| Application of an adaptive neuro-fuzzy inference system and mathematical rate of penetration models to predicting drilling rate H Yavari, M Sabah, R Khosravanian, D Wood Iranian Journal of Oil and Gas Science and Technology 7 (3), 73-100, 2018 | 38 | 2018 |
| Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices H Yavari, R Khosravanian, DA Wood, BS Aadnoy Advances in Geo-Energy Research 5 (4), 386-406, 2021 | 17 | 2021 |
| Implementation of Amorphous Mesoporous Silica Nanoparticles to formulate a novel water-based drilling fluid V Zarei, H Yavari, A Nasiri, M Mirzaasadi, A Davarpanah Arabian Journal of Chemistry 16 (8), 104818, 2023 | 5 | 2023 |
| An approach for optimization of controllable drilling parameters for motorized bottom hole assembly in a specific formation H Yavari, M Fazaelizadeh, BS Aadnoy, R Khosravanian, J Qajar, ... Results in Engineering 20, 101548, 2023 | 4 | 2023 |
| Selection of optimal well trajectory using multi-objective genetic algorithm and TOPSIS method H Yavari, J Qajar, BS Aadnoy, R Khosravanian Arabian Journal for Science and Engineering 48 (12), 16831-16855, 2023 | 2 | 2023 |
| Solution gas-oil ratio estimation using histogram gradient boosting regression, machine learning, and mathematical models: a comparative analysis H Yavari Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 46 …, 2024 | | 2024 |
Rasool KhosravanianDepartment of Energy and Petroleum Engineering University of Stavanger, Stavanger, Norway在 uis.no 的电子邮件经过验证
