1887
Volume 54, Issue 3
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

Accurate prediction of S-wave velocity is of great significance in many aspects, such as inversion, migration, brittleness index calculation, etc. Under normal circumstances, the more types of known input parameters there are, the more accurate the rock’s specific situation, and the more accurate the predicted S-wave velocity. However, considering the actual situation, the types of parameters obtained through logging curves are relatively limited. Some parameters cannot be measured and calculated, which limits the accuracy of S-wave prediction. Therefore, if the parameters can be predicted, a more accurate underground situation is able to described by these parameters. Through rockphysical analysis, the pore aspect ratio and capillary pressure coefficient can affect the velocity. In this way, a new orthorhombic (ORT) rockphysical modeling process considering the pore aspect ratio and capillary pressure coefficient is proposed. The model consists of VTI anisotropy from compaction or textural alignment of minerals, and HTI anisotropy from high-angle fractures caused by stratum pressure, thus showing ORT anisotropy. The inputs of the model can be multiple minerals. And the pore structure and the modulus of the mixed fluids in the pores are considered. We use inverse theory (quantum genetic algorithms) to obtain the pore aspect ratio and capillary pressure coefficient and finally calculate the S-wave velocity through the above parameters. The calculation results in a shale reservoir show that the predicted S-wave velocity is in good agreement with the real logging data. This shows that the proposed rockphysical modeling process and inverse algorithm method are effective.

© 2022 Australian Society of Exploration Geophysicists
Loading

Article metrics loading...

