Books:

1. D. Grana, T. Mukerji, and P. Doyen, 2021, Seismic reservoir modeling: Theory, Examples, and Algorithms, Wiley.

2. J. Dvorkin, M. Gutierrez, and D. Grana, 2014, Seismic reflections of rock properties, Cambridge University Press.

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Book chapters:

1. M. Liu, D. Grana, and P. Nivlet, 2021, Recurrent neural network for seismic reservoir characterizations, In: Data Analytics in Energy Resources Exploration, in press.

2. D. Grana, K. Mosegaard, and H. Omre, 2021, Bayesian inversion in geosciences, In: Encyclopedia of Mathematical Geoscience, in press.

3. D. Grana, and L. Azevedo, 2020, Subsurface geostatistical modeling, In: Encyclopedia of Geology, in press.

4. D. Grana, 2016, Rock physics modeling in conventional reservoirs, In: New Frontiers in Oil and Gas Exploration, Springer, 137-163.

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Peer-review journals:

1. S. Anyosa, J. Eidsvik, and D. Grana, 2024, Evaluating geophysical monitoring strategies for a CO2 storage project, Computers & Geosciences, 105561. 

2. B. Flinchum, D. Grana, B. Carr, N. Ravichandran, B. Eppinger, and W.S. Holbrook, 2024, Low Vp/Vs ratios as an indicator for fractures in the critical zone, Geophysical Research Letters, 51(2), e2023GL105946. [PDF]

3. R. Feng, K. Mosegaard, D. Grana, and T. Mukerji, 2024, Estimation of Reservoir Fracture Properties from Seismic Data Using Markov Chain Monte Carlo Methods, Mathematical Geosciences, 1-24. 

4. P. Li+, M. Liu, M. Alfarraj, P. Tahmasebi, and D. Grana, 2024, Probabilistic physics informed neural network P-PINN for seismic petrophysical inversion, Geophysics, 89(2), M17-M32. [PDF]

5. N. Ahmed+, W. Weibull, and D. Grana, 2024, Constrained non-linear AVO inversion for dynamic reservoir changes estimation from time-lapse seismic data, Geophysics, 89(1), R1-R15.

6. A. Li+, D. Grana, A. Parsekian, and B. Carr, 2023, Uncertainty quantification in tomographic inversion of near-surface seismic refraction data, Mathematical Geosciences, 56, 76-101. [PDF]

7. R. Feng, K. Mosegaard, D. Grana, T. Mukerji, and T. Hansen, 2023, Stochastic facies inversion with prior sampling by conditional generative adversarial networks based on training image, Mathematical Geosciences, 1-26.

8. T. Alyousuf, Y. Li, R. Krahenbuhl, and D. Grana, 2023, Three-axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator, Geophysical Prospecting, 1-24.

9. 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, 88(6), E159-E171

10. Q. Guo, C. Luo, and D. Grana, 2023, Bayesian linearized rock-physics AVO inversion for petrophysical and pore-geometry parameters in carbonate reservoirs, Geophysics, 88(5), MR273-MR287.

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

12. M. Liu, Divakar 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.

13. 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. [PDF]

14. R. Miele+, L. Azevedo, D. Grana, L. Varella, and B. Barreto, 2022, Iterative geostatistical seismic inversion with rock physics constraints for permeability prediction, Geophysics, 88(2), M105-M117. [PDF]

15. 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.

16. R. Callahan, C. Riebe, L. Sklar, S. Pasquet, Ken. Ferrier, J. Hahm, N. Taylor, D. Grana, B. Flinchum, J. Hayes, and S. Holbrook, 2022, Forest vulnerability to drought controlled by bedrock composition, Nature Geoscience, 15, 714–719.

17. 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. [PDF]

18. 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. [PDF]

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

20. D. Grana, A. Parsekian, B. Flinchum, N. Smeltz, R. Callahan, A. Li, J. Hayes, B. Carr, K. Singha, C. Riebe, S. Holbrook, 2022, Geostatistical rock physics inversion for predicting the spatial distribution of porosity and saturation in the critical zone, Mathematical Geosciences, 1-31. [PDF}

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

22. R. Feng, K. Mosegaard, D. Grana, and T. Mukerji, 2022, Application of Bayesian generative adversarial networks to geological facies modeling, Mathematical Geosciences, 54 (518).  [PDF]

23. 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.

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

25. 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.

26. 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.

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

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

29. 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.

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

31. M. Sengupta, H. Zhang, Y. Zhao, M. Jervis, and D. Grana, 2021, Direct depth domain Bayesian AVO inversion, Geophysics, 86 (5), M167-M176.

32. 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.

33. R. Feng, N. Balling, and D. Grana, 2021, Imputation of missing well log data by random forest and uncertainty analysis, Computers & Geosciences, 152, 104763. 

34. 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.

35. 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. [PDF]

36. 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.

37. A.D. Parsekian, D. Grana, F. Neves, M. S. Pleasants, N Y. Smeltz, and T. Kelleners, 2021, Hydro-geophysical comparison of hillslope critical zone architecture for different geologic substrates, Geophysics, 86 (3), WB29-WB49. 

