1887
  1. Home
  2. A-Z Publications
  3. Geophysical Prospecting
  4. Volume 70, Issue 5
  5. Article
Volume 70, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Thanks to the recent developments in both hardware and software capabilities of computers, intelligent rock physics modelling has emerged as an alternative to the conventional approach to the rock physics. Respecting the crucial contribution of the pore geometry into the rock physics modelling, I propose an accurate yet cost‐ and time‐efficient intelligent framework to measure pore space based on digital rock physics. In this method, total pore space was calculated after estimating the pore geometry through pattern recognition on thin section images captured through polarized‐light microscopy. Next, applying three different multi‐class classifiers (radial basis function, support vector machine and k‐nearest neighbours) for estimating pore type and aspect ratio, the best results were obtained using the fuzzy Sugeno integral, and the pore types were classified according to the most widely used pore type classification scheme. Next, an artificial neural network was applied to interpolate discrete data points (thin sections) into continuous profiles of pore type and aspect ratio. Subsequently, as a case study, the proposed approaches were applied to a real‐world carbonate reservoir for modelling the P‐ and S‐wave velocities through a rock physics model. Verifying the modelling results against ultrasonic and measured well‐logging data, the methodology showed promising performance at acceptable levels of uncertainty. The most significant advantage of the intelligent pore type quantification over the conventional methods was found to be its ability to estimate elastic properties with good accuracy. The key findings of this research include automatic detection of the pore types and aspect ratio, provision of a database of pore geometry, attenuation of uncertainty in pore type characterization and improvement of rock physics modelling in the absence of reliable S‐wave velocity data.

© 2022 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13204
2022-05-18
2024-04-25

  • From This Site

    /content/journals/10.1111/1365-2478.13204
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Adams, J.A.S. & Gasparini, P. (1970) Gamma‐Ray Spectrometry of Rocks, 1st edition. New York: Elsevier Pub. Co , p. 308.
     [Google Scholar]
  2. Akbar, M., Petricola, M.J.C., Watfa, M. & Dhabi, A. (1995) Classic interpretation problems: evaluating carbonates. Oilfield Review, 7(1), 38–57.
     [Google Scholar]
  3. Al‐Bazzaz, W.H. & Al‐Mehanna, Y.W. (2007) Porosity, permeability and MHR calculations using SEM and thin‐section images for characterizing complex Mauddud‐Burgan carbonate reservoir. In: Asia Pacific Oil and Gas Conference and Exhibition, Jakarta.
  4. Amalokwu, K., Best, A.I. & Chapman, M. (2016) Effects of aligned fractures on the response of velocity and attenuation ratios to water saturation variation: a laboratory study using synthetic sandstones. Geophysical Prospecting, 64(4), 942–957.
     [Google Scholar]
  5. Amoudi, A.A. & Zang, L. (2000) Application of radial basis function networks for solararray modeling and maximum power‐point prediction. IEE Proceedings: Generation, Transmission and Distribution, 147(5), 310–316.
     [Google Scholar]
  6. Anderson, J.A. (1995) An Introduction to Neural networks. Cambridge, MA: MIT Press.
     [Google Scholar]
  7. Andrä, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Kabel, M., et al. (2013a) Digital rock physics benchmarks—Part I: Imaging and segmentation. Computers and Geosciences, 50, 25–32. https://www.sciencedirect.com/science/article/pii/S0098300412003147
     [Google Scholar]
  8. Andrä, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., & Kabel, M., et al. (2013b) Digital rock physics benchmarks—Part II: Computing effective properties. Computers and Geosciences, 50, 33–43. https://www.sciencedirect.com/science/article/pii/S0098300412003172
     [Google Scholar]
  9. Anselmetti, F.S. & Eberli, G.P. (1993) Controls on sonic velocity in carbonate rocks. Pure and Applied Geophysics, 141(2), 287–323.
     [Google Scholar]
  10. Anselmetti, F.S. & Eberli, G.P. (1999) The velocity‐deviation log: a tool to predict pore type and permeability trends in carbonate drill holes from sonic and porosity or density logs. AAPG Bulletin, 83(3), 450–466.
     [Google Scholar]
  11. Anselmetti, F.S., Luthi, S. & Eberli, G.P. (1998) Quantitative characterization of carbonate pore systems by digital image analysis. AAPG Bulletin, 82(2), 1815–1836.
     [Google Scholar]
  12. Arbib, M. (2003) The Handbook of Brain Theory and Neural Network. Cambridge, MA: MIT Press.
     [Google Scholar]
  13. Archie, G.E. (1952) Classification of carbonate reservoir rocks and petrophysical considerations. AAPG Bulletin, 36(2), 278–298.
