InterPore2022

Morphology Decoder: Untangling Heterogeneous Porous Media Texture and Quantifying Permeability and Capillary Pressure by Semantic Segmentation

2 Jun 2022, 09:25

15m

Oral Presentation (MS15) Machine Learning and Big Data in Porous Media MS15

Speaker

Dr Omar Alfarisi (Khalifa University of Science and Technology)

Description

Determining porous media physical properties, like permeability and capillary pressure, in heterogeneous porous media, like carbonate rock, is one of the most challenging tasks for scientists in the digital rock physics domain. One of these challenges is the untangling of the heterogeneous texture. Another challenge is the image's resolution, which controls the visible details of the texture. The third challenge is the size of the representative sample, where the resolution and size form an inverse relationship. At the same time, the fourth challenge is the pore network simulation, which holds lots of assumptions with error accumulation. Finally, the fifth challenge is the high computation power and related high power consumption and long calculation time. Therefore, we propose a solution for untangling the heterogeneous texture: the morphology decoder of image resolution-independent, sample size-independent, simulation free, and machine learning-driven [1-86].

References

1. Nikolaidis, N. and I. Pitas, 3-D image processing algorithms. 2000: John Wiley & Sons, Inc.

2. Bakke, S. and P.-E. Øren, 3-D pore-scale modelling of sandstones and flow simulations in the pore networks. Spe Journal, 1997. 2(02): p. 136-149.

3. Chen, X., et al. 3D Permeability Characterization Based on Pore Structure Analysis and Multi-Parameters Seismic Inversion and Its Application in H Oilfield. in International Petroleum Technology Conference. 2019. International Petroleum Technology Conference.

4. Dong, H., et al., 3D pore-type digital rock modeling of natural gas hydrate for permafrost and numerical simulation of electrical properties. Journal of Geophysics and Engineering, 2018. 15(1): p. 275-285.

5. Chowdhury, M.R., et al., 3D printed polyamide membranes for desalination. Science, 2018. 361(6403): p. 682-686.

6. du Plessis, A., S.G. le Roux, and M. Tshibalanganda, Advancing X-ray micro computed tomography in Africa: going far, together. Scientific African, 2019: p. e00061.

7. García-Salaberri, P.A., et al., Analysis of representative elementary volume and through-plane regional characteristics of carbon-fiber papers: diffusivity, permeability and electrical/thermal conductivity. International Journal of Heat and Mass Transfer, 2018. 127: p. 687-703.

8. Varfolomeev, I., I. Yakimchuk, and I. Safonov, An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples. Computers, 2019. 8(4): p. 72.

9. Guntoro, P.I., et al., Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data. Minerals Engineering, 2019. 142: p. 105882.

10. Kim, K.-H., et al., Assessing whether the 2017 Mw 5.4 Pohang earthquake in South Korea was an induced event. Science, 2018. 360(6392): p. 1007-1009.

11. Chhatre, S.S., et al. A Blind Study of Four Digital Rock Physics Vendor Labs on Porosity, Absolute Permeability, and Primary Drainage Capillary Pressure Data on Tight Outcrop Rocks. in Oral presentation given at the Annual Symposium of the Society of Core Analysts, Vienna, Austria. 2017.

12. Noé, F., et al., Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. Science, 2019. 365(6457): p. eaaw1147.

13. Da Wang, Y., R.T. Armstrong, and P. Mostaghimi, Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning. arXiv preprint arXiv:1907.07131, 2019.

14. Parmigiani, A., et al., Bubble accumulation and its role in the evolution of magma reservoirs in the upper crust. Nature, 2016. 532(7600): p. 492.

15. Wolf, M.J., Building imaginary worlds: The theory and history of subcreation. 2014: Routledge.

16. Lucia, F.J., Carbonate Reservoir Characterization: An Integrated Approach. 2nd Edition ed. 2007: Springer.

17. Al-Farisi, O., et al. Carbonate Rock Type Matrix RocMat, The Ultimate Rock Properties Catalogue. in International Conference on Offshore Mechanics and Arctic Engineering. 2013. American Society of Mechanical Engineers.

