| 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, 18-21 March, Manama, Bahrain, 2019 | 89 | 2019 |
| Pore System Characterization and Petrophysical Rock Classification Using a Bimodal Gaussian Density Function C Xu, C Torres-Verdín Mathematical Geosciences 45 (6), 753-771, 2013 | 84 | 2013 |
| Formation of photovoltaic bright spatial soliton in photorefractive LiNbO3 crystal by a defocused laser beam induced by a background laser beam WL She, CC Xu, B Guo, WK Lee JOSA B 23 (10), 2121-2126, 2006 | 67 | 2006 |
| Rock Classification in Carbonate Reservoirs based on Static and Dynamic Petrophysical Properties Estimated from Conventional Well Logs C Xu, Z Heidari, C Torres-Verdin SPE Annual Technical Conference and Exhibition, 2012 | 47 | 2012 |
| Saturation-Height and Invasion Consistent Hydraulic Rock Typing Using Multi-well Conventional Logs C Xu, C Torres-Verdín SPWLA Annual Symposium, 2012 | 46 | 2012 |
| 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 |
| Quantifying fluid distribution and phase connectivity with a simple 3D cubic pore network model constrained by NMR and MICP data C Xu, C Torres-Verdín Computers & geosciences 61, 94-103, 2013 | 27 | 2013 |
| A Comparative Study of Three Supervised Machine-Learning Algorithms for Classifying Carbonate Vuggy Facies in the Kansas Arbuckle Formation T Deng, C Xu, D Jobe, R Xu SPWLA Petrophysics 60 (6), 838 - 853, 2019 | 24 | 2019 |
| Synthetic Sonic Log Generation With Machine Learning: A Contest Summary From Five Methods Y Yu, C Xu, et al. Petrophysics 62 (04), 393-406, 2021 | 23 | 2021 |
| Petrophysical rock classification in the Cotton Valley tight-gas sandstone reservoir with a clustering pore-system orthogonality matrix C Xu, C Torres-Verdín Interpretation 2 (1), T13-T23, 2014 | 21 | 2014 |
| Diagenetic Facies Classification in the Arbuckle Formation Using Deep Neural Networks JD T Deng, C Xu, X Lang Mathematical Geosciences, 2021 | 17 | 2021 |
| Deep-Learning-Based Vuggy Facies Identification from Borehole Images CX J Jiang, R Xu, J.C. Scott SPE Reservoir Evaluation & Engineering, 2020 | 17* | 2020 |
| Machine learning in petrophysics: Advantages and limitations C Xu, L Fu, T Lin, W Li, S Ma Artificial Intelligence in Geosciences 3, 157-161, 2022 | 16 | 2022 |
| Integration of NMR and Conventional Logs for Vuggy Facies Classification in the Arbuckle Formation: A Machine Learning Approach CX Rui Xu, Tianqi Deng, Jiajun Jiang, Dawn Jobe SPE Reservoir Evaluation & Engineering, 13, 2020 | 16* | 2020 |
| 3D printing for experiments in petrophysics, rock physics, and rock mechanics: a review L Kong, S Ishutov, F Hasiuk, C Xu SPE Reservoir Evaluation & Engineering 24 (04), 721-732, 2021 | 15 | 2021 |
| Scattering from PEC cylinders by normally incident plane wave P Tsuji, K Parrish, C Xu The university of Texas at Austin, Tech. Rep, 2010 | 13 | 2010 |
| 3D Printing of True Pore-Scale Berea Sandstone and Digital Rock Verification A Li, S Zhang, C Xu, X Zhao, X Zhang SPE Journal, 2021 | 10 | 2021 |
| Nuclear magnetic resonance (NMR) microscopic simulation based on random-walk: Theory and parameters analysis M Tan, J Xu, Y Zou, C Xu Journal of Central South University 21 (3), 1091-1097, 2014 | 10 | 2014 |
| Reservoir description with well-log-based and core-calibrated petrophysical rock classification C Xu | 10 | 2013 |
| Enhanced dispersion analysis of borehole array sonic measurements with amplitude and phase estimation method W Li, R Guo, G Tao, H Wang, C Torres-Verdín, J Ma, C Xu SEG Technical Program Expanded Abstracts 2012, 1-5, 2012 | 9 | 2012 |
Carlos Torres-VerdinUniversity of Texas at Austin, Cockrell School of Engineering在 austin.utexas.edu 的电子邮件经过验证