     [Google Scholar]
  14. Assefa, S., McCann, C. & Sothcott, S. (2003) Velocities of compressional and shear waves in limestones. Geophysical Prospecting, 51(1), 1–13.
     [Google Scholar]
  15. Avseth, P., Mukerji, T., & Mavko, G. (2005) Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk. Cambridge: Cambridge University Press, p. 359.
     [Google Scholar]
  16. Bashah, N.S.I. & Pierson, B.J. (2012) The impact of pore geometry and microporosity on velocity‐porosity relationship in carbonates of Central Luconia, Sarawak: Paper presented at the Presentation at AAPG International Conference and Exhibition, Singapore.
  17. Birch, A.F. (1960) The velocity of compressional waves in rocks to 10 kilobars. Part 1. Journal of Geophysical Research, 65(4), 1083–1102.
     [Google Scholar]
  18. Bishop, C.M. (1995) Neural Networks for Pattern Recognition. Oxford: Oxford University Press.
     [Google Scholar]
  19. Bizon, K., Continillo, G., Lombardi, S., Mancaruso, E. & Vaglieco, B.M. (2014) ANN‐based virtual sensor for on‐line prediction of in‐cylinder pressure in a diesel engine: Proceedings of the 24th European Symposium on Computer Aided Process Engineering.
  20. Buades, A., Coll, B. & Morel, J.M. (2005) A non‐local algorithm for image denoising. In: Proceeding of the International Conference on Computer Vision and Pattern Recognition, 2, 60–65.
     [Google Scholar]
  21. Choquette, P.W. & Pray, L.C. (1970) Geologic nomenclature and classification of porosity in sedimentary carbonates. AAPG Bulletin, 54(2), 207–250.
     [Google Scholar]
  22. Corinna, C. & Vapnik, V. (1995) Support‐vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018.
     [Google Scholar]
  23. Das, V., Mukerji, T. & Mavko, G. (2019) Numerical simulation of coupled fluid‐solid interaction at the pore scale. A Digital Rock‐Physics Technology, 84(4), WA71–WA81. https://library.seg.org/doi/abs/10.1190/geo2018‐0488.1.
     [Google Scholar]
  24. Duda, R.O., Hart, P.E. & Stork, D.G. (2002) Pattern Classification, 2nd edition. New York: Wiley.
     [Google Scholar]
  25. Dvorkin, J. & Nur, A. (1993) Dynamic poroelasticity: a unified model with the squirt and the Biot mechanisms. Geophysics, 58(4), 524–533.
     [Google Scholar]
  26. Dvorkin, J., Derzhi, N., Diaz, E. & Fang, Q. (2011) Relevance of computational rock physics. Geophysics, 76(5), E141–E153. https://library.seg.org/doi/abs/10.1190/geo2010‐0352.1.
     [Google Scholar]
  27. Eberli, G., Baechle, G., Anselmetti, F. & Incze, M. (2003) Factors controlling elastic properties in carbonate sediments and rocks. The Leading Edge, 22, 654–660.
     [Google Scholar]
  28. Ghiasi‐Freez, J., Soleimanpour, I., Kadkhodaie‐Ilkhchi, A., Ziaii, M., Sedighi, M. & Hatampour, A. (2012) Semi‐automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers. Computers & Geosciences, 45, 36–45. http://doi.org/10.1016/j.cageo.2012.03.006.
     [Google Scholar]
  29. Gonzalez, R.C. & Woods, R.E. (2008) Digital Image Processing. 3rd edition. New Jersey: Prentice Hall.
     [Google Scholar]
  30. Han, D. & Batzle, M.L. (2004) Gassmann's equation and fluid‐saturation effects on seismic velocities. Geophysics, 69, 398–405. https://doi.org/10.1190/1.1707059.
     [Google Scholar]
  31. Hu, Y., Zhao, J., Sun, L., Long, T. & Li, F. (2018) Digital rock physics based pore type effect analysis on acoustic properties of carbonate rocks. 80th Annual International Conference and Exhibition, EAGE, Extended Abstracts.
  32. Keys, R.G. & Xu, S. (2002) An approximation for the Xu‐White velocity model. Geophysics, 67(5), 1406–1414. https://library.seg.org/doi/abs/10.1190/1.1512786.
     [Google Scholar]
  33. Kumar, M. & Han, D. (2005) Pore shape effect on elastic properties of carbonate rocks. SEG Technical Program Expanded Abstracts, Society of Exploration Geophysicists, 1477–1480.
     [Google Scholar]
  34. Kuncheva, L.I. (2001) Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Sets and Systems, 122, 401–407.
     [Google Scholar]
  35. Kuster, G.T. & Toksöz, M.N. (1974a) Velocity and attenuation of seismic waves in two‐phase media: Part I. theoretical formulations. Geophysics, 39(5), 587–606.
