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Bayesian Learning Consortium

BLC Publications



Seismic Reservoir Modeling book cover
  • (Book) D. Grana, T. Mukerji, and P. Doyen, 2021, Seismic reservoir modeling, Wiley.
  • D. Grana, 2021, Probabilistic rock physics model using Kumaraswamy distributions, Geophysics, accepted for publication.
  • M. Conjard+, D. Grana, and H. Omre, 2021, Ensemble-based seismic and production data assimilation using selection Kalman model,Mathematical Geosciences, accepted for publication.
  • H. Wang, V. Alvarado, D. Bagdonas, F. McLaughlin, J. Kaszuba, D. Grana, E. Campbell, and K. Ng, 2021, Effect of CO2-brine-rock reactions on pore architecture and permeability in dolostone: Implications for CO2 storage and EOR: International Journal of Greenhouse Gas Control, 107, 103283.
  • R. Feng, N. Balling, and D. Grana, 2021, Imputation of missing well log data by random forest and uncertainty analysis, Computers & Geosciences, 152, 104763.
  • M. Loe+, D. Grana, and H. Tjelmeland, 2021, Geophysics-based fluid-facies predictions using ensemble updating of binary state, Mathematical Geosciences, 53 (3), 325-347.
  • D. Grana, M. Liu+, and M. Ayani+, 2021, Prediction of CO2 saturation spatial distribution using geostatistical inversion of time-lapse geophysical data, IEEE Transactions on Geoscience and Remote Sensing, 59 (5), 3846-3856.
  • L. de Figueiredo, T. Schmitz, R. Lunelli, M. Roisenberg, D. Freitas, and D. Grana, 2021, Direct Multivariate Simulation - A stepwise conditional transformation for multivariate geostatistical simulation, Computers & Geosciences, 147, 104659.
  • R. Feng, D. Grana, N. Balling, and T.M. Hansen, 2021, Bayesian convolutional neural networks for seismic facies classification, IEEE Transactions on Geoscience and Remote Sensing, accepted for publication. 
  • R. Feng, D. Grana, and N. Balling, 2020, Variational inference in Bayesian neural network for well log prediction, Geophysics, 86 (3), M91-M99. 
  • R. Feng, D. Grana, and N. Balling, 2020, Uncertainty quantification in fault detection using convolutional neural networks, Geophysics, 86 (3), M41-M48.
  • E. Talarico, W. Leao, and D. Grana, 2020, Comparison of recursive neural network and Markov chain models in facies inversion, Mathematical Geosciences, 53 (3), 395-413.
  • O. Forberg+, and D.Grana, 2020, Bayesian inversion of time-lapse seismic AVO data for multimodal reservoir properties, IEEE Transactions on Geoscience and Remote Sensing, accepted for publication.
  • G. Ghon+, D. Grana, E.C. Rankey, G.T. Baechle, F. Bleibinhaus, X. Lang, L. de Figueiredo, and M.C Poppelreiter, 2020, Bayesian facies inversion on a partially dolomitized isolated carbonate platform. A case study from Central Luconia province, Malaysia. Geophysics, 86 (2), 1MA-W19.
  • M. Ayani+, and D. Grana, 2020, Statistical rock physics inversion of elastic and electrical properties for CO2 sequestration studies, Geophysical Journal International, 223 (1), 707-724.
  • M. Ayani+, M. Liu+, and D. Grana, 2020, Stochastic inversion method of time-lapse controlled source electromagnetic data for CO2 plume monitoring, International Journal of Greenhouse Gas Control, 100, 103098.
  • L. Azevedo, D. Grana, and L. de Figueiredo+, 2020, Stochastic Perturbation Optimization for discrete-continuous inverse problems, Geophysics, 85 (5), M73-M83.
  • R. Feng, T. Hansen, D. Grana, and N. Balling, 2020, An unsupervised deep-learning method for porosity estimation based on post-stack seismic data, Geophysics, 85 (6), M97–M105.
  • M. Liu+, and D. Grana, 2020, Petrophysical characterization of deep saline aquifers for CO2 storage using ensemble smoother and deep convolutional autoencoder, Advances in Water Resources, 142, 103634.
  • D. Grana, 2020, Bayesian petroelastic inversion with multiple prior models, Geophysics, 85 (5), 57–M71.
  • E. Talarico, L. de Figueiredo, and D. Grana, 2020, Uncertainty quantification for seismic facies classification, Geophysics, 85 (4), M43–M56. 
  • R. Feng, N. Balling, and D. Grana, 2020, Lithofacies classification of a geothermal reservoir in Denmark and its facies-dependent porosity estimation from seismic inversion, Geothermics, 87, 101854.
  • L. de Figueiredo, D. Grana, and M. Le Ravalec, 2019, Revisited formulation of FFT-moving average, Mathematical Geosciences, 52, 801–816.
  • D. Grana, L. Azevedo, and M. Liu+, 2019, A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data, Geophysics, 85 (4), WA41-WA52.
  • R. Lorenzen, T. Bhakta, D. Grana, X. Luo, R. Valestrand, and G. Nevdal, 2019, Simultaneous assimilation of production and seismic data: application to the Norne field, Computational Geosciences, 24, 907–920.
  • M. Liu+, and D. Grana, 2019, Time-lapse seismic history matching with iterative ensemble smoother and deep convolutional autoencoder, Geophysics, 85 (1), M15-M31.
  • D. Grana, L. de Figueiredo, and L. Azevedo, 2019, Uncertainty quantification in Bayesian inverse problems with model and data dimension reduction, Geophysics, 84 (6), M15-M24.
  • M. Liu+, and D. Grana, 2019, Accelerating geostatistical seismic inversion using TensorFlow: A heterogeneous distributed deep learning framework, Computers & Geosciences, 124, 37-35.
  • X. Lang+, and D. Grana, 2019, Rock physics modeling and inversion for saturation and pressure changes in time-lapse studies, Geophysical Prospecting, 67 (7), 1912-1928.
  • L. de Figueiredo+, D. Grana, M. Roisenberg, and B. Rodrigues, 2019, Multimodal McMC method for non-linear petrophysical seismic inversion, Geophysics, 84 (5), M1-M13.
  • L. de Figueiredo+, D. Grana, M. Roisenberg, and B. Rodrigues, 2019, Gaussian Mixture McMC method for linear seismic inversion, Geophysics, 49 (4), 493-515.
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