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Volume 67, Issue 7
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The technique of seismic amplitude‐versus‐angle inversion has been widely used to estimate lithology and fluid properties in seismic exploration. The amplitude‐versus‐angle inversion problem is intrinsically ill‐posed and generally stabilized by the use of L2‐norm regularization methods but with drawback of smoothing important boundaries between adjacent layers. In this study, we propose a sparse Bayesian linearized solution for amplitude‐versus‐angle inversion problem to preserve the sharp geological interfaces. In this regard, constraint term with two regularization functions is presented: the sparse constraint regularization and the low‐frequency model information. In addition, to obtain high‐resolution reflectivity estimation, the model parameters decorrelation technique combined with dipole decomposition method is employed. We validate the applicability of the presented method by both synthetic and real seismic data from the Gulf of Mexico. The accuracy improvement of the presented method is also confirmed by comparing the results with the commonly used Bayesian linearized amplitude‐versus‐angle inversion.

© 2019 European Association of Geoscientists & Engineers © 2019 European Association of Geoscientists & Engineers
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2019-05-31
2024-04-19

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References

  1. AkiK. and RichardsP.G.1980. Quantitative Seismology: Theory and Methods. W. H. Freeman and Co.
     [Google Scholar]
  2. AlemieW. and SacchiM.D.2011. High‐resolution three‐term AVA inversion by means of a trivariate Cauchy probability distribution. Geophysics76, R43–R55.
     [Google Scholar]
  3. AsterR.C., BorchersB. and ThurberC.H.2012. Parameter Estimation and Inverse Problems, 2nd edn. Academic Press.
     [Google Scholar]
  4. AzevedoL., CorreiaP., NunesR. and SoaresA.2013. Geostatistical AVA direct facies inversion. Mathematics of Planet Earth, Lecture Notes in Earth System Sciences, pp. 565–569. Springer.
     [Google Scholar]
  5. AzevedoL., NunesR., SoaresA., NetoG.S. and MartinsT.S.2018. Geostatistical seismic amplitude‐versus‐angle inversion. Geophysical Prospecting66, 116–131.
     [Google Scholar]
  6. AzevedoL. and SoaresA.2017. Geostatistical Methods for Reservoir Geophysics. Springer.
     [Google Scholar]
  7. BackusM.M.1987. Amplitude versus offset: a review. Proceedings of the 57th International Meeting of the Society of Exploration Geophysics, pp. 359–364.
  8. BaeK. and MallickB.K.2004. Gene selection using a two‐level hierarchical Bayesian model. Bioinformatics20, 3423–3430.
     [Google Scholar]
  9. BulandA.2002. Bayesian Seismic AVO Inversion: Ph.D. Thesis, Norwegian University of Science and Technology.
  10. BulandA. and OmreH.2003. Bayesian linearized AVA inversion. Geophysics68, 185–198.
     [Google Scholar]
  11. BulandA. and OuairY.El.2006. Bayesian lithology and fluid prediction from seismic prestack data. Geophysics73,C13–C21.
     [Google Scholar]
  12. CasellaG.2001. Empirical Bayes Gibbs sampling. Biostatistics2, 485–500.
     [Google Scholar]
  13. CastagnaJ.P., BatzleM.L. and KanT.K.1993. Rock physics – The link between rock properties and AVA response. In: Offset‐Dependent Reflectivity – Theory and Practice of AVA Analysis, Vol. 8 (eds J.P.Castagna and M.Backus), pp. 135–171. Investigations in Geophysics.
     [Google Scholar]
  14. CorduaK.S., HansenT.M. and MosegaardK.2010. Nonlinear AVA Inversion Using Geostatistical a Priori Information. IAMG, Budapest, Hungary.
     [Google Scholar]
  15. DahleP., HaugeR., KolbjornsenO., Della RosaE., LuoniF. and Junio MariniA.2007. Geostatistical AVA inversion on a deepwater oil field. Petroleum Geostatistics,EAEG, Cascais, Portugal.
     [Google Scholar]
  16. DahleT. and UrsinB.1991. Parameter estimation in a one‐dimensional anelastic medium. Journal of Geophysical Research96, 217–233.
     [Google Scholar]
  17. DellaportasP., ForsterJ. and NtzoufrasI.2002. On Bayesian model and variable selection using MCMC. Statistical Computing12, 27–36.
