1887
Volume 18 Number 2
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

To improve the resolution of seismic data, it is important to accurately estimate the near‐surface quality factor, , which provides a measure of seismic wave attenuation. In view of the unique advantages provided by uphole surveys when investigating near‐surface structures, they are widely employed to estimate the near‐surface factor. However, the factor estimated using the traditional spectral ratio method is not always precise and provides larger oscillations in the estimated factors due to errors associated with first‐break picking and velocity estimation. Following on the traditional logarithmic spectral ratio method, a new method called the logarithmic spectral ratio integral method was proposed to estimate the layer factor using uphole survey data. It calculates first the weighted integral of the logarithmic spectral ratio in an effective frequency interval between non‐adjacent traces, then makes a linear regression between the inter‐trace travel moveout and the weighted integral of logarithmic spectral ratio under the constraint of velocity stratification. The result of model analysis shows that under an ideal condition (without first‐break picking errors), the layer values estimated by the logarithmic spectral ratio integral method are fairly consistent with the true layer‐specific Q values in the model. In addition, the values estimated from field‐measured data and data from forward modelling with 10% random noise added, both have smaller mean relative errors than the results using traditional spectral ratio method and the double‐linear regression method. A case study is employed and the results show that the layer factor estimated using the new method correlates well with the velocity stratification and is thus applicable for use with various uphole survey observation systems. Furthermore, all results indicate that the logarithmic spectral ratio integral method delivers a more precise and stable estimation of the layered than the other methods, and the anti‐noise characteristics are stronger.

Loading

Article metrics loading...

/content/journals/10.1002/nsg.12090
2020-03-01
2024-04-24
Loading full text...

Full text loading...