18. BinAbadat, E., et al. Complex carbonate rock typing and saturation modeling with highly-coupled geological description and petrophysical properties. in SPE Reservoir Characterisation and Simulation Conference and Exhibition. 2019. OnePetro.

19. Balli, J., S. Kumpaty, and V. Anewenter. Continuous Liquid Interface Production of 3D Objects: An Unconventional Technology and Its Challenges and Opportunities. in ASME 2017 International Mechanical Engineering Congress and Exposition. 2017. American Society of Mechanical Engineers Digital Collection.

20. Swisher III, C.C., et al., Cretaceous age for the feathered dinosaurs of Liaoning, China. Nature, 1999. 400(6739): p. 58.

21. Christian, L., Cretaceous subsurface geology of the Middle East region. GeoArabia, 1997. 2(3): p. 239-256.

22. Caplan, J.S., et al., Decadal-scale shifts in soil hydraulic properties as induced by altered precipitation. Science advances, 2019. 5(9): p. eaau6635.

23. Alfarisi, O., Z. Aung, and M. Sassi, Deducing of Optimal Machine Learning Algorithms for Heterogeneity. arXiv preprint arXiv:2111.05558, 2021.

24. Goodfellow, I., Y. Bengio, and A. Courville, Deep learning. 2016: MIT press.

25. Alqahtani, N., R.T. Armstrong, and P. Mostaghimi. Deep learning convolutional neural networks to predict porous media properties. in SPE Asia Pacific oil and gas conference and exhibition. 2018. Society of Petroleum Engineers.

26. Armatas, G.S., Determination of the effects of the pore size distribution and pore connectivity distribution on the pore tortuosity and diffusive transport in model porous networks. Chemical Engineering Science, 2006. 61(14): p. 4662-4675.

27. Dillard, L.A. and M.J. Blunt, Development of a pore network simulation model to study nonaqueous phase liquid dissolution. Water Resources Research, 2000. 36(2): p. 439-454.

28. Dernaika, M., et al., Digital and Conventional Techniques to Study Permeability Heterogeneity in Complex Carbonate Rocks. Petrophysics, 2018. 59(03): p. 373-396.

29. Umbaugh, S.E., Digital image processing and analysis: human and computer vision applications with CVIPtools. 2010: CRC press.

30. Wu, Y., et al., Effects of micropores on geometric, topological and transport properties of pore systems for low-permeability porous media. Journal of Hydrology, 2019. 575: p. 327-342.

31. Al-Farisi, O., et al. Electrical Resistivity and Gamma-Ray Logs: Two Physics for Two Permeability Estimation Approaches in Abu Dhabi Carbonates. in Abu Dhabi International Conference and Exhibition. 2004. OnePetro.

32. Da Wang, Y., R.T. Armstrong, and P. Mostaghimi, Enhancing Resolution of Digital Rock Images with Super Resolution Convolutional Neural Networks. Journal of Petroleum Science and Engineering, 2019. 182: p. 106261.

33. Prager, E.J., J.B. Southard, and E.R. VIVONI‐GALLART, Experiments on the entrainment threshold of well‐sorted and poorly sorted carbonate sands. Sedimentology, 1996. 43(1): p. 33-40.

34. Al-Raoush, R. and C. Willson, Extraction of physically realistic pore network properties from three-dimensional synchrotron X-ray microtomography images of unconsolidated porous media systems. Journal of hydrology, 2005. 300(1-4): p. 44-64.

35. Yoshida, H., et al., Fe-oxide concretions formed by interacting carbonate and acidic waters on Earth and Mars. Science advances, 2018. 4(12): p. eaau0872.

36. Blunt, M.J., Flow in porous media—pore-network models and multiphase flow. Current opinion in colloid & interface science, 2001. 6(3): p. 197-207.

37. Munson, B., D. Young, and T. Okiishi, Fundamentals of Fluid Mechanics. 1998.

38. Mahabadi, N., et al., Gas Bubble Migration and Trapping in Porous Media: Pore‐Scale Simulation. Journal of Geophysical Research: Solid Earth, 2018. 123(2): p. 1060-1071.

39. Blott, S.J. and K. Pye, GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth surface processes and Landforms, 2001. 26(11): p. 1237-1248.

