1887

Abstract

Summary

We show that object models are able to handle real world data complexity by applying a recently published object model to a North Sea reservoir. The reservoir used is the Statfjord formation of Gullfaks Sør, which has rich alluvial-fluvial sandstone deposits. For this reservoir, object modelling of channel objects and crevasse splays is preferred as it provides better geometric control of the channels and crevasses than indicator/data-driven models. However, earlier object models have had problems with conditioning to the amount of well data here. With this new approach, we can condition perfectly on well data, while also reducing the run time compared to previous models. The article addresses the improvements of the well conditioning which is central as it enhances the possibilities of doing automatic modelling of multiple realizations without any subjective modifications by the field geologists around wells. The improvements implicate that the necessary manual time for the geologists to create a good model can be reduced, which again implicates both cost-saving and a more robust automatable model. Our results demonstrate that object models have a vital role to play even in the current data-driven market of our industry.

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/content/papers/10.3997/2214-4609.201902241
2019-09-02
2024-04-26
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References

  1. Hauge, R., Vigsnes, M., Fjellvoll, B., Vevle, M., and Skorstad, A. [2017] Object-Based Modeling with Dense Well Data. Quantitative Geology and Geostatistics.Springer, Valencia, 557–573.
    [Google Scholar]
  2. Keogh, K. J., Leary, S., Martinius, A.W., Scott, A. S. J., Riordan, S., Viste, I., Gowland, S., Taylor, A.M. and Howell, J. [2014] Data capture for multiscale modelling of the Lourinhã formation, Lusitanian Basin, Portugal: an outcrop analogue for the Statfjord Group, Norwegian North Sea. Geological Society, London, Special Publications, 348, 25–56.
    [Google Scholar]
  3. Keogh, K.J., Martinius, A.W. and Osland, R. [2007] The development of fluvial stochastic modelling in the Norwegian oil industry: A historical review, subsurface implementation and future directions. Sedimentary Geology, 202, 249–268.
    [Google Scholar]
  4. Ryseth, A. [2001] Sedimentology and paleogeography of the Statfjord Formation (Rhaetian-Sinemurian), North Sea. Norwegian Petroleum Society Special Publications, 10, 67–85.
    [Google Scholar]
  5. Syversveen, A., Hauge, R., Tollefsrud, J., Lœgreid, U., & MacDonald, A. [2011] A Stochastic Object Model Conditioned to High-Quality Seismic Data. Mathematical Geosciences, 43, 763–781.
    [Google Scholar]
  6. Vollset, J. and Doré, A.G., [1984] A revised Triassic and Jurassic lithostratigraphic nomenclature for the Norwegian North Sea. Nor. Pet. Direct. Bull., 3, 1–53.
    [Google Scholar]
  7. Zachariassen, E., Skjervheim, J.-A., Vabo, J., Lunt, I., Hove, J. and Evensen, G., [2011] Integrated workflow for model update using geophysical monitoring data. Proceedings of the 73rd EAGE Conference & Exhibition, Vienna, Austria, 23-26 May.
    [Google Scholar]
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