loop Reservoir Management Using Deep Reinforcement Learning Y Nasir, LJ Durlofsky SPE Journal, 1-14, 2023 | 2 | 2023 |
| Deep reinforcement learning for field development optimization Y Nasir arXiv preprint arXiv:2008.12627, 2020 | 2 | 2020 |
| Derivative-free techniques for optimal development of conventional and unconventional reservoirs Y Nasir | 1 | 2018 |
| Generalizable Field Development Optimization Using Deep Reinforcement Learning with Field Examples J He, Y Nasir, S Tanaka Machine Learning Applications in Subsurface Energy Resource Management, 291-310, 2022 | | 2022 |
Louis DurlofskyProfessor of Energy Science and Engineering, Stanford UniversityVerified email at stanford.edu