38. 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, 59 (10), 8933-8940. 

39. R. Feng, D. Grana, and N. Balling, 2020, Variational inference in Bayesian neural network for well log prediction, Geophysics, 86 (3), M91-M99. 

40. R. Feng, D. Grana, and N. Balling, 2020, Uncertainty quantification in fault detection using convolutional neural networks, Geophysics, 86 (3), M41-M48.

41. 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.

42. 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, 59 (11), 9104-9119. [PDF]

43. 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. [PDF]

44. D.R. Rosa+, J.M. Santos, R.M. Souza, D. Grana, D.J. Schiozer, A. Davolio, and Y. Wang, 2020. Comparing different approaches of time-lapse seismic inversion. Journal of Geophysics and Engineering, 17 (6), 929-939.

45. R. Callahan+, C. Riebe, S. Pasquet, K. Ferrier, D. Grana, L. Sklar, N. Taylor, B. Flinchum, J. Hayes, B. Carr, P. Hartsough. A. Green, and S. Holbrook, 2020, Subsurface weathering revealed in hillslope-integrated porosity distributions, Geophysical Research Letters, 47 (15).

46. 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.

47. 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.

48. L. Azevedo, D. Grana, and L. de Figueiredo+, 2020, Stochastic Perturbation Optimization for discrete-continuous inverse problems, Geophysics, 85 (5), M73-M83.

49. 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. 

50. 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.

51. D. Grana, 2020, Bayesian petroelastic inversion with multiple prior models, Geophysics, 85 (5), 57–M71. [PDF]

52. E. Talarico, L. de Figueiredo, and D. Grana, 2020, Uncertainty quantification for seismic facies classification, Geophysics, 85 (4), M43–M56. 

53. N. Claes, G.B. Paige, D. Grana, and A.D. Parsekian, 2020, Parameterization of a hydrologic model with geophysical data to simulate observed subsurface return flow paths: Vadose Zone Journal, 19 (1).

54. 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. [PDF]

55. H. Yu, K. Ng, E. Campbell, V. Alvarado, D. Grana, and J. Kaszuba, 2020, A generalized power-law criterion for rocks based on Mohr failure theory, International Journal of Rock Mechanics and Mining Sciences, 128, 104274.

56. V. H. Le, A. M. Diaz-Viera, D. Vázquez-Ramírez, R. del Valle-García, A. Erdely, and D. Grana, 2020, Bernstein copula-based spatial cosimulation for petrophysical property prediction conditioned to elastic attributes, Journal of Petroleum Science and Engineering, 193, 107382. 

57. L. de Figueiredo, D. Grana, and M. Le Ravalec, 2019, Revisited formulation of FFT-moving average, Mathematical Geosciences, 52, 801–816. [PDF]

58. 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. [PDF]

59. 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. 

60. M. Liu+, and D. Grana, 2019, Time-lapse seismic history matching with iterative ensemble smoother and deep convolutional autoencoder, Geophysics, 85 (1), M15-M31.

61. 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. [PDF]

62. M. Liu+, and D. Grana, 2019, Accelerating geostatistical seismic inversion using TensorFlow: A heterogeneous distributed deep learning framework, Computers & Geosciences, 124, 37-35.

63. 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. [PDF]

64. 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.

65. H. Pan, H. Li, D. Grana, Y. Zhang, T. Liu, and C. Cheng, 2019, Quantitative characterization of gas hydrate bearing sediment using elastic-electrical rock physics models, Marine and Petroleum Geology, 105, 173-183.

66. H. Yu, K. Ng, D. Grana, J. Kaszuba V. Alvarado and E. Campbell, 2019, Experimental investigation of the effect of compliant pores on reservoir rocks under hydrostatic and triaxial compression stress states, Canadian Geotechnical Journal, 56 (7), 983-991.

67. L. de Figueiredo+, D. Grana, M. Roisenberg, and B. Rodrigues, 2019, Gaussian Mixture McMC method for linear seismic inversion, Geophysics, 49 (4), 493-515.

68. L. Azevedo, D. Grana, and C. Amaro, 2019, Geostatistical rock physics AVA inversion, Geophysical Journal International, 216 (3), 1728–1739.

69. B. Flinchum+, S. Holbrook, D. Grana, A. Parsekian, B. Carr, J. Hayes, and J. Jiao, 2018, Estimating the water holding capacity of the critical zone using near‐surface geophysics, Hydrological Processes, 32 (22), 3308-3326.

70. H. Wang+, V. Alvarado, J. McLaughlin, D. Bagdonas, J. Kaszuba, E. Campbell, and D. Grana, 2018, Low‐field nuclear magnetic resonance characterization of carbonate and sandstone reservoirs from Rock Spring Uplift of Wyoming, Journal of Geophysical Research: Solid Earth, 123.

71. L. de Figueiredo+, D. Grana, F. Bordignon, M. Santos, M. Roisenberg, and B. Rodrigues, 2018, Joint Bayesian inversion based on rock-physics prior modeling for the estimation of spatially correlated reservoir properties, Geophysics, 83 (5), M49-M61.

