1887
Volume 62, Issue 3
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Cost reduction in seismic reconnaissance is an issue in geothermal exploration and can principally be achieved by sparse acquisition. To address the adherent decrease in signal/noise ratio, the common‐reflection‐surface method has been proposed. We reduced the data density of an existing 3D dataset and evaluated the results of common‐reflection‐surface processing using seismic attributes. The application of the common‐reflection‐surface method leads in all cases to an improvement of the signal/noise ratio. The most distinct improvement can be seen in the low fold regions. The improvement depends strongly on the midpoint aperture, and there is a tradeoff between reflector continuity and horizontal resolution. If small scale targets are to be imaged, a small aperture size is necessary, which may be far below the Fresnel zone for a specific reflector. The substantial reduction of the data density leads in our case to an irrecoverable information loss.

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2014-01-19
2024-04-24
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References

  1. AgemarT., SchellschmidtR. and SchulzR.2012. Subsurface Temperature Distribution of Germany. Geothermics.
    [Google Scholar]
  2. BouskaJ.1997. Sparse 3‐D. What's in a name? CSEG Recorder22(7).
    [Google Scholar]
  3. BunessH., von HartmannH., RumpelH.‐M., BeileckeT., MusmannP. and SchulzR.2010. Seismic Exploration of Deep Hydrogeothermal Reservoirs in Germany. Expanded Abstracts, World Geothermal Congress, Indonesia, P1346.
    [Google Scholar]
  4. BunessH., von HartmannH., BeileckeT. and SchulzR.2011. Sparse 3D acquisition and CRS processing for the imaging of a fault system: a case study. Expanded Abstracts, 73rd EAGE Conference & exhibition, Austria, P327.
  5. ChopraS. and MarfurtK.2007. Seismic Attributes for Prospect Identification and Reservoir Characterization. SEG Geophysical Developments Series11.
    [Google Scholar]
  6. CordsenA., GalbraithM. and PeirceJ.2000. Planning land 3‐D seismic surveys. SEG Geophysical Developments Series9.
    [Google Scholar]
  7. CrisiP. and HubbellS.2007. Comparison of two low‐fold 3D geometries in Saudi Arabia. Geophysics72(5), A63–A67.
    [Google Scholar]
  8. DuveneckE.2004. Velocity model estimation with data derived wavefront attributes. Geophysics, 69(1), 265–274.
    [Google Scholar]
  9. GierseG., Bezouska‐StrozykH., ThiessenJ. and WeberU.2007. Using CRS Processing to Design a Sparse 3D Acquisition Geometry. Expanded Abstracts, 69th EAGE meeting, London, B043.
  10. HaenelR. and StarosteE.1988. Atlas of Geothermal Resources in the European Community, Austria and Switzerland. Verlag Th. Schaefer, Hannover.
    [Google Scholar]
  11. HertweckT., SchleicherJ. and MannJ.2007. Data stacking beyond CMP. The Leading Edge26(7), 818–827.
    [Google Scholar]
  12. HubralP., HoechtG. and JaegerR.1998. An introduction to the common reflection surface stack. Expanded Abstracts, 60th EAGE meeting, Leipzig, 1–19.
  13. JaegerR., MannJ., HoechtG. and HubralP.2001. Common‐reflection‐surface stack: Image and attributes. Geophysics66(1), 97–109.
    [Google Scholar]
  14. JodocyM. and StoberI.2010. Geologisch‐geothermische Tiefenprofile fuer den baden‐wuerttemmbergischen Teil des noerdlichen und mittleren Oberrheingrabens. Erdoel Erdgas Kohle126, Heft2, 2010.
    [Google Scholar]
  15. LiD., LiZ. and SunX.2011. Improving the quality of prestack seismic data with the CO CRS stacking method. Expanded Abstracts, 82nd SEG meeting, Las Vegas, 3648–3652.
  16. LinerC.L.2004. Elements of 3D seismology. PennWell Publishing, Tulsa, USA.
    [Google Scholar]
  17. LueschenE., DusselM., ThomasR. and SchulzR.2011. 3D seismic survey for geothermal exploration at Unterhaching, Munich, Germany. First Break29(1), 45–54.
    [Google Scholar]
  18. LutzM. and CleintuarM.1999. Geological results of a hydrocarbon exploration campaign in the southern Upper Rhine Graben. Bulletin for Applied Geology, 4, 3–80.
    [Google Scholar]
  19. MuenchW., SistenrichH.P., BueckerC. and BlankeT.2005. Moeglichkeiten der geo­thermischen Stromerzeugung im Oberrheingraben. VGB PowerTech 10/2005.
  20. RumpelH.‐M., SchlüterP. and BunessH.2009. Imaging fault structures for hydrothermal use in the Upper Rhine Graben. Expanded Abstracts, 71th EAGE Meeting, Amsterdam. B05.
  21. StoberI. and JodocyM.2009. Eigenschaften geothermischer Nutzhorizonte im baden‐wuerttembergischen und franzoesischen Teil des Oberrheingrabens. Grundwasser – Zeitschrift der Fachsektion Hydrogeologie14, 127–137.
    [Google Scholar]
  22. TrappeH., ComanR., GierseG., RobinsonS., OwensM. and NielsenE.M.2005. Combining CRS technology and sparse 3D seismic surveys: A new approach to acquisition design. Expanded Abstracts, 67th EAGE meeting, Madrid, P182.
  23. VermeerG.J.O.2002. 3‐D Seismic Survey Design. SEG Geophysical References Series12.
    [Google Scholar]
  24. YilmazÖ.2001. Seismic data analysis: Processing, inversion, and interpretation of seismic data. SEG Investigations in Geophysics Series10.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): CRS; Fault imaging; Upper Rhine Graben

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