Fetching the paper…
Reading the bibliography…
A deep-learning-based surrogate model is developed and applied for predicting dynamic subsurface flow in channelized geological models.
1906
Earlier work this paper cites.
D. W. Peaceman, Interpretation of well-block pressures in numerical reservoir simulation with nonsquare grid blocks and anisotropic permeability, SPE Journal 23 (03) (1983) 531–543
1983
Earlier work this paper cites.
R. Hecht-Nielsen, Theory of the backpropagation neural network, in: Neural Networks for Perception, Elsevier, 65–93, 1992
1992
Earlier work this paper cites.
Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks 5 (2) (1994) 157–166
1994
Earlier work this paper cites.
P. K. Kitanidis, Quasi-linear geostatistical theory for inversing, Water Resources Research 31 (10) (1995) 2411–2419
1995
Earlier work this paper cites.
D. S. Oliver, Multiple realizations of the permeability field from well test data, SPE Journal 1 (02) (1996) 145–154
1996
Earlier work this paper cites.
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8) (1997) 1735–1780
1997
Earlier work this paper cites.
G. Gao, M. Zafari, A. C. Reynolds, Quantifying uncertainty for the PUNQ-S3 problem in a Bayesian setting with RML and EnKF, in: SPE Reservoir Simulation Symposium, 2005
2005
Earlier work this paper cites.
J. F. Van Doren, R. Markovinović, J.-D. Jansen, Reduced-order optimal control of water flooding using proper orthogonal decomposition, Computational Geosciences 10 (1) (2006) 137–158
2006
Earlier work this paper cites.
C. Audet, J. E. Dennis Jr, Mesh adaptive direct search algorithms for constrained optimization, SIAM Journal on Optimization 17 (1) (2006) 188–217
2006
Earlier work this paper cites.
M. A. Cardoso, L. J. Durlofsky, P. Sarma, Development and application of reduced-order modeling procedures for subsurface flow simulation, International Journal for Numerical Methods in Engineering 77 (9) (2009) 1322–1350
2009
Earlier work this paper cites.
N. Remy, A. Boucher, J. Wu, Applied geostatistics with SGeMS: a user’s guide, Cambridge University Press, 2009
2009
Earlier work this paper cites.
T. Mikolov, M. Karafiát, L. Burget, J. Černockỳ, S. Khudanpur, Recurrent neural network based language model, in: Eleventh Annual Conference of the International Speech Communication Association, 2010
2010
Earlier work this paper cites.
Y. Zhou, Parallel general-purpose reservoir simulation with coupled reservoir models and multisegment wells, Ph.D. thesis, Stanford University, 2012
2012
Cited alongside, same era.
J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization, Journal of Machine Learning Research 13 (Feb) (2012) 281–305
2012
Cited alongside, same era.
J. He, P. Sarma, L. J. Durlofsky, Reduced-order flow modeling and geological parameterization for ensemble-based data assimilation, Computers & Geosciences 55 (2013) 54–69
2013
Cited alongside, same era.
T. Baltrusaitis, P. Robinson, L.-P. Morency, Constrained local neural fields for robust facial landmark detection in the wild, in: Proceedings of the IEEE International Conference on Computer Vision Workshops, 354–361, 2013
2013
Cited alongside, same era.
J. He, L. J. Durlofsky, Reduced-order modeling for compositional simulation by use of trajectory piecewise linearization, SPE Journal 19 (05) (2014) 858–872
H. X. Vo, L. J. Durlofsky, Data assimilation and uncertainty assessment for complex geological models using a new PCA-based parameterization, Computational Geosciences 19 (4) (2015) 747–767
2015
Later among the works it cites.
Y. Yang, M. Ghasemi, E. Gildin, Y. Efendiev, V. Calo, Fast multiscale reservoir simulations with POD-DEIM model reduction, SPE Journal 21 (06) (2016) 2–141
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778, 2016
2016
Later among the works it cites.
R. P. Poudel, P. Lamata, G. Montana, Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation, in: Reconstruction, Segmentation, and Analysis of Medical Images, Springer, 83–94, 2016
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
L. A. N. Costa, C. Maschio, D. J. Schiozer, Application of artificial neural networks in a history matching process, Journal of Petroleum Science and Engineering 123 (2014) 30–45
2014
Cited alongside, same era.
M. D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in: European Conference on Computer Vision, Springer, 818–833, 2014
2014
Cited alongside, same era.
H. Bazargan, M. Christie, A. H. Elsheikh, M. Ahmadi, Surrogate accelerated sampling of reservoir models with complex structures using sparse polynomial chaos expansion, Advances in Water Resources 86 (2015) 385–399
2015
Cited alongside, same era.
F. Liu, C. Shen, G. Lin, Deep convolutional neural fields for depth estimation from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5162–5170, 2015
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, 234–241, 2015
2015
Cited alongside, same era.
S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, W.-C. Woo, Convolutional LSTM network: A machine learning approach for precipitation nowcasting, in: Advances in Neural Information Processing Systems, 802–810, 2015
2015
Cited alongside, same era.
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431–3440, 2015
2015
Cited alongside, same era.
H. Hamdi, I. Couckuyt, M. C. Sousa, T. Dhaene, Gaussian processes for history-matching: application to an unconventional gas reservoir, Computational Geosciences 21 (2) (2017) 267–287
2017
Later among the works it cites.
P. Isola, J.-Y. Zhu, T. Zhou, A. A. Efros, Image-to-image translation with conditional adversarial networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1125–1134, 2017
2017
Later among the works it cites.
G. Zhu, L. Zhang, P. Shen, J. Song, Multimodal gesture recognition using 3-D convolution and convolutional LSTM, IEEE Access 5 (2017) 4517–4524
2017
Later among the works it cites.
Z. L. Jin, L. J. Durlofsky, Reduced-order modeling of CO 2 \text{CO}_{2} storage operations, International Journal of Greenhouse Gas Control 68 (2018) 49–67
2018
Later among the works it cites.
Y. Zhu, N. Zabaras, Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification, Journal of Computational Physics 366 (2018) 415–447
2018
Later among the works it cites.
C. Xiao, O. Leeuwenburgh, H. X. Lin, A. Heemink, Non-intrusive subdomain POD-TPWL for reservoir history matching, Computational Geosciences 23 (03) (2019) 537–565
2019
Closest in time.
Y. Liu, W. Sun, L. J. Durlofsky, A deep-learning-based geological parameterization for history matching complex models, Mathematical Geosciences 51 (6) (2019) 725–766
2019
Closest in time.
Y. Liu, L. J. Durlofsky, Multilevel strategies and geological parameterizations for history matching complex reservoir models, in: SPE Reservoir Simulation Conference, 2019
2019
Closest in time.