72. X. Lang+, and D. Grana, 2018, Bayesian linearized petrophysical AVO inversion, Geophysics, 83 (3), M1-M14. [PDF]

73. D. Grana, 2018, Joint facies and reservoir properties inversion, Geophysics, 83 (3), M15-M24.

74. M. Liu+, and D. Grana, 2018, Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data re-parameterization, Geophysics, 83 (3), M25-M39.

75. T. Fjeldstad+, and D. Grana, 2018, Joint probabilistic petrophysics-seismic inversion based on Gaussian mixture and Markov chain prior models, Geophysics, 83 (1), R31-R42.

76. W. Wu+, and D. Grana, 2017, Integrated petrophysics and rock physics modeling for well log interpretation of elastic, electric, and petrophysical properties, Journal of Applied Geophysics, 146, 54-66.

77. D. Grana, T. Fjeldstad+, and H. Omre, 2017, Bayesian Gaussian mixture linear inversion for geophysical inverse problems, Mathematical Geosciences, 49 (4), 493–515.

78. D. Grana, S. Verma, J. Pafeng+, X. Lang+, H. Sharma+, W. Wu+, F. McLaughlin, E. Campbell, K. Ng, V. Alvarado, S. Mallick, and J. Kaszuba, 2017, A rock physics and seismic reservoir characterization study of the Rock Springs Uplift, a CO2 sequestration site in Southwestern Wyoming, International Journal of Greenhouse Gas Control, 63, 296-309.

79. L. de Figueiredo+, D. Grana, M. Santos, W. Figueiredo, M. Roisenberg, and G.S. Neto, 2017, Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies, Journal of Computational Physics, 336, 128-142. 

80. X. Lang+, and D. Grana, 2017, Geostatistical inversion of prestack seismic data for the joint estimation of facies and impedances using stochastic sampling from Gaussian mixture posterior distributions, Geophysics, 82 (4), M55-M65. [PDF]

81. M. Koneshloo, S. Aryana, D. Grana, and J. Pierre, 2017, A workflow for static reservoir modeling guided by seismic data in a fluvial system, Mathematical Geosciences, 49, 995–1020.

82. D. Grana, X. Lang+, and W. Wu+, 2017, Statistical facies classification from multiple seismic attributes: comparison between Bayesian classification and Expectation-Maximization method and application in petrophysical inversion, Geophysical Prospecting, 65 (2), 544-562.

83. D. Grana, 2016, Bayesian linearized rock-physics inversion, Geophysics, 81 (6), D625-D641. [PDF]

84. D. Grana, 2016, Pressure–velocity relations in reservoir rocks: Modified MacBeth's equation, Journal of Applied Geophysics, 132, 234-241.

85. K. Schlanser+, D. Grana, and E. Campbell-Stone, 2016, Lithofacies classification in the Marcellus Shale by applying a statistical clustering algorithm to petrophysical and elastic well logs inversion, Interpretation, 4 (2), SE31-SE49.

86. D. Grana and M. Bronston, 2015, Probabilistic formulation of AVO modeling and AVO-attribute-based facies classification using well logs, Geophysics, 80 (4), D343-D354. [PDF]

87. D. V. Lindberg+, and D. Grana, 2015, Petro-elastic log-facies classification using the Expectation–Maximization algorithm and hidden Markov models, Mathematical Geosciences, 47 (6), 719-752.

88. D. Grana and T. Mukerji, 2015, Bayesian inversion of time-lapse seismic data for the estimation of static reservoir properties and dynamic property changes, Geophysical Prospecting, 63 (3), 637- 655.

89. D. Grana, 2014, Probabilistic approach to rock physics modeling, Geophysics, 79 (4), D123-D143. [PDF]

90. D. Grana, K. Schlanser+, and E. Campbell-Stone, 2014, Petro-elastic and geomechanical classification of lithologic facies in the Marcellus shale, Interpretation, 3 (1), SA51-SA63.

91. D. Grana, E. Paparozzi, S. Mancini and C. Tarchiani, 2013, Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea, Geophysical Prospecting, 61 (3), 613-629.

92. D. Grana, T. Mukerji, J. Dvorkin, and G. Mavko, 2012, Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method, Geophysics, 77 (4), M53-M72. [PDF]

93. D. Grana, T. Mukerji, L. Dovera, and E. Della Rossa, 2012, Sequential Simulations of Mixed Discrete-Continuous Properties: Sequential Gaussian mixture Simulation, Geostatistics Oslo 2012, Quantitative Geology and Geostatistics, Volume 17, 239-250.

94. D. Grana, M. Pirrone, and T. Mukerji, 2012, Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock physics modeling and formation evaluation analysis, Geophysics, 77 (3), WA45–WA63. [PDF]

95. D. Grana, J. Dvorkin, and P. Cibin, 2011, Factor analysis prediction of effective stress from measurable rock attributes and calibration data, First Break, 29 (7), 63-72.

96. D. Grana, and E. Della Rossa, 2010, Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion, Geophysics, 75 (3), O21-O37. [PDF]