References

  1. AdriansyahA. and McMechanG.A.1997. Effects of near‐surface structure, scattering and Q on AVO measurements. 67th annual international SEG meeting, Expanded Abstracts, 146–149.
  2. AlbuquerqueM.A.P., GaiaB.F. and CavalcantiM.G.P. 2011. Oral cleft volumetric assessment by 3D multislice computed tomographic images. International Journal of Oral and Maxillofacial Surgery40, 1280–1288.
    [Google Scholar]
  3. BestA.1997. The effect of pressure on ultrasonic velocity and attenuation in near‐surface sedimentary rocks, Geophysical Prospecting45, 345–364.
    [Google Scholar]
  4. BrzostowskiM.A. and McMechanG.A.1992. 3‐D tomographic imaging of near‐surface seismic velocity and attenuation. Geophysics57, 396–403.
    [Google Scholar]
  5. CavalcaM. and MooreI.2011. Ray‐based tomography for Q estimation and Q compensation in complex media. 81st annual international SEG meeting, Expanded Abstracts, 3989–3993.
  6. ClarkR.A., CarterA.J., NevillP.C. and BensonP.M.2001. Attenuation measurements from surface seismic data – azimuthal variation and timelapsecase studies. 63rd annual international EAGE meeting, Extended Abstracts, L‐28.
  7. DingG.D., ZhangX.M., ChenH.L., QuanH.Y., MaoH.J., TongL.Q., et al. 2018. Effects of excitation depth and geophone coupling on near‐surface Q value estimation. Geophysical Prospecting for Petroleum57, 678–684.
    [Google Scholar]
  8. DuZ.L., ShiZ.J., XuF., ChuY.H. and WangJ.N.2007. The mathematical modeling of absorption compensation in loose medium at surface. Journal of Southwest Petroleum University29, 44–46.
    [Google Scholar]
  9. EI YadariN., ErnstF. and MulderW.2007. Improvement of near‐surface attenuation estimation wave‐propagation modeling. 77th annual internet SEG meeting, Expanded Abstracts, 1242–1246.
  10. EI YadariN., ErnstF. and MulderW.2008. Near‐surface attenuation estimation using wave‐propagation modeling. Geophysics73, U27–U37.
    [Google Scholar]
  11. FuttermanW.I.1962. Dispersive body waves. Journal of Geophysical Research67, 5279–5291.
    [Google Scholar]
  12. GardnerG.H.F., GardnerL.W. and GregoryA.R.1974. Formation velocity and density – the diagnostic basics for stratigraphic traps. Geophysics39, 770–780.
    [Google Scholar]
  13. HuJ.F. and SuY.J. 1999. Estimation of the quality factor in shallow soil using surface waves. Acta Seismological Sinica21, 433–438.
    [Google Scholar]
  14. JengY., TsaiJ.Y. and ChenS.H.1999. An improved method of determining near‐surface Q. Geophysics64, 1608–1617.
    [Google Scholar]
  15. JohnstonD.H., ToksozM.N. and TimurA.1979. Attenuation of seismic waves in dry and saturated rocks: II. Mechanisms. Geophysics44, 691–711.
    [Google Scholar]
  16. LiT.S., ChenB.D. and SuD.R.2004. Application of twin‐well microlog in near surface investigation. Geophysical Prospecting for Petroleum43, 471–474.
    [Google Scholar]
  17. LiW.N., YunM.H., DangP.F. and ZhaoQ.F.2017. Stability Q estimation by dual linear regression based on uphole survey data. Geophysical Prospecting for Petroleum56, 483–490.
    [Google Scholar]
  18. LiuG.C., ChenX.H., DuJ. and LiuY.2011. Seismic Q estimation using S‐transform with regularized inversion. Oil Geophysical Prospecting46, 417–422.
    [Google Scholar]
  19. LiuX.W., TaiS.H. and HeQ.D. 1996. Inversion of quality factor Q for weathered layer using surface waves compensating seismic wave absorption in weathered layer to increase resolution. Geophysical Prospecting for Petroleum35, 89–95.
    [Google Scholar]
  20. MaultzschS., ChapmanM., LiuE. and LiX.2007. Modelling and analysis of attenuation anisotropy in multi‐azimuth VSP data from the Clair field. Geophysical Prospecting55, 627–642.
    [Google Scholar]
  21. PeiJ.Y., ChenS.M., LiuZ.K. and WangJ.M.2001. Near‐surface Q value extraction and amplitude compensation. Progress in Geophysics16, 18–22.
    [Google Scholar]
  22. QuanY. and HarrisJ.M.1997. Seismic attenuation tomography using the frequency shift method. Geophysics63, 895–905.
    [Google Scholar]
  23. RaoY. and WangY.2009. Fracture effects in seismic attenuation images reconstructed by waveform tomography. Geophysics74, R25–R34.
    [Google Scholar]
  24. RiceJ.A., ChristineE.K. and HoustonL.M.1991. Shallow near‐surface effects on seismic waves. 61st annual internet SEG meeting, Expended Abatracts, 747–749.
  25. RickettJ.2006. Integrated estimation of interval‐attenuation profiles. Geophysics71, A19–A2.
    [Google Scholar]
  26. ScholzC.H., SykesL.R. and AggarwalY.P.1973. Earthquake prediction, a physical basis. Science181, 803–810.
    [Google Scholar]