     [Google Scholar]
  36. Kuster, G.T. & Toksöz, M.N. (1974b) Velocity and attenuation of seismic waves in two‐phase media: Part II. experimental results. Geophysics, 39(5), 607–618.
     [Google Scholar]
  37. Larionov, V.V. (1969) Borehole Radiometry. Moscow: Nedra.
     [Google Scholar]
  38. Lebedev, M., Toms‐Stewart, J., Clennell, B., Pervukhina, M., Shulakova, V., Paterson, L.et al. (2009) Direct laboratory observation of patchy saturation and its effects on ultrasonic velocities. The Leading Edge, 28(1), 24–27.
     [Google Scholar]
  39. Lipo, W. (2005) Support Vector Machines: Theory and Applications. New York: Springer.
     [Google Scholar]
  40. Lønøy, A. (2006) Making sense of carbonate pore systems. Aapg Bulletin, 90, 9.
     [Google Scholar]
  41. Lubis, L.A. & Harith, Z.Z.T. (2014) Pore type classification on carbonate reservoir in offshore Sarawak using rock physics model and rock digital images. IOP Conference Series: Earth and Environmental Science, 19, 012003. http://doi.org/10.1088/1755‐1315/19/1/012003.
     [Google Scholar]
  42. Lucia, F.J. (2007) Carbonate Reservoir Characterization: An Integrated Approach, 2nd edition. Berlin Heidelberg: Springer‐Verlag.
     [Google Scholar]
  43. Marmo, R., Amodio, S., Tagliaferri, R., Ferreri, V. & Longo, G. (2005) Textural identification of carbonate rocks by image processing and neural network: methodology proposal and examplesComputational Geosciences, 31, 649e659.
     [Google Scholar]
  44. Martinez‐Martinez, J., Benavente, D. & Garcı´adel Cura, M.A. (2007) Petrographic quantification of brecciated rocks by image analysis: application to the interpretation of elastic wave velocities. Journal of Engineering Geology, 90, 41–54.
     [Google Scholar]
  45. Mavko, G., Mukerji, T. & Dvorkin, J. (2009) The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media. Cambridge: Cambridge University Press.
     [Google Scholar]
  46. Mirkamali, M.S., Javaherian, A., Hassani, H., Saberi, M.R. & Hosseini, S.A. (2020) Quantitative pore type characterization from well logs based on the seismic petrophysics in a carbonate reservoir. Geophysical Prospecting, 68(7), 2195–2216. http://doi.org/10.1111/1365‐2478.12989.
     [Google Scholar]
  47. Mollajan, A., Ghiasi‐Freez, J. & Memarian, H., (2016) Improving pore type identification from thin section images using an integrated fuzzy fusion of multiple classifiers. Journal of Natural Gas Science and Engineering, 31, 396–404.
     [Google Scholar]
  48. Motiei, H. (1993) Stratigraphy of Zagros. Tehran: Geological Survey of Iran.
     [Google Scholar]
  49. Park, J. & Sandberg, I.W. (1991) Universal approximation using radial basis function networks. Neural Computer, 3(2), 246–257.
     [Google Scholar]
  50. Prananda, A., Rosid, M. & Widodo, R. (2019) Pore type and porosity distribution of carbonate reservoir based on 3D seismic inversion in ‘P’ Field Salawati Basin. E3S Web of Conferences, 125, 15002.
     [Google Scholar]
  51. Ritter, G.L., Woodruff, H.B., Lowry, S.R. & Isenhour, T.L. (1975) An algorithm for a selective nearest neighbor decision rule. IEEE Transactions on Information Theory, 21(6), 665–669.
     [Google Scholar]
  52. Rittscher, J., Machiraju, R. & Wong, S. (2008) Microscopic Image Analysis for Life Science Application. Norwood, MA: Artech House, INC, p. 489.
     [Google Scholar]
  53. Ruzyla, K. (1986) Characterization of pore space by quantitative image analysis. SPE Formation Evaluation, 1(4), 389–398. https://doi.org/10.2118/13133‐PA.
     [Google Scholar]
  54. Sayers, C.M. (2008) The elastic properties of carbonates. The Leading Edge, 27(8), 1020–1024.
     [Google Scholar]
  55. Schepp, L.L., Ahrens, B., Balcewicz, M., Duda, M., Nehler, M. & Osorno, M. (2020) Digital rock physics and laboratory considerations on a high‐porosity volcanic rock. Scientific Reports, 10(1), 5840. https://doi.org/10.1038/s41598‐020‐62741‐1.
