Inverse Distance Weighting

The inverse distance weighting gridding method is a weighted average interpolator that can be exact or smoothing. Data points are weighted through the use of a weighting power that determines how weighting factors decrease as the distance from a grid node increases.

The larger the weighting power, the less effect data points far from a grid node have during interpolation. As the weighting power approaches 5, the grid node values approach the value of the nearest data point. Thus, large weighting powers result in grids that closely honor your input data.

As a result, they can also produce isolated closures with concentric contours (bull’s eyes). A smoothing factor can be added to lessen this effect but data will not be as closely honored.

Inverse Distance Weighting Parameters – Set the power and smoothing parameters.
Input Data Restriction – Restrict the input data to positive or negative values, and minimum and/or maximum values.
Duplicated Data Treatment - Specify how to grid an area where two or more input data points fall at the same location.
Empirical Covariance – An expression of how data values vary with distance.
Data Weighting - Give differing weights to Control Points, 2D data, 3D data and or wells.
Data Search Option – Select to search for data points based on proximity to the grid node or to use a directional bias. (Not an option for Minimum Curvature.)
Data Outside Gridding Area – Select to include data values outside your area of interest in the gridding calculation.
Data Output Option – Limit the range of output grid node values.
Apply to current horizon only - Allows you to apply changes to only the current horizon, or to all horizons being gridded.
Undo changes - Reverts back to default settings.
Cancel - Closes the window without applying any changes.

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