     [Google Scholar]
  18. DonohoD.L.2006, Compressed sensing. IEEE Transactions on Information Theory52, 1289–1306.
     [Google Scholar]
  19. DowntonJ.E.2005. Seismic Parameter Estimation from AVA Inversion. PhD thesis, University of Calgary.
     [Google Scholar]
  20. DowntonJ.E. and LinesL.R.2004. Three term AVA waveform inversion. 74th Annual International Meeting, SEG, Expanded Abstracts, 215–218.
  21. DuQ.Z. and YanH.Z.2013. PP and PS joint AVO inversion and fluid prediction. Journal of Applied Geophysics90, 110–118.
     [Google Scholar]
  22. GholamiA., AghamiryH.S. and AbbasiM.2018. Constrained nonlinear amplitude variation with offset inversion using Zoeppritz equations. Geophysics83, R245–R255.
     [Google Scholar]
  23. GholamiA., SiahkoohiH.R.2010. Regularization of linear and non‐linear geophysical ill‐posed problems with joint sparsity constraints. Geophysical Journal International180, 871–882.
     [Google Scholar]
  24. GranaD.2018. Joint facies and reservoir properties inversion. Geophysics, 83, M15–M24.
     [Google Scholar]
  25. GranaD. and Della RossaE.2010. Probabilistic petrophysical‐properties estimation integrating statistical rock physics with seismic inversion. Geophysics75, O21–O37.
     [Google Scholar]
  26. GranaD., MukerjiT., DvokinJ. and MavkoG.2012. Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method. Geophysics77, M53–M72.
     [Google Scholar]
  27. KuoL. and MallickB.1998. Variable selection for regression models. Sankhya B60, 65–81.
     [Google Scholar]
  28. KurtH.2007. Joint inversion of AVA data for elastic parameters by bootstrapping. Computers & Geosciences33, 367–382.
     [Google Scholar]
  29. LehochiI., AvsethP. and HadziavidicV.2015. Probabilistic estimation of density and shear information from Zeoppritz's equation. Leading Edge34, 1036–47.
     [Google Scholar]
  30. LevyS. and FullagarP.K.1981. Reconstruction of a sparse spike train from a portion of its spectrum and application to high‐resolution deconvolution. Geophysics46, 1235–1243.
     [Google Scholar]
  31. LiC., ZhangJ. and ZhuaZ.2017. Bayesian AVA inversion with consistent angle parameters. Journal of Applied Geophysics139, 246–256.
     [Google Scholar]
  32. LinesL.R.1998. Density contrast is difficult to determine from AVO. CREWES Research Report, Vol. 10.
     [Google Scholar]
  33. LiuC., SongC., LuaQ., LiuaY., FengX. and GaoY.2015. Impedance inversion based on L1 norm regularization. Journal of Applied Geophysics120, 7–13.
     [Google Scholar]
  34. LuJ., YangZ., WangY. and ShiY.2015. Joint PP and PS AVA seismic inversion using exact Zoeppritz equations. Geophysics80, R239–R250.
     [Google Scholar]
  35. MahmoudianF.2006. Linear AVA inversion of multi‐component surface seismic and VSP data. MS thesis, University of Calgary.
     [Google Scholar]
  36. MahmoudianF. and MagraveG.F.2004. AVO inversion of multi‐component data for P‐ and S‐ impedance. CREWES Research Report15, 1–34.
     [Google Scholar]
  37. MalkotiA., VedantiN. and TiwariR.K.2018. An algorithm for fast elastic wave simulation using a vectorized finite difference operator. Computers and Geosciences116, 23–31.
     [Google Scholar]
  38. MartinG.S., WileyR. and MartfurtK.J.2006. Marmousi2: an elastic upgrade for Marmousi. Leading Edge25, 156–166.
     [Google Scholar]
  39. MenkeW.2012. Geophysical Data Analysis: Discrete Inverse Theory, 3rd edn. Elsevier, 350 pp.
     [Google Scholar]
  40. MollajanA., MemarianH. and QuintalB.2019. Imperialist competitive algorithm (ICA) optimization method for nonlinear AVA inversion. Geophysics.
     [Google Scholar]
  41. OldenburgD.W., ScheuerT. and LevyS.1983. Recovery of the acoustic impedance from reflection seismograms. Geophysics48, 1318–1337.
     [Google Scholar]
  42. PanX., ZhangG., ZhangJ. and YinX.2017. Zeoppritz‐based AVO inversion using an improved Markov chain Monte Carlo method. Petroleum Science14, 75–83.
     [Google Scholar]
  43. Passos de FigueiredoL., GranaD., Luis BordignonF., SantosM., RoisenbergM. and RodriguesB.B.2018. Joint Bayesian inversion based on rock‐physics prior modeling for the estimation of spatially correlated reservoir properties. Geophysics83, 1–53.