     [Google Scholar]
  56. Shams, M., Singh, K., Bijeljic, B. & Blunt, M.J., (2021) Direct numerical simulation of pore scale trapping events during capillary dominated two phase flow in porous media. Transport in Porous Media, 138, 443–458. https://doi.org/10.1007/s11242‐021‐01619‐w.
     [Google Scholar]
  57. Sharifi, J., Ghiasi‐Freez, J. & Taheri, S.M. (2021) Pore type classification using multi‐class classifiers: application in rock physics modeling. 82th EAGE Conference & Exhibition. Amsterdam, Netherlands.
  58. Sharifi, J., Hafezi Moghaddas, N., Lashkaripour, G.R., Javaherian, A. & Mirzakhanian, M. (2019a) Application of extended elastic impedance in seismic geomechanics. Geophysics, 84(3), R429–R446. https://doi.org/10.1190/geo2018‐0242.1.
     [Google Scholar]
  59. Sharifi, J., Mirzakhanian, M., Saberi, M.R., Moradi, M. & Sharifi, M. (2018) Quantification of pore type system in carbonate rocks for rock physics modeling. 80th Annual International Conference and Exhibition, EAGE, Extended Abstracts, Tu A11 12, https://doi.org/10.3997/2214‐4609.201800674.
  60. Sharifi, J., Saberi, M.R. & Javaherian, A. (2019b) Estimation of pore types in a carbonate reservoir through artificial neural networks. 81st Annual International Conference and Exhibition, EAGE, Extended Abstracts, Tu R02 08, https://doi.org/10.3997/2214‐4609.201900799.
  61. Soete, J., Kleipool, L., Claes, H., Claes, S., Hamaekers, H., Kele, S.et al. (2014) Acoustic properties in travertines and their relation to porosity and pore types. Marine and Petroleum Geology, 59, 320–335.
     [Google Scholar]
  62. Verri, I., Della Torre, A., Montenegro, G., Onorati, A., Duca, S. & Mora, C.A. (2017) Development of a digital rock physics workflow for the analysis of sandstones and tight rocks. Journal of Petroleum Science and Engineering, 156, 790–800. http://www.sciencedirect.com/science/article/pii/S0920410517305430.
     [Google Scholar]
  63. Wang, L. (2005) Support Vector Machines: Theory and ApplicationsBerlin/Heidelberg: Springer Verlag, p. 441.
     [Google Scholar]
  64. Weger, R.J., Eberli, G.P., Baechle, G.T., Massaferro, J.L. & Sun, Y.F. (2009) Quantification of pore structure and its effect on sonic velocity and permeability in carbonates. AAPG Bulletin, 93(10), 1297–1317. http://doi.org/10.1306/05270909001.
     [Google Scholar]
  65. Winkler, K.W. (1986) Estimates of velocity dispersion between seismic and ultrasonic frequencies. Geophysics, 51, 183–189
     [Google Scholar]
  66. Woods, K., Kegelmeyer, W.P. & Bowyer, K. (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4), 405–410.
     [Google Scholar]
  67. Xu, S. & Payne, M.A. (2009) Modeling elastic properties in carbonate rocks. The Leading Edge, 28, 66–74, https://doi.org/10.1190/1.3064148.
     [Google Scholar]
  68. Xu, S. & White, R.E. (1995) A new velocity model for clay–sand mixtures. Geophysical Prospecting, 43, 91–118.
     [Google Scholar]
  69. Youn, H. & Tonon, F. (2010) Multi‐stage triaxial test on brittle rock. International Journal of Rock Mechanics and Mining Sciences, 47(4), 678–684.
     [Google Scholar]
  70. Yurikov, A., Lebedev, M. & Pervukhina, M., (2017), Ultrasonic velocity measurements on thin rock samples: Experiment and numerical modeling. Geophysics, 83(2), MR47–MR56. http://doi.org/10.1190/geo2016‐0685.1.
     [Google Scholar]
  71. Zhang, Z. (2016) Introduction to machine learning: K‐nearest neighbors. Annals of Translational Medicine, 4(11), 218. doi: 10.21037/atm.2016.03.37.
     [Google Scholar]
  72. Zhao, L., Nasser, M. & Han, D. (2013) Quantitative geophysical pore‐type characterization and its geological implication in carbonate reservoirs. Geophysical Prospecting, 61, 827–841.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.13204
Loading
Intelligent pore type characterization: Improved theory for rock physics modelling
Geophysical Prospecting 70, 921 (2022); https://doi.org/10.1111/1365-2478.13204
/content/journals/10.1111/1365-2478.13204
/content/journals/10.1111/1365-2478.13204
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Aspect ratio; Carbonate rock physics model; Digital image analysis; Multi‐class classifier; Pore type

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error