1887

Abstract

Summary

A major challenge in reservoir modeling is the accurate representation of lithofacies in a defined framework to honor geologic knowledge and available subsurface data. Considering the impact of lithofacies distribution on reservoir petrophysics, a two-stage methodology was applied to enhance lithofacies characterization in the Hugin formation, Volve field. The approach applies the Truncated Gaussian Simulation method that relies on sediment patterns and variograms, derived from geological process simulations. The methodology involves: (1) application of the geological process modeling (Petrel-GPMTM) software to reproduce stratigraphic models of the shallow-marine to marginal-marine Hugin formation (2) define lithofacies distribution in GPM outputs by using the property calculator tool in PetrelTM. Resultant lithofacies trends and variograms are applied to constrain facies modeling. Data includes: seismic data and 24 complete suites of well logs. The Hugin formation consists of a complex mix of wave and riverine sediment deposits within a period of transgression of the Viking Graben. Twenty depositional models were reproduced using different geological process scenarios. GPM-based facies models show an improvement in lithofacies representation, evident in the geologically realistic distribution of lithofacies in inter-well volumes, leading to the conclusion that a robust stratigraphic model provides an important stratigraphic framework for modeling facies heterogeneities.

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/content/papers/10.3997/2214-4609.201902242
2019-09-02
2024-03-28
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References

  1. Bertoncello, A. Sun, T. Li, H. Mariethoz, G., and Caers, J.2013. Conditioning Surface-Based Geological Models to Well and Thickness Data. Math Geosci, 45, 873–893.
    [Google Scholar]
  2. Caers, J., and Zhang, T., 2004. Multiple-point geostatistics: A quantitative vehicle for integrating geologic analogs into multiple reservoir models, in Integration of Outcrop and Modern Analog Data in Reservoir Modeling, edited by GrammerM., HarrisP., and EberliG.AAPG Mem, 80, 383–394.
    [Google Scholar]
  3. Deutsch, C. & Journel, A.1999. GSLIB. Geostatistical software library and user’s guide. Geological magazine, 136(1), 83–108.
    [Google Scholar]
  4. Dubrule, O.1998. Geostatistics in Petroleum Geology. American Association of Petroleum Geologists Continuing Education Course Note Series #38. Oklahoma, U.S.A.
    [Google Scholar]
  5. Folkestad, A. Satur, N.2008. Regressive and transgressive cycles in a rift-basin: Depositional model and sedimentary partitioning of the Middle Jurassic Hugin Formation, Southern Viking Graben, North Sea. Sedimentary Geology, 207(1), 1–21.
    [Google Scholar]
  6. Grammar, G.M. Harris, P.M. and Eberli, G.P.2004. Integration of Outcrop and Modern Analogs in Reservoir Modeling. American Association of Petroleum Geologists Memoir, 80, 1–22.
    [Google Scholar]
  7. Guardiano, F. and Srivastava, R.M.1993. Multivariate Geostatistics: Beyond Bivariate Moments, Geostatistics-Troia, SoaresA. (ed), Kluwer Academic Publications, Dordrecht, 1, 133–144.
    [Google Scholar]
  8. Hu, L. and Chugunova, T.2008. Multiple-point geostatistics for modeling subsurface heterogeneity: A comprehensive review. Water resources research, 44, 1–14.
    [Google Scholar]
  9. Huang, X. Griffiths, C. and Liu, J.2015. Recent development in stratigraphic forward modeling and its application in petroleum exploration. Australian journal of Earth science, 62(8), 903–919.
    [Google Scholar]
  10. Kieft, R.L., Jackson, C.A.-L., Hampson, G.J., and Larsen, E.2011. Sedimentology and sequence stratigraphy of the Hugin Formation, Quadrant 15, Norwegian sector, South Viking Graben. Geology Society, London, Petroleum Geology Conference Series, 7, 157–176.
    [Google Scholar]
  11. Michael, H.A. Li, H. Boucher, A. Sun, T. Caers, J. and Gorelick, S.M.2010. Combining geologic-process models and geostatistics for conditional simulation of 3-D subsurface heterogeneity. Water Resource Research, 46(W05527), 1–20.
    [Google Scholar]
  12. Orellana, N. Cavero, J. Yemez, I. Singh, V. and Sotomayor, J.2014. Influence of variograms in 3D reservoir-modeling outcomes: An example. The leading edge, 33(8), 890–902.
    [Google Scholar]
  13. Pyrcz, M.J. Sech, R.P. Covault, J.A. Willis, B.J. Sylvester, Z. and Sun, T.2015. Stratigraphic rule-based reservoir modeling. Canadian petroleum geology, 63(4), 287–303.
    [Google Scholar]
  14. Sisinmi, V. McDougall, N. Victoria, M. Vallez, G. Y. and Estaba, V.V.2016. Facies modeling described by probabilistic patterns using Multi-point statistics an application to the k-field, Libya. American Association of Petroleum Geologists, Article #42045, 1–19.
    [Google Scholar]
  15. StrebelleS. Levy, M.2008. Using Multiple-Point Statistics to build geologically realistic reservoir models: the MPS/FDM workflow. Geological Society of London, Special Publications, 309, 67–74.
    [Google Scholar]
  16. Vollset, J. and Dore, A.G.1984. A revised Triassic and Jurassic lithostratigraphic nomenclature for the Norwegian North Sea. NPD Bulletin Oljedirektoratet, 3, 53.
    [Google Scholar]
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