40. Teklu, T.W., S.G. Ghedan, and O. Al Farisi. Hybrid Artificial Intelligence and Conventional Empirical Approach for improved Prediction of Log-Derived Permeability of Heterogeneous Carbonate Reservoir. in SPE Production and Operations Conference and Exhibition. 2010. Society of Petroleum Engineers.

41. Ghedan, S.G., T. Weldu, and O. Al-Farisi. Hybrid Log-Derived Permeability Prediction Model for a Heterogeneous Carbonate Reservoir with Tarmat Layers Considering Different Levels of Cutoffs. in Abu Dhabi International Petroleum Exhibition and Conference. 2010. Society of Petroleum Engineers.

42. Nordahl, K. and P.S. Ringrose, Identifying the representative elementary volume for permeability in heterolithic deposits using numerical rock models. Mathematical geosciences, 2008. 40(7): p. 753.

43. Sommer, C., et al. Ilastik: Interactive learning and segmentation toolkit. in 2011 IEEE international symposium on biomedical imaging: From nano to macro. 2011. IEEE.

44. Panton, R.L., Incompressible flow. 2013: John Wiley & Sons.

45. Beard, D. and P. Weyl, Influence of texture on porosity and permeability of unconsolidated sand. AAPG bulletin, 1973. 57(2): p. 349-369.

46. Alberts, L.J.H., Initial porosity of random packing: computer simulation of grain rearrangement. 2005.

47. Zhao, Y.-l., et al., Lattice Boltzmann simulation of gas flow and permeability prediction in coal fracture networks. Journal of Natural Gas Science and Engineering, 2018. 53: p. 153-162.

48. Hatiboglu, C.U. and T. Babadagli, Lattice-Boltzmann simulation of solvent diffusion into oil-saturated porous media. Physical Review E, 2007. 76(6): p. 066309.

49. Gothelf, K.V., LEGO-like DNA structures. science, 2012. 338(6111): p. 1159-1160.

50. Al-Farisi, O., et al. Machine Learning for 3D Image Recognition to Determine Porosity and Lithology of Heterogeneous Carbonate Rock. in SPE Reservoir Characterisation and Simulation Conference and Exhibition. 2019. Society of Petroleum Engineers.

51. Bergen, K.J., et al., Machine learning for data-driven discovery in solid Earth geoscience. Science, 2019. 363(6433): p. eaau0323.

52. Alfarisi, O., et al., Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination. arXiv preprint arXiv:2111.04612, 2021.

53. Carpenter, C., Machine-Learning Image Recognition Enhances Rock Classification. Journal of Petroleum Technology, 2020. 72(10): p. 63-64.

54. Sprawls, P., Magnetic resonance imaging: principles, methods, and techniques. 2000: Medical Physics Publishing.

55. Nedanov, P.B. and S.G. Advani, A method to determine 3D permeability of fibrous reinforcements. Journal of composite materials, 2002. 36(2): p. 241-254.

56. Sitti, M., Mobile microrobotics. 2017: MIT Press.

57. Soille, P., Morphological image analysis: principles and applications. 2013: Springer Science & Business Media.

58. Li, H., S. Misra, and J. He, Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints. Neural Computing and Applications, 2019: p. 1-13.

59. Zhang, H., et al. NMR-MRI Characterization of Low-Salinity Water Alternating CO2 Flooding in Tight Carbonate. in SEG/AAPG/EAGE/SPE Research and Development Petroleum Conference and Exhibition. 2018. OnePetro.

60. Trykozko, A., W. Zijl, and A. Bossavit, Nodal and mixed finite elements for the numerical homogenization of 3D permeability. Computational Geosciences, 2001. 5(1): p. 61-84.

61. Bachmat, Y. and J. Bear, On the concept and size of a representative elementary volume (REV), in Advances in transport phenomena in porous media. 1987, Springer. p. 3-20.

62. Kersey, A.D., Optical fiber sensors for permanent downwell monitoring applications in the oil and gas industry. IEICE transactions on electronics, 2000. 83(3): p. 400-404.

63. Langlois, V., et al., Permeability of solid foam: Effect of pore connections. Physical Review E, 2018. 97(5): p. 053111.