     [Google Scholar]
  44. ParkT. and CasellaG.2008. The Bayesian Lasso. Journal of the American Statistical Association103, 681–686.
     [Google Scholar]
  45. PérezD.O., VelisD.R. and SacchiM.D.2013. High‐resolution prestack seismic inversion using a hybrid FISTA least‐squares strategy. Geophysics78, R185–R95.
     [Google Scholar]
  46. RabbenT.E., TjelmelandH. and UrsinB.2008. Nonlinear Bayesian joint inversion of seismic reflection coefficients. Geophysical Journal International173, 265–280.
     [Google Scholar]
  47. RobertsG., WentD., HawkinsK., WilliamsG., KabirN., NashT. and Whitcombe. D.2002. A case study of long‐offset acquisition, processing and interpretation over the Harding Field, North Sea. 64th Annual International Conference and Exhibition, EAGE, Extended Abstracts, 27–30.
  48. RussellB.H., GrayD., HampsonD.P.2013. Linearized AVA and poroelasticity. Geophysics76, C19–C29.
     [Google Scholar]
  49. SacchiM.D.1997. Reweighting strategies in seismic deconvolution. Geophysical Journal International129, 651–656.
     [Google Scholar]
  50. SmithG.C. and GidlowP.M.1987. Weighted stacking for rock property estimation and gas detection of gas. Journal of Geophysical Prospecting35, 993–1014.
     [Google Scholar]
  51. TarantolaA.2005. Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM.
     [Google Scholar]
  52. TaylorH.L., BanksS.C. and McCoyJ.F.1979. Deconvolution with the L1 norm. Geophysics44, 39–52.
     [Google Scholar]
  53. TibshiraniR.1996. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society58, 267–288.
     [Google Scholar]
  54. VeireH.H. and LandrøM.2006. Simultaneous inversion of PP and PS seismic data. Geophysics71, R1–R10.
     [Google Scholar]
  55. YinX.Y., LiuX.J. and ZongZ.Y.2015. Pre‐stack basis pursuit seismic inversion for brittleness of shale. Petroleum Science12, 618–627.
     [Google Scholar]
  56. YinX., LiK. and ZongZ.2016. Resolution enhancement of robust Bayesian pre‐stack inversion in the frequency domain. Journal of Geophysics and Engineering13, 646–656.
     [Google Scholar]
  57. YuanM. and LinY.2005. Efficient empirical Bayes variable selection and estimation in linear models. Journal of the American Statistical Association100, 1215–1225.
     [Google Scholar]
  58. ZhangF., DaiR. and LiuH.2014. Seismic inversion based on L1‐norm misfit function and total variation regularization. Journal of Applied Geophysics109, 111–118.
     [Google Scholar]
  59. ZhangR. and CastagnaJ.2011. Seismic sparse‐layer reflectivity inversion using basis pursuit decomposition. Geophysics76, R147–R158.
     [Google Scholar]
  60. ZhangR., SenM.K. and SrinivasanS.2013. A prestack basis pursuit seismic inversion. Geophysics78, R1–11.
     [Google Scholar]
  61. ZhangS.X., YinX.Y. and ZhangF.C.2009. Fluid discrimination study from fluid elastic impedance (FEI). SEG Technical Program Expanded Abstracts.
  62. ZhiL.X., ChenS.Q. and LiX.Y.2013. Joint AVA inversion of PP and PS waves using exact Zoeppritz equation. 83rd Annual International Meeting, SEG, Expanded Abstracts, 457–461.
  63. ZhouL., LiJ., ChenX., LiuX. and ChenL.2017. Prestack AVA inversion of exact Zoeppritz equations based on modified trivariate Cauchy distribution. Journal of Applied Geophysics138, 80–90.
     [Google Scholar]
  64. ZongZ.Y., YinX.Y. and WuG.C.2012. Elastic impedance variation with angle inversion for elastic parameters. Journal of Geophysics and Engineering9, 247–260.
     [Google Scholar]
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Sparse Bayesian linearized amplitude‐versus‐angle inversion
Geophysical Prospecting 67, 1745 (2019); https://doi.org/10.1111/1365-2478.12789
/content/journals/10.1111/1365-2478.12789
/content/journals/10.1111/1365-2478.12789
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  • Article Type: Research Article
Keyword(s): A priori constraint; AVA inversion; Bayesian method; Posterior distribution; Sparsity

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