| Application of artificial intelligence to forecast hydrocarbon production from shales P Panja, R Velasco, M Pathak, M Deo Petroleum 4 (1), 75-89, 2018 | 120 | 2018 |
| Fluid flow distribution in fractures for a doublet system in Enhanced Geothermal Systems (EGS) P Asai, P Panja, R Velasco, J McLennan, J Moore Geothermics 75, 171-179, 2018 | 34 | 2018 |
| Least square support vector machine: an emerging tool for data analysis P Panja, M Pathak, R Velasco, M Deo SPE Rocky Mountain Petroleum Technology Conference/Low-Permeability …, 2016 | 28 | 2016 |
| Analysis of North‐American Tight Oil Production R Velasco, P Panja, M Pathak, M Deo AIChE Journal, 2017 | 18 | 2017 |
| In situ and laboratory studies of radiofrequency propagation through ice and implications for siting a large-scale Antarctic neutrino detector D Besson, R Keast, R Velasco Astroparticle Physics 31 (5), 348-358, 2009 | 17 | 2009 |
| What happens to permeability at the nanoscale? A molecular dynamics simulation study R Velasco, M Pathak, P Panja, M Deo Unconventional Resources Technology Conference, Austin, Texas, 24-26 July …, 2017 | 15 | 2017 |
| New production performance and prediction tool for unconventional reservoirs R Velasco, P Panja, M Deo SPE/AAPG/SEG Unconventional Resources Technology Conference, URTEC-2461718-MS, 2016 | 15 | 2016 |
| Understanding and modeling of gas-condensate flow in porous media P Panja, R Velasco, M Deo Advances in Geo-Energy Research 4 (2), 173-186, 2020 | 12 | 2020 |
| Suppression in the bubble points of oils in shales combined effect of presence of organic matter and confinement M Pathak, H Kweon, P Panja, R Velasco, MD Deo SPE Canada Unconventional Resources Conference?, D021S005R004, 2017 | 11 | 2017 |
| Experimental verification of changing bubble points of oils in shales: effect of preferential absorption by kerogen and confinement of fluids M Pathak, R Velasco, P Panja, MD Deo SPE Annual Technical Conference and Exhibition?, D021S027R008, 2017 | 9 | 2017 |
| Application of artificial intelligence to forecast hydrocarbon production from shales. Petroleum 4 (1): 75–89 P Panja, R Velasco, M Pathak, M Deo | 8 | 2018 |
| Flow of long chain hydrocarbons through carbon nanotubes (CNTs) P Asai, P Panja, R Velasco, M Deo Scientific Reports 11 (1), 11015, 2021 | 7 | 2021 |
| Application of artificial intelligence to forecast hydrocarbon production from Shales. Petroleum, 4, 75–89 P Panja, R Velasco, M Pathak, M Deo | 5 | 2018 |
| Moving boundary approach to forecast tight oil production R Velasco, P Panja, M Deo AIChE Journal 67 (2), e17012, 2021 | 4 | 2021 |
| Production Prediction of Hydraulically Fractured Reservoirs Based on Material Balances R Velasco, P Panja, M Deo | 2 | 2019 |
| Production of Liquid Hydrocarbons from Shales P Panja, R Velasco Encyclopedia of Petroleum Geoscience, 1-11, 2018 | 2 | 2018 |
| Advanced techniques for reservoir engineering and simulation R Guachalla The University of Utah, 2016 | 2 | 2016 |
| Simplification workflow for hydraulically fractured reservoirs R Velasco, P Panja, M Deo Petroleum 4 (2), 134-147, 2018 | 1 | 2018 |
| New discrete fracture networks (DFN) model with coupled geomechanics and fluid flow P Panja, R Velasco, P Asai, M Deo Unconventional Resources Technology Conference, 20–22 June 2022, 3039-3051, 2022 | | 2022 |
| Pre-processing Protocol for Nonlinear Regression of Uneven Spaced-Data P Panja, P Asai, R Velasco, M Deo Journal of Modeling and Optimization 12 (1), 23-37, 2020 | | 2020 |
Palash PanjaResearch Assistant Professor, University of Utah在 egi.utah.edu 的电子邮件经过验证
