| Reservoir lithology classification based on seismic inversion results by hidden Markov models: Applying prior geological information R Feng, SM Luthi, D Gisolf, E Angerer Marine and Petroleum Geology 93, 218-229, 2018 | 22 | 2018 |
| Uncertainty quantification in fault detection using convolutional neural networks R Feng, D Grana, N Balling Geophysics 86 (3), M41-M48, 2021 | 21 | 2021 |
| An unsupervised deep-learning method for porosity estimation based on poststack seismic dataDeep learning for porosity estimation R Feng, TM Hansen, D Grana, N Balling Geophysics 85 (6), M97-M105, 2020 | 21 | 2020 |
| Reservoir lithology determination by hidden Markov random fields based on a Gaussian mixture model R Feng, SM Luthi, D Gisolf, E Angerer IEEE Transactions on Geoscience and Remote Sensing 56 (11), 6663-6673, 2018 | 20 | 2018 |
| Lithofacies classification of a geothermal reservoir in Denmark and its facies-dependent porosity estimation from seismic inversion R Feng, N Balling, D Grana Geothermics 87, 101854, 2020 | 19 | 2020 |
| Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm R Feng Journal of Petroleum Science and Engineering 196, 107995, 2021 | 17 | 2021 |
| Imputation of missing well log data by random forest and its uncertainty analysis R Feng, D Grana, N Balling Computers & Geosciences 152, 104763, 2021 | 15 | 2021 |
| Obtaining a high-resolution geological and petrophysical model from the results of reservoir-orientated elastic wave-equation-based seismic inversion R Feng, SM Luthi, D Gisolf, S Sharma Petroleum Geoscience 23 (3), 376-385, 2017 | 15 | 2017 |
| Lithofacies classification based on a hybrid system of artificial neural networks and hidden Markov models R Feng Geophysical Journal International 221 (3), 1484-1498, 2020 | 11 | 2020 |
| Estimation of reservoir porosity based on seismic inversion results using deep learning methods R Feng Journal of Natural Gas Science and Engineering 77, 103270, 2020 | 11 | 2020 |
| Uncertainty analysis in well log classification by Bayesian long short-term memory networks R Feng Journal of Petroleum Science and Engineering 205, 108816, 2021 | 9 | 2021 |
| Variational inference in Bayesian neural network for well-log prediction R Feng, D Grana, N Balling Geophysics 86 (3), M91-M99, 2021 | 8 | 2021 |
| Bayesian convolutional neural networks for seismic facies classification R Feng, N Balling, D Grana, JS Dramsch, TM Hansen IEEE Transactions on Geoscience and Remote Sensing 59 (10), 8933-8940, 2021 | 8 | 2021 |
| Unsupervised learning elastic rock properties from pre-stack seismic data R Feng Journal of Petroleum Science and Engineering 192, 107237, 2020 | 7 | 2020 |
| An outcrop-based detailed geological model to test automated interpretation of seismic inversion results R Feng, S Sharma, SM Luthi, A Gisolf 77th EAGE Conference and Exhibition 2015 2015 (1), cp-451-00628, 2015 | 6 | 2015 |
| A Bayesian approach in machine learning for lithofacies classification and its uncertainty analysis R Feng IEEE Geoscience and Remote Sensing Letters 18 (1), 18-22, 2020 | 5 | 2020 |
| Interpretations of gravity and magnetic anomalies in the Songliao Basin with Wavelet Multi-scale Decomposition C Li, L Wang, B Sun, R Feng, Y Wu Frontiers of Earth Science 9 (3), 427-436, 2015 | 5 | 2015 |
| Simulating reservoir lithologies by an actively conditioned Markov chain model R Feng, SM Luthi, D Gisolf Journal of Geophysics and Engineering 15 (3), 800-815, 2018 | 4 | 2018 |
| Non-linear full-waveform inversion (FWI-res) of time-lapse seismic data on a higher-resolution geological and petrophysical model, Book Cliffs (Utah, USA) R Feng, SM Luthi, D Gisolf, S Sharma 2015 SEG Annual Meeting, 2015 | 4 | 2015 |
| Characterization of a Geothermal Reservoir in Denmark based on Seismic Inversion Results R Feng, N Balling, D Grana European Geothermal Workshop 2019, 2019 | 3 | 2019 |
Stefan M. LuthiProfessor Emeritus of Geology, Delft University of TechnologyVerified email at tudelft.nl
