1887
Volume 65 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Reflection tomography is the industry standard tool for velocity model building, but it is also an ill‐posed inverse problem as its solution is not unique. The usual way to obtain an acceptable result is to regularize tomography by feeding the inversion with some information. The simplest regularization forces the solution to be smooth, implicitly assuming that seismic velocity exhibits some degree of spatial correlation. However, velocity is a rock property; thus, the geometry and structure of rock formations should drive correlation in velocity depth models. This observation calls for constraints driven by geological models.

In this work, we present a set of structural constraints that feed reflection tomography with geometrical information. These constraints impose the desired characteristics (flatness, shape, position, etc.) on imaged reflectors but act on the velocity update. Failure to respect the constraints indicates either velocity inaccuracies or wrong assumptions concerning the constraints.

Reflection tomography with structural constraints is a flexible framework that can be specialized in order to achieve different goals: among others, to flatten the base of salt bodies or detachment surfaces, to recover the horizontalness of oil–water contacts, or to impose the co‐location of the same imaged horizon between PP and PS images.

The straightforward application of structural constraints is that of regularizing tomography through geological information, particularly at the latest stages of the depth imaging workflow, when the depth migration structural setting reached a consistent geological interpretation. Structural constraints are also useful in minimizing the well‐to‐seismic mis‐ties. Moreover, they can be used as a tool to check the consistency of interpreters' hypothesis with seismic data. Indeed, inversion with structural constraints will preserve image focusing only if the interpreters' insights are consistent with the data.

Results from synthetic and real data demonstrate the effectiveness of reflection tomography with structural constraints.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12374
2016-04-05
2024-04-27
Loading full text...

Full text loading...

References

  1. BakulinA., WoodwardM., NicholsN., OsypovK. and ZdravevaO.2010. Localized anisotropic tomography with well information in VTI media. Geophysics75(5), D37–D45.
    [Google Scholar]
  2. BiondiB.2006. 3D Seismic Imaging. Society of Exploration Geophysicists.
    [Google Scholar]
  3. ClappR. and BiondiB.1995. Multi‐azimuth velocity estimation. 65th SEG meeting, Houston, USA, Expanded Abstracts, 1014–1017.
  4. ClappR., BiondiB. and ClaerboutJ.2004. Incorporating geologic information into reflection tomography. Geophysics164, 383–393.
    [Google Scholar]
  5. Delprat‐JannaudF. and LaillyP.1992. What information on the earth model do reflection travel times provide? Journal of Geophysical Research97, 19827–19844.
    [Google Scholar]
  6. EgoziU., YatesM., OmanaJ., Ver WestB., BurkeN., MesaM.et al. 2006. A comprehensive velocity model building approach ‐ Cusiana Cupiagua sur TTI. 76th SEG meeting, New Orleans, USA, Expanded Abstracts, 525–529.
  7. GrechkaV., TsvankinI., BakulinA., HansenJ.O. and SignerC.2002. Joint inversion of PP and PS reection data for VTI media: A North Sea case study. Geophysics67, 1382–1395.
    [Google Scholar]
  8. HirscheK., Porter‐HirscheJ., MewhortL. and DavisR.1997. The use and abuse of geostatistics. The Leading Edge16, 253–260.
    [Google Scholar]
  9. KaipioJ.P., KolehmainenV., VauhkonenM. and SomersaloE.1999. Inverse problems with structural prior information. Inverse Problems15, 713–729.
    [Google Scholar]
  10. NicholsD.1994. Velocity‐stack inversion using Lp norms. SEP report 82, 1–16.
  11. PaigeC.C. and SaundersM.A.1982. LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Transactions on Mathematical Software8, 43–71.
    [Google Scholar]
  12. StorkC. and ClaytonR.W.1992. Using constraints to address the instabilities of automated prestack velocity analysis. Geophysics57, 404–419.
    [Google Scholar]
  13. TikhonovA.N. and ArseninV.Y.2006. Solutions of Ill‐Posed Problems. V.H. Winston and Sons.
    [Google Scholar]
  14. WoodwardM., FarmerP., NicholsD. and CharlesS.1998. Automated 3D tomographic velocity analysis of residual moveout in prestack depth migrated common image point gathers. 68th SEG meeting, New Orleans, USA, Expanded Abstracts 1218–1221.
  15. ZhouC.2013. Incorporating geologic information into reection tomography with a dip oriented Gaussian filter. 75th EAGE Conference, London, UK, Extended abstracts., Th 04 02.
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12374
Loading
/content/journals/10.1111/1365-2478.12374
Loading

Data & Media loading...

  • Article Type: Research Article

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