斯伦贝谢petrel教程2

Sequential Gaussian Simulation
Principles
Distribution ― From Input data
– Distribution is according to input data
― Normal distribution
– Use if there is no input data for unconditional simulation
Artificial Algorithms
Principles
Fractal
Crates a surface with z-values between min and max
― Exponent
– Number of fractal rows and colums between 2 and 10. A higher number-> more detail
– Using the same seed number generates the same results
– If not set by the user it will be randomly generated
Sequential Gaussian Simulation
Influence of Variogram
Overview
This function uses the closest input point for the created surface. Fast way of getting an idea of the influence area of the data points
Artificial Algorithms
Functional Interpolation
Summary
Strengths: May be used to extract a trend from the data Medium fast
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Weaknesses:
May fail if few data points <10
Only recommended if more than 20 datapoints are present as input
– Uses a mean and std. dev. defined by the user or estimated.
Sequential Gaussian Simulation
Principles
Other ― Seed
– Defines the start for the random number generation in the algorithm
― Hurst factor
– Degree of variance in small scale between 0.3 and 3.0. A higher number gives a smoother surface
Sequential Gaussian Simulation
Overview
The Sequential Gaussian Simulation is a stochastic method of interpolation based on Kriging, it can honor input data, input distributions, variograms and trends.
Weaknesses:
Produces a ”fuzzy” picture and may need smoothing
Not to be used for ordinary input like seismic interpretation e.g.
Assign to closest point
Major = minor Orientation = 0
Major = 5000 Minor = 1500 Orientation = 38
Sequential Gaussian Simulation
Influence of distribution
Normal distribution Mean = - 3180 Std = 10
Sequential Gaussian Simulation
Principles
Variogram ― Type
– Exponential – Spherical – Gaussian
― Sill ― Nugget
Anisotropy range and orientation ― Range ― Azimuth
Overview
Five different methods, with different interpolation settings, can be used to create an artificial surface. These are: ― Constant value ― Fractal ― Plane ― Areas ― Channels
By creating multiple realizations, SGS can be used to gain an understanding of uncertainty
The algorithm uses GSLIB directly (Geostatistical Software Library)
Sequential Gaussian Simulation
Principles
Output data range ― The expected data output range of the result ― Min/Max
―Relative ―Absolute
― If no input data is present, using relative % will fail
Normal distribution Mean = - 3180 Std = 71
Sequential Gaussian Simulation
Summary
Strengths:
Suitable to make a stochastic surface for uncertainty modeling