stratigraphic realization

The stratigraphic realization module is one of three similar modules (the other two are analytical_realization and lithologic realization), which allows you to very quickly generate statistical realizations of your stratigraphic horizons based uponC Tech's Proprietary Extended Gaussian Geostatistical Simulation (GGS), which we refer to as Fast Geostatistical Realizations^© or FGR^©. Our extensions to GGS allow you to:

• Create realizations rapidly
• Exercise greater control over the frequency and magnitude of noise typical in GGS.
• Control deviation magnitudes from the nominal kriged prediction based on a Min Max Confidence Equivalent.
  • Deviations are the absolute value of the changes to surface elevations for each stratigraphic horizon.
• Apply Simple or Advanced Anisotropy control over 2D wavelengths
• For stratigraphic realizations only: we support Natural Neighbor as well as kriging for the input model.

Module Input Ports

• Realization [Special Field] Accepts outputs from gridding and horizons to allow for FGR^© models to be created

Module Output Ports

• Output Field [Field] Outputs the subsetting level
• Deviations Field [Field] Outputs the deviations from the nominal kriged model

Important Parameters

There are several parameters which affect the realizations. A brief description of each follows:

• Randomness Generator Type
  • There are four types, each of which create a different 2D/3D random distribution
• Anisotropy Mode
  • Two options here are Simple or Advanced. These are equivalent to the variogram settings in gridding and horizons
• Seed
  • The "Seed" is used in the random number generator, and makes it reproducible.
  • Unique seeds create unique realizations
• Wavelength
  • The 2D or 3D random distribution is governed by a mean wavelength that determines the apparent frequency of deviations from the nominal kriged (or Natural Neighbor) result.
  • Wavelength is in your project coordinates (e.g. meters or feet)
  • Longer wavelengths create smoother realizations
  • Shorter wavelengths create more "noisy" variations in the realizations
  • Very short wavelengths will give results more similar to GGS (aka Sequential Gaussian Simulations)
• Min Max Confidence Equivalent
  • This parameter determines the magnitude of the deviations.
  • Values close to 50% result in outputs that deviate very little from the nominal kriged (or Natural Neighbor) result.
    • (we do not allow values below 51% for algorithm stability reasons)
  • Values at or approaching 99.99% will result in the greatest (4 sigma) variations (more similar to GGS)