1887
Volume 67 Number 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Seismic petro‐facies characterization in low net‐to‐gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro‐facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro‐facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies‐dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro‐gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms‐Finnmark Fault Complex. The facies‐based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies‐based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12654
2018-07-04
2020-07-08
Loading full text...

Full text loading...

References

  1. AkiK. and RichardsP.G.1980. Quantitative Seismology: Theory and Methods. Freeman and Co.
    [Google Scholar]
  2. BaigI., FaleideJ.I., JahrenJ. and MondolN.H.2016. Cenozoic exhumation on the southwestern Barents Shelf: estimates and uncertainties constrained from compaction and thermal maturity analyses. Marine and Petroleum Geology73, 105–130.
    [Google Scholar]
  3. BuiaM., CironeC., LeutscherJ., TarranS. and WebbB.2010. Multi‐azimuth 3D survey in the Barents Sea. First Break28, 65–69.
    [Google Scholar]
  4. CastagnaJ.P., SwanH.W. and FosterD.J.1998. Framework for AVO gradient and intercept interpretation. Geophysics63, 948–956.
    [Google Scholar]
  5. CookeD.A. and SchneiderW.A.1983. Generalized linear inversion of reflection seismic data. Geophysics48, 665–676.
    [Google Scholar]
  6. DallandA., WorsleyD. and OfstadK.1988. A Lithostratigraphic Scheme for the Mesozoic and Cenozoic Succession Offshore Mid‐ and Northern Norway. Direktoratet.
    [Google Scholar]
  7. FaleideJ.I., GudlaugssonS.T. and JacquartG.1984. Evolution of the western Barents Sea. Marine and Petroleum Geology1, 123–150, IN1–IN4, 129–136, IN5–IN8, 137–150.
    [Google Scholar]
  8. GabrielsenR.H.1984. Long‐lived fault zones and their influence on the tectonic development of the southwestern Barents Sea. Journal of Geological Society of London141, 651–662.
    [Google Scholar]
  9. GabrielsenR.H., FærsethR.B., JensenL.N., KalheimJ.E. and RiisF.1990. Structural elements of the Norwegian continental shelf. Part I. The Barents Sea region. Norwegian Petroleum Directorate Bulletin6, 33.
    [Google Scholar]
  10. GardnerG.H.F., GardnerL.W. and GregoryA.R.1974. Formation velocity and density – the diagnostic basics for stratigraphic traps. Geophysics39, 770–780.
    [Google Scholar]
  11. GassmannF.1951. Elastic waves through a packing of spheres. Geophysics16, 673–685.
    [Google Scholar]
  12. Glørstad‐ClarkE., FaleideJ.I., LundschienB.A. and NystuenJ.P.2010. Triassic seismic sequence stratigraphy and paleogeography of the western Barents Sea area. Marine and Petroleum Geology27, 1448–1475.
    [Google Scholar]
  13. HampsonD.P., SchuelkeJ.S. and QuireinJ.A.2001. Use of multiattribute transforms to predict log properties from seismic data. Geophysics66, 220–236.
    [Google Scholar]
  14. HenriksenE., RysethA.E., LarssenG.B., HeideT., RønningK., SollidK. and StoupakovaA.V.2011. Tectonostratigraphy of the greater Barents Sea: implications for petroleum systems. Geological Society, London, Memoirs35, 163–195.
    [Google Scholar]
  15. JohansenT.A., JensenE.H., MavkoG. and DvorkinJ.2013. Inverse rock physics modeling for reservoir quality prediction. Geophysics78(2), M1–M18.
    [Google Scholar]
  16. KemperM. and GunningJ.2014. Joint impedance and facies inversion – Seismic inversion redefined. First Break32, 89–95.
    [Google Scholar]
  17. KlausenT.G., RysethA.E., Helland‐HansenW., GawthorpeR. and LaursenI.2014. Spatial and temporal changes in geometries of fluvial channel bodies from the Triassic Snadd formation of offshore Norway. Journal of Sedimentary Research84, 567–585.
    [Google Scholar]
  18. KlausenT.G., RysethA.E., Helland‐HansenW., GawthorpeN. and LaursenI.2015Regional development and sequence stratigraphy of the middle to late Triassic Snadd Formation, Norwegian Barents Sea. Marine and Petroleum Geology62, 102–122.
    [Google Scholar]
  19. MørkM.B.E.1999. Compositional variations and provenance of Triassic sandstones from the Barents shelf. Journal of Sedimentary Research69, 690–710.
    [Google Scholar]
  20. MulrooneyM.J., LeutscherJ. and BraathenA.2017. A 3D structural analysis of the Goliat field, Barents Sea, Norway. Marine and Petroleum Geology86, 192–212.
    [Google Scholar]
  21. OhmS.E., KarlsenD.A. and AustinT.J.F.2008. Geochemically driven exploration models in uplifted areas: examples from the Norwegian Barents Sea. AAPG Bulletin92, 1191–1223.
    [Google Scholar]
  22. PramanikA.G., SinghV., VigR., SrivastavaA.K. and TiwaryD.N.2004. Estimation of effective porosity using geostatistics and multiattribute transforms: a case study. Geophysics69, 352–372.
    [Google Scholar]
  23. RiisF., LundschienB.A., HoyT., MorkA. and MorkM.B.E.2008. Evolution of the Triassic shelf in the northern Barents Sea region. Polar Research27, 318–338.
    [Google Scholar]
  24. SamsM. and CarterD.2017. Stuck between a rock and a reflection: a tutorial on low‐frequency models for seismic inversion. Interpretation5, B17–B27.
    [Google Scholar]
  25. WhitcombeD.N., ConnollyP.A., ReaganR.L. and RedshawT.C.2002. Extended elastic impedance for fluid and lithology prediction. Geophysics67, 63–67.
    [Google Scholar]
  26. WhiteR.E.1980. Partial coherence matching of synthetic seismograms with seismic traces. Geophysical Prospecting28, 333–358.
    [Google Scholar]
  27. YenwongfaiH.D., MondolN.H., FaleideJ.I. and LecomteI.2017a. Prestack simultaneous inversion to predict lithology and pore fluid in the Realgrunnen Subgroup of the Goliat Field, southwestern Barents Sea. Interpretation5, SE75–SE96.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12654
Loading
/content/journals/10.1111/1365-2478.12654
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Facies , Inversion , Neural network , Reservoir characterization and Rock physics
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