Runhai Feng
Runhai Feng
Niels Bohr Institute, University of Copenhagen
Verified email at nbi.ku.dk - Homepage
Title
Cited by
Cited by
Year
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
202018
An unsupervised deep-learning method for porosity estimation based on poststack seismic data
R Feng, T Mejer Hansen, D Grana, N Balling
Geophysics 85 (6), M97-M105, 2020
182020
Uncertainty quantification in fault detection using convolutional neural networks
R Feng, D Grana, N Balling
Geophysics 86 (3), M41-M48, 2021
172021
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
172018
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
152017
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
112021
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
112020
Imputation of missing well log data by random forest and its uncertainty analysis
R Feng, D Grana, N Balling
Computers & Geosciences 152, 104763, 2021
102021
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
92020
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
92020
Variational inference in Bayesian neural network for well-log prediction
R Feng, D Grana, N Balling
Geophysics 86 (3), M91-M99, 2021
62021
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
62015
Unsupervised learning elastic rock properties from pre-stack seismic data
R Feng
Journal of Petroleum Science and Engineering 192, 107237, 2020
52020
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
52015
Uncertainty analysis in well log classification by Bayesian long short-term memory networks
R Feng
Journal of Petroleum Science and Engineering 205, 108816, 2021
42021
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
42020
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
42018
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
42015
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, 2021
32021
Characterization of a Geothermal Reservoir in Denmark based on Seismic Inversion Results
R Feng, N Balling, D Grana
European Geothermal Workshop 2019, 2019
32019
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