| Noninvasive fracture characterization based on the classification of sonic wave travel times S Misra, H Li, J He Machine learning for subsurface characterization 4, 243-287, 2020 | 164 | 2020 |
| Machine learning for subsurface characterization S Misra, H Li, J He Gulf Professional Publishing, 2019 | 116 | 2019 |
| Interfacial polarization of disseminated conductive minerals in absence of redox-active species—Part 1: Mechanistic model and validation S Misra, C Torres-Verdín, A Revil, J Rasmus, D Homan Geophysics 81 (2), E139-E157, 2016 | 92 | 2016 |
| When petrophysics meets big data: What can machine do? C Xu, S Misra, P Srinivasan, S Ma SPE Middle East Oil and Gas Show and Conference, D041S038R002, 2019 | 89 | 2019 |
| Machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking S Misra, Y Wu Machine learning for subsurface characterization 289 (3), 4, 2019 | 89 | 2019 |
| Relative permeability estimates for Wolfcamp and Eagle Ford shale samples from oil, gas and condensate windows using adsorption-desorption measurements SP Ojha, S Misra, A Tinni, C Sondergeld, C Rai Fuel 208, 52-64, 2017 | 79 | 2017 |
| Prediction of subsurface NMR T2 distributions in a shale petroleum system using variational autoencoder-based neural networks H Li, S Misra IEEE Geoscience and Remote Sensing Letters 14 (12), 2395-2397, 2017 | 78 | 2017 |
| Machine learning for locating organic matter and pores in scanning electron microscopy images of organic-rich shales Y Wu, S Misra, C Sondergeld, M Curtis, J Jernigen Fuel 253, 662-676, 2019 | 58 | 2019 |
| Interfacial polarization of disseminated conductive minerals in absence of redox-active species—Part 2: Effective electrical conductivity and dielectric permittivity S Misra, C Torres-Verdín, A Revil, J Rasmus, D Homan Geophysics 81 (2), E159-E176, 2016 | 57 | 2016 |
| Long short-term memory and variational autoencoder with convolutional neural networks for generating NMR T2 distributions H Li, S Misra IEEE Geoscience and Remote Sensing Letters 16 (2), 192-195, 2018 | 56 | 2018 |
| Pore connectivity and pore size distribution estimates for Wolfcamp and Eagle Ford shale samples from oil, gas and condensate windows using adsorption-desorption measurements SP Ojha, S Misra, A Tinni, C Sondergeld, C Rai Journal of Petroleum Science and Engineering 158, 454-468, 2017 | 49 | 2017 |
| Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints H Li, S Misra, J He Neural Computing and Applications 32, 3873-3885, 2020 | 46 | 2020 |
| Intelligent image segmentation for organic-rich shales using random forest, wavelet transform, and hessian matrix Y Wu, S Misra IEEE Geoscience and Remote Sensing Letters 17 (7), 1144-1147, 2019 | 40 | 2019 |
| Data-driven in-situ geomechanical characterization in shale reservoirs H Li, J He, S Misra SPE Annual Technical Conference and Exhibition?, D032S065R002, 2018 | 36 | 2018 |
| Machine learning workflow to predict multi-target subsurface signals for the exploration of hydrocarbon and water O Osogba, S Misra, C Xu Fuel 278, 118357, 2020 | 34 | 2020 |
| Reinforcement learning based automated history matching for improved hydrocarbon production forecast H Li, S Misra Applied Energy 284, 116311, 2021 | 32 | 2021 |
| Unsupervised outlier detection techniques for well logs and geophysical data S Misra, O Osogba, M Powers Mach. Learn. Subsurface Charact 1, 2019, 2019 | 31 | 2019 |
| Assessment of miscible light-hydrocarbon-injection recovery efficiency in Bakken shale formation using wireline-log-derived indices H Li, S Misra Marine and Petroleum Geology 89, 585-593, 2018 | 27 | 2018 |
| Prediction of subsurface NMR T2 distribution from formation-mineral composition using variational autoencoder H Li, S Misra SEG International Exposition and Annual Meeting, SEG-2017-17798488, 2017 | 27 | 2017 |
| Comparative study of shallow learning models for generating compressional and shear traveltime logs J He, S Misra, H Li Petrophysics 59 (06), 826-840, 2018 | 26 | 2018 |
Dean HomanAdvisor Research Scientist, Schlumberger在 slb.com 的电子邮件经过验证
