Mathematical Geosciences

, 43:763 | Cite as

A Stochastic Object Model Conditioned to High-Quality Seismic Data

  • Anne Randi SyversveenEmail author
  • Ragnar Hauge
  • Jan Inge Tollefsrud
  • Ulf Lægreid
  • Alister MacDonald
Open Access


We present an approach for modeling facies bodies in which a highly constrained stochastic object model is used to integrate detailed seismic interpretation of the reservoir’s sedimentological architecture directly in a three-dimensional reservoir model. The approach fills the gap between the use of seismic data in a true deterministic sense, in which the facies body top and base are resolved and mapped directly, and stochastic methods in which the relationship between seismic attributes and facies is defined by conditional probabilities. The lateral geometry of the facies bodies is controlled by seismic interpretations on horizon slices or by direct body extraction, whereas facies body thickness and cross-sectional shape are defined by a mixture of seismic data, well data, and user defined object shapes. The stochastic terms in the model are used to incorporate local geometric variability, which is used to increase the geological realism of the facies bodies and allow for correct, flexible well conditioning. The result is a set of three-dimensional facies bodies that are constrained to the seismic interpretations and well data. Each body is defined as a parametric object that includes information such as location of the body axis, depositional direction, axis-to-margin normals, and external body geometry. The parametric information is useful for defining geologically realistic intrabody petrophysical trends and for controlling connectivity between stacked facies bodies.


Facies modeling Seismic data Intrabody petrophysical trends 


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Copyright information

© The Author(s) 2011

Authors and Affiliations

  • Anne Randi Syversveen
    • 1
    Email author
  • Ragnar Hauge
    • 1
  • Jan Inge Tollefsrud
    • 2
  • Ulf Lægreid
    • 3
  • Alister MacDonald
    • 4
  1. 1.Norwegian Computing CenterBlindern, OsloNorway
  2. 2.StatoilOsloNorway
  3. 3.Bayerngas Norway ASOsloNorway
  4. 4.Roxar Software Solutions ASLysakerNorway

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