64. Li, J., et al., Permeability tensor and representative elementary volume of saturated cracked soil. Canadian Geotechnical Journal, 2009. 46(8): p. 928-942.

65. Amyx, J., D. Bass, and R.L. Whiting, Petroleum reservoir engineering physical properties. 1960.

66. Ezekwe, N., Petroleum reservoir engineering practice. 2010: Pearson Education.

67. Liu, X. and P.X. Ma, Phase separation, pore structure, and properties of nanofibrous gelatin scaffolds. Biomaterials, 2009. 30(25): p. 4094-4103.

68. Erlich, A., et al., Physical and geometric determinants of transport in fetoplacental microvascular networks. Science advances, 2019. 5(4): p. eaav6326.

69. Liu, X. and P.X. Ma, Polymeric scaffolds for bone tissue engineering. Annals of biomedical engineering, 2004. 32(3): p. 477-486.

70. King Jr, H.E., et al., Pore architecture and connectivity in gas shale. Energy & Fuels, 2015. 29(3): p. 1375-1390.

71. Dong, H. and M.J. Blunt, Pore-network extraction from micro-computerized-tomography images. Physical review E, 2009. 80(3): p. 036307.

72. Li, H., et al. Pore-Scale Lattice Boltzmann Simulation of Oil-Water Flow in Carbonate Rock with Variable Wettability. in Abu Dhabi International Petroleum Exhibition and Conference. 2015. Society of Petroleum Engineers.

73. Hommel, J., E. Coltman, and H. Class, Porosity–permeability relations for evolving pore space: a review with a focus on (bio-) geochemically altered porous media. Transport in Porous Media, 2018. 124(2): p. 589-629.

74. Al Farisi, O., et al. Quantification of Fracture Permeability From Micro Resistivity Logs in Offshore Abu Dhabi Reservoir. in Abu Dhabi International Petroleum Exhibition and Conference. 2006. Society of Petroleum Engineers.

75. Mosser, L., O. Dubrule, and M.J. Blunt, Reconstruction of three-dimensional porous media using generative adversarial neural networks. Physical Review E, 2017. 96(4): p. 043309.

76. Al-Raoush, R. and A. Papadopoulos, Representative elementary volume analysis of porous media using X-ray computed tomography. Powder technology, 2010. 200(1-2): p. 69-77.

77. Costanza‐Robinson, M.S., B.D. Estabrook, and D.F. Fouhey, Representative elementary volume estimation for porosity, moisture saturation, and air‐water interfacial areas in unsaturated porous media: Data quality implications. Water Resources Research, 2011. 47(7).

78. Al-Farisi, O., et al. Revelation of carbonate rock typing—the resolved gap. in SPE/EAGE Reservoir Characterization & Simulation Conference. 2009. European Association of Geoscientists & Engineers.

79. McLane, M., Sandstone: secular trends in lithology in southwestern Montana. Science, 1972. 178(4060): p. 502-504.

80. Pedregosa, F., et al., Scikit-learn: Machine learning in Python. Journal of machine learning research, 2011. 12(Oct): p. 2825-2830.

81. Serra, O., Sedimentary environments from wireline logs. 1985: Schlumberger Limited.

82. Wu, J., X. Yin, and H. Xiao, Seeing permeability from images: fast prediction with convolutional neural networks. Science bulletin, 2018. 63(18): p. 1215-1222.

83. Graton, L.C. and H. Fraser, Systematic packing of spheres: with particular relation to porosity and permeability. The Journal of Geology, 1935. 43(8, Part 1): p. 785-909.

84. Arganda-Carreras, I., et al., Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics, 2017. 33(15): p. 2424-2426.

85. Patzek, T.W. Verification of a complete pore network simulator of drainage and imbibition. in SPE/DOE Improved Oil Recovery Symposium. 2000. Society of Petroleum Engineers.

86. Al-Farisi, O., et al., Well Logs: The Link Between Geology and Reservoir Performance. Abstract Geo2002, 2002. 96.

Presentation materials

20220117-Morphology-Decoder-Inperpore2022-Dr-O-Alfarisi-v3.pdf

20220520-Morphology-Decoder_Dr_O_Alfarisi_v23_.pdf