/content/journals/10.1080/08123985.2022.2117602
2023-05-04
2026-01-19

  • From This Site

    /content/journals/10.1080/08123985.2022.2117602
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Backus, G.E.1962. Long-wave elastic anisotropy produced by horizontal layering. Journal of Geophysical Research149, no. 67: 4427–4440.
     [Google Scholar]
  2. Bakulin, A., V.Grechka, and I.Tsvankin. 2000. Estimation of fracture parameters from reflection seismic data – part II: fractured models with orthorhombic symmetry. Geophysics65: 1803–1817.
     [Google Scholar]
  3. Bakulin, A., V.Grechka, and I.Tsvankin. 2002. Seismic inversion for the parameters of two orthogonal fracture sets in a VTI background medium. Geophysics67, no. 1: 292–299.
     [Google Scholar]
  4. Berryman, J.G.1995. Mixture theories for rock properties. In A handbook of physical constants, 205–228. Washington, DC: American Geophysical Union.
     [Google Scholar]
  5. Brie, A., F.Pampuri, A.F.Marsala, et al.1995. Shear sonic interpretation in gas-bearing sands. SPE Annual Technical Conference and Exhibition.
  6. Brown, R.J., and J.Korringa. 1975. On the dependence of elastic properties of a porous rock on the compressibility of the pore fluid. Geophysics40, no. 4: 608–616.
     [Google Scholar]
  7. Castagna, J.P., M.L.Batzle, and R.L.Eastwood. 1986. Relationship between compressional-wave and shear-wave velocities in clastic silicate rocks. Geophysics50, no. 4: 571–581.
     [Google Scholar]
  8. Eshelby, J.D.1957. The determination of the elastic field of an ellipsoidal inclusion, and related problems. Proceedings of Royal Society London241, no. 1226: 376–396.
     [Google Scholar]
  9. Gardner, G., W.Gardner, and R.Gregory. 1974. Formation velocity and density; the diagnostic basics for stratigraphic traps. Geophysics39, no. 6: 770–780.
     [Google Scholar]
  10. Greenberg, M.L., and J.P.Castagna. 1992. Shear-wave velocity estimation in porous rocks: theoretical formulation, preliminary verification, and applications. Geophysical Prospecting40: 195–209.
     [Google Scholar]
  11. Han, K.H., and J.H.Kim. 2000. Genetic quantum algorithm and its application to combinatorial optimization problem. In: IEEE Proceedings of the 2000 Congress on Evolutionary Computation. San Diego. USA, Piscataway, NJ: IEEE Press, 2, 1354–1360.
  12. Han, D.H., A.Nur, and D.Morgan. 1986. Effects of porosity and clay content on wave velocities. Geophysics51, no. 11: 2093–2107.
     [Google Scholar]
  13. Han, K.H., K.H.Park, and C.H.Lee. 2001. Parallel quantum-inspired genetic algorithm for combinatorial optimization problems. In Proc. of the IEEE Congress on Evolutionary Computation, Piscataway, IEEE Press, 1442–1429.
  14. Hashin, Z., and S.Shtrikman. 1962. A variational approach to the theory of effective magnetic permeability of multiphase materials. Applied Physics33: 3125–3131.
     [Google Scholar]
  15. Hashin, Z., and S.Shtrikman. 1963. A variational approach to the elastic behavior of multiphase materials. Mechanics and Physics of Solids11: 127–140.
     [Google Scholar]
  16. Hill, R.1963. Elastic properties of reinforces solids: some theoretical principles. Mechanics and Physics of Solids11: 357–372.
     [Google Scholar]
  17. Hornby, B.E., M.Schwartz, and J.A.Hudson. 1994. Anisotropic effective-medium modeling of the elastic properties of shales. Geophysics59, no. 10: 1570–1583.
     [Google Scholar]
  18. Hsu, K., C.Esmersoy, and M.Schoenberg. 1988. Seismic velocities and anisotropy from high-resolution sonic logs. Seg Technical Program Expanded Abstracts7, no. 1: 1359.
     [Google Scholar]
  19. Hudson, J.A.1980. Overall properties of a cracked solid. Mathematical Proceedings of the Cambridge Philosophical Society88: 371–384.
     [Google Scholar]
  20. Hudson, J.A.1981. Wave speeds and attenuation of elastic waves in material containing cracks. Geophysical Journal International64: 133–150.
     [Google Scholar]
  21. Imhof, M.G.2003. Scale dependence of reflection and transmission coefficients. Geophysics68, no. 1: 322–336.
     [Google Scholar]
  22. Kinoshita, N., and T.Mura. 1971. Elastic fields of inclusions in anisotropic media. Physica Status Solidi5, no. 3: 759–768.
     [Google Scholar]
  23. Krief, M., J.Garat, J.Stellingwerff, and J.Ventre. 1990. A petrophysical interpretation using the velocities of P and S waves (full-waveform sonic). The Log Analyst31: 355–369.
     [Google Scholar]
  24. Kumar, D.2013. Applying Backus averaging for deriving seismic anisotropy of a long-wavelength equivalent medium from well-log data. Journal of Geophysics & Engineering10, no. 5: 86–95.
     [Google Scholar]
  25. Liner, C.L.2006. Layer-induced seismic anisotropy from full wave sonic logs. SEG Technical Program Expanded Abstracts25: 1.
     [Google Scholar]
  26. Liu, Z.H., J.Z.Liu, H.Xia, et al.2017. A new S-wave velocity prediction model for tight dolomite reservoir-A case study on Ordos Basin, Western China, EAGE Conference & Exhibition volume, 1–5.
  27. Margesson, R.W., and C.H.Sondergeld. 1999. Anisotropy and amplitude versus offset: a case history from the West of Shetlands. In Petroleum geology of northwest Europe, eds. A. J.Fleet, and S. A. R.Boldy, 635–643. London: Geological Society.
     [Google Scholar]
  28. Mavko, G., T.Mukerji, and J.Dvorkin. 2020. The rock physics handbook. Cambridge: Cambridge University Press.
  29. Mura, T.1991. Micromechanics of defects in solids. Amsterdam: Kluwer Academic Publisher.
  30. Narayanan, A., and M.Moore. 1996. Quantum-inspired genetic algorithms. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation. (ICEC96). Nogaya, Japan, IEEE Press, 41–46.
  31. Pan, H., H.Li, S.Cai, and Q.Gao. 2020. An improved pride model for S-wave velocity prediction in tight gas sandstones. 82nd EAGE Annual Conference & Exhibition, Jul 2020, Volume, 1–5.
  32. Papageorgiou, G., K.Amalokwu, and M.Chapman. 2016. Theoretical derivation of a Brie-like fluid mixing law. Geophysical Prospecting64, no. 4: 1048–1053.
     [Google Scholar]
  33. Qian, K.2007. Anisotropic rock physics modeling and brittleness analysis of shale reservoir. Doctoral thesis, China University of Petroleum (Beijing).
  34. Reuss, A.1929. Berechnung der fliessgrenzen von misc hkristallen auf grund der plastizitatsbedi ngung füreinkristalle. Zeitschrift fur Angewandte Mathematik und Mechani k9: 49–58.
     [Google Scholar]
  35. Rüger, A.1977. P-wave reflection coefficients for transversely isotropic models with vertical and horizontal axis of symmetry. Geophysics62, no. 3: 713–722.
     [Google Scholar]
  36. Schoenberg, I., and K.Helbig. 1997b. Orthorhombic media: Modeling elastic wave behavior in a vertically fractured earth. Geophysics62, no. 5: 3475–3484.
     [Google Scholar]
  37. Sehoenbegr, M., and K.Helbig. 1997a. Orthorhombic media: Modeling elastic wave behavior in a vertically fracture earth. Geophysics62: 1954–1974.
     [Google Scholar]
  38. Sondergeld, C.H., and C.S.Rai. 2011. Elastic anisotropy of shales. Leading Edge30, no. 3: 324–331.
     [Google Scholar]
  39. Sun, Y., and L.Yang. 2020. Prediction of S-wave velocity based on GRU neural network. Oil Geophysical Prospecting55, no. 3: 484–492.
     [Google Scholar]
  40. Thomsen, L.1986. Weak elastic anisotropy. Geophysics51, no. 10: 1954–1966.
     [Google Scholar]
  41. Tsvankin, I.1997. Anisotropic parameters and P-wave velocity for orthorhombic media. Geophysics62, no. 4: 1292–1309.
     [Google Scholar]
  42. Voigt, W.1928. Lehrbuch der kristallphysik. Leipzig: Teubner.
  43. Wang, X.2013. Application of self-adaptive BP neural network to the prediction of shear wave velocity. Lithologic Reservoirs25, no. 5: 86–88.
     [Google Scholar]
  44. Wood, A.W.1955. A text of sound. New York: The MacMillan Co.
  45. Xu, S., and R.E.White. 2010. A physical model for shear-wave velocity prediction. Geophysical Prospecting44, no. 4: 687–717.
     [Google Scholar]
  46. Yang, L., and L.Ji. 2019. S-wave velocity prediction for complex reservoirs using a deep learning method, SEG Technical Program Expanded Abstracts.
  47. Yuan, H.2007. Study of anisotropic rock physics model and application. Doctoral thesis. China University of Geosciences (Beijing).
  48. Zhao, X.2017. Methodologies of pre-stack seismic inversion for shale gas play. Doctoral thesis. China University of Petroleum (East China).
/content/journals/10.1080/08123985.2022.2117602
Loading
Estimation of pore aspect ratio and capillary pressure coefficient to predict S-wave velocity based on rockphysics modeling in orthorhombic anisotropic reservoirs
Exploration Geophysics 54, 229 (2023); https://doi.org/10.1080/08123985.2022.2117602
/content/journals/10.1080/08123985.2022.2117602
/content/journals/10.1080/08123985.2022.2117602
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): ORT anisotropy; parameter inversing; rockphysics modeling

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