| Machine learning technique for the prediction of shear wave velocity using petrophysical logs M Anemangely, A Ramezanzadeh, H Amiri, SA Hoseinpour Journal of Petroleum Science and Engineering 174, 306-327, 2019 | 174 | 2019 |
| Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field SB Ashrafi, M Anemangely, M Sabah, MJ Ameri Journal of Petroleum Science and Engineering 175, 604-623, 2019 | 165 | 2019 |
| Drilling rate prediction from petrophysical logs and mud logging data using optimized Multi Layer Perceptron neural network M Anemangely, A Ramezanzadeh, B Tokhmechi, A Molaghab, ... Journal of Geophysics and Engineering, 2018 | 127 | 2018 |
| A machine learning approach to predict drilling rate using petrophysical and mud logging data M Sabah, M Talebkeikhah, DA Wood, R Khosravanian, M Anemangely, ... Earth Science Informatics 12, 319-339, 2019 | 123 | 2019 |
| Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield M Anemangely, A Ramezanzadeh, B Tokhmechi Journal of Natural Gas Science and Engineering 38, 373-387, 2017 | 111 | 2017 |
| A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning N Mohamadian, H Ghorbani, DA Wood, M Mehrad, S Davoodi, S Rashidi, ... Journal of Petroleum Science and Engineering 196, 107811, 2021 | 93 | 2021 |
| Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field M Sabah, M Mehrad, SB Ashrafi, DA Wood, S Fathi Journal of Petroleum Science and Engineering 198, 108125, 2021 | 71 | 2021 |
| Developing a new rigorous drilling rate prediction model using a machine learning technique M Mehrad, M Bajolvand, A Ramezanzadeh, JG Neycharan Journal of Petroleum Science and Engineering 192, 107338, 2020 | 70 | 2020 |
| Geomechanical parameter estimation from mechanical specific energy using artificial intelligence M Anemangely, A Ramezanzadeh, MM Behboud Journal of Petroleum Science and Engineering 175, 407-429, 2019 | 61 | 2019 |
| Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms M Rajabi, O Hazbeh, S Davoodi, DA Wood, PS Tehrani, H Ghorbani, ... Journal of Petroleum Exploration and Production Technology 13 (1), 19-42, 2023 | 57 | 2023 |
| Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields ARB Abad, H Ghorbani, N Mohamadian, S Davoodi, M Mehrad, ... Fuel 308, 121872, 2022 | 56 | 2022 |
| Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms S Rashidi, M Mehrad, H Ghorbani, DA Wood, N Mohamadian, ... Journal of Petroleum Science and Engineering 202, 108425, 2021 | 54 | 2021 |
| Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites S Davoodi, HV Thanh, DA Wood, M Mehrad, VS Rukavishnikov, Z Dai Expert Systems with Applications 222, 119796, 2023 | 52 | 2023 |
| Prediction of permeability from well logs using a new hybrid machine learning algorithm M Matinkia, R Hashami, M Mehrad, MR Hajsaeedi, A Velayati Petroleum 9 (1), 108-123, 2023 | 47 | 2023 |
| Optimal cut-off grade determination based on variable capacities in open-pit mining J Abdollahisharif, E Bakhtavar, M Anemangely Journal of the Southern African Institute of Mining and Metallurgy 112 (12 …, 2012 | 44 | 2012 |
| Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables S Davoodi, HV Thanh, DA Wood, M Mehrad, M Al-Shargabi, ... Separation and Purification Technology 316, 123807, 2023 | 41 | 2023 |
| Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms M Mehrad, A Ramezanzadeh, M Bajolvand, MR Hajsaeedi Journal of Petroleum Science and Engineering 212, 110254, 2022 | 36 | 2022 |
| Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes H Ghorbani, DA Wood, N Mohamadian, S Rashidi, S Davoodi, ... Flow Measurement and Instrumentation 76, 101849, 2020 | 36 | 2020 |
| Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids S Davoodi, M Mehrad, DA Wood, H Ghorbani, VS Rukavishnikov Engineering Applications of Artificial Intelligence 123, 106459, 2023 | 35 | 2023 |
| Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations S Davoodi, HV Thanh, DA Wood, M Mehrad, VS Rukavishnikov Applied Soft Computing 143, 110408, 2023 | 35 | 2023 |