  27. ShiZ. and TianG.2007. Technique of attenuation of near‐surface seismic wave and high‐frequency compensation in western large desert area. Oil Geophysical Prospecting42, 392–395.
    [Google Scholar]
  28. SongJ.J., YuJ.Y., WangC. and ZhangM.2018. Q estimation for near‐surface media and its application in the northern Tahe oilfield, China. Geophysical Prospecting for Petroleum57, 436–442.
    [Google Scholar]
  29. ToksozM.N., JohnstonD.H. and TimurA., 1979. Attenuation of seismic waves in dry and saturated rocks, I: laboratory measurements. Geophysics44, 681–690.
    [Google Scholar]
  30. TonnR., 1991. The determination of the seismic quality factor Q from VSP data: a comparison of different computational methods. Geophysical Prospecting39, 1–27.
    [Google Scholar]
  31. WangJ.M., ChenS.M., SuM.X., WangY.B., WangL.N., GuanX., et al. 2007. A study of the near surface high‐frequency compensation technology in 3‐D seismic exploration. Chinese Journal of Geophysics50, 1837–1843.
    [Google Scholar]
  32. WangS., YangD., LiJ. and SongH., 2015. Q factor estimation based on the method of logarithmic spectral area difference. Geophysics80, V157–V171.
    [Google Scholar]
  33. WangZ.J., FanT.E., MaS.F., FanH.J., ZhangH.L., and MaY.Y.2015. Variation characteristics of seismic wavelet centroid frequency. Oil Geophysical Prospecting50, 861–872.
    [Google Scholar]
  34. WhitcombJ.H., GarmanyJ.E. and AndersonD.L.1973. Earthquake prediction, variation of seismic velocities before the San Francisco earthquake. Science180, 632–635.
    [Google Scholar]
  35. WinklerK. and NurA.1979. Pore fluids and seismic attenuation in rocks, Geophysical Research Letters6, 1–4.
    [Google Scholar]
  36. WinklerK. and NurA.1982. Seismic attenuation, effects of pore fluids and frictional sliding. Geophysics47, 1–15.
    [Google Scholar]
  37. XiaJ. and MillerR.D.2002. Determining Q of near‐surface materials from Rayleigh waves. Journal of Applied Geophysics51, 121–129.
    [Google Scholar]
  38. XiaJ., MillerR.D. and IvanovJ.2010. Estimation of near‐surface quality factors by inversion of Rayleigh‐wave attenuation coefficients. 80th annual internet SEG meeting, Expanded Abstracts, 1908–1915.
  39. XiaJ., XuY., MillerR.D. and IvanovJ.2012. Estimation of near‐surface quality factors by constrained inversion of Rayleigh‐wave attenuation coefficients, Journal of Applied Geophysics82, 137–144.
    [Google Scholar]
  40. XuF., YinC., LiZ., PanS.L., XiongJ. and LvW.B.2009. Investigation method for near‐surface structure with shot in hole and received at surface. Geophysical Prospecting for Petroleum48, 294–298.
    [Google Scholar]
  41. YuC.Y. and ZhouZ.C.2011. Estimation of near surface Q value based on the datasets of the uphole survey in double hole. Oil Geophysical Prospecting46, 89–92.
    [Google Scholar]
  42. YunM.H., CaoW.M., NieY., ZhangZ.H. and LiY.X. 2012. Preliminary study on the attenuation characteristics of seismic wave propagating in near surface layers of carbonate outcropped area. Geophysical Prospecting for Petroleum51, 425–431.
    [Google Scholar]
  43. YunM.H., NieY., LiY.X. and ZhaoQ.F.2013. Definition and mutual relationship of several quality factors. Oil Geophysical Prospecting48, 816–823.
    [Google Scholar]
  44. ZhaiT.L., MaX., PengX.M., WangW.J., GuoC.X., CaiA.B. and LiuC.Y.2018. Near‐surface Q factor measurements by combining surface and cross‐hole seismic surveys. Geophysical Prospecting for Petroleum(in Chinese)57, 685–690.
    [Google Scholar]
  45. ZhangF.C., ZhangX.X., ZhangL.Q. and ZongZ.Y.2016. Extraction method for quality factor Q based on adaptive wavelet decomposition. Oil Geophysical Prospecting55, 41–48.
    [Google Scholar]
  46. ZhangG.D., LiuB., ZhangZ.L., HeJ.G. and RenH.Q.2013. Surface investigation of salt beds in Sanhu Area, Qaidam Basin. Geophysical Prospecting for Petroleum52, 195–200.
    [Google Scholar]
  47. ZhangZ.F., LiuS., YangJ.Y., LiX.M., LiangZ.H. and TuY.G.2012. Surface layer structure multi‐wave investigation method. Geophysical Prospecting for Petroleum51, 257–263.
    [Google Scholar]
  48. ZhaoQ.F.2018. Research and application of near‐surface seismic wave attenuation and inversion methods of quality factor Q. PhD thesis, Henan Polytechnic University.
http://instance.metastore.ingenta.com/content/journals/10.1002/nsg.12090
Loading
/content/journals/10.1002/nsg.12090
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Layered Q value; Near surface; Quality factor; Spectral ratio integral; Uphole survey

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