BLC Publications

Publications


Seismic Reservoir Modeling book cover
  • M. Liu, J. Narciso, D. Grana, E. Van De Vijver, and L. Azevedo, 2023, Frequency-domain electromagnetic induction for the prediction of electrical conductivity and magnetic susceptibility using geostatistical inversion and randomized tensor decomposition, Geophysics, accepted for publication.

  • Q. Guo, C. Luo, and D. Grana, 2023, Bayesian linearized rock-physics AVO inversion for petrophysical and pore-geometry parameters in carbonate reservoirs, Geophysics, accepted for publication.

  • D. Grana, L. de Figueiredo, and K. Mosegaard, 2023, Markov chain Monte Carlo for seismic facies classification, Geophysics, 88(3), M131-M143.

  • M. Liu, D. Vashisth, D. Grana, and T. Mukerji, 2023, Joint inversion of geophysical data for geologic carbon sequestration monitoring: a differentiable physics-informed deep learning model, Journal of Geophysical Research: Solid Earth, 128(3), e2022JB025372.

  • Q. Hu, K. Innanen, and D. Grana, 2023, Feasibility of seismic time-lapse monitoring of CO2 with rock physics parameterized full waveform inversion, Geophysical International Journal, 233(1), 402-419.

  • R. Miele, L. Azevedo, D. Grana, L. Varella, and B. Barreto, 2023, Iterative geostatistical seismic inversion with rock physics constraints for permeability prediction, Geophysics, 88(2), M105-M117.

  • N. Ahmed, W. Weibull, and D. Grana, 2022, Frequency-dependent AVO inversion applied to physically based models for seismic attenuation, Geophysical International Journal, 233(1), 234-252.

  • D. Grana, B. Russell, and T. Mukerji, 2022, Petrophysical inversion based on f-s-r AVO linearization and canonical correlation analysis, Geophysics, 87 (6), 87: M247-M258.

  • D. Grana, L. Azevedo, L. de Figueiredo, P. Connolly, and T. Mukerji, 2022, Probabilistic inversion of seismic data for reservoir characterization: A review, Geophysics, 87 (5), M199-M216.

  • N. Ahmed, W. Weibull, and D. Grana, 2022, Constrained non-linear AVO Inversion based on the adjoint-state optimization, Computers & Geosciences, 168, 105214.

  • D. Grana, 2022, Bayesian rock physics inversion with Kumaraswamy prior models, Geophysics, 87 (3), M87-M97.

  • R. Feng, K. Mosegaard, D. Grana, and T. Mukerji, 2022, Application of Bayesian generative adversarial networks to geological facies modeling, Mathematical Geosciences, accepted for publication

  • M. Liu+, D. Grana, and T. Mukerji, 2022, Randomized tensor decomposition for large-scale data assimilation problems for carbon dioxide sequestration, Mathematical Geosciences, 1-25.

  • D. Grana, L. de Figueiredo, and K. Mosegaard, 2022, Markov chain Monte Carlo for petrophysical inversion, Geophysics, 87 (1), M13-M24.

  • M. Liu+, D. Grana, and L. de Figueiredo, 2022, Uncertainty quantification in stochastic inversion with model and data dimension reduction using variational autoencoder, Geophysics, 87 (2), M43-M58.

  • K. Li+, X. Ying, Z. Zong, and D. Grana, 2022, Estimation of porosity, fluid bulk modulus, and stiff-pore volume fraction using a multi-trace Bayesian AVO petrophysics inversion in multi-porosity reservoirs, Geophysics, 87 (1), M25-M41.

  • D. Grana, 2021, Multivariate probabilistic rock physics model using Kumaraswamy distributions, Geophysics, 86 (5), 86(5), MR261-MR270.

  • D. Grana, and L. de Figueiredo, 2021, SeReMpy: Seismic reservoir modeling python library, Geophysics, 86 (6), F61-F69.

  • F. Turco, L. Azevedo, D. Grana, A. Gorman, G. Crutchley, 2021, Characterization of gas hydrate systems of the Hikurangi margin (New Zealand) thought geostatistical seismic and petrophysical, Geophysics, 86 (6), R825-R838.

  • M. Conjard+, and D. Grana, 2021, Ensemble-based seismic and production data assimilation using selection Kalman model, Mathematical Geosciences, 53 (7), 1445-1468.

  • (Book) D. Grana, T. Mukerji, and P. Doyen, 2021, Seismic reservoir modeling, Wiley.

  • 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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