There are several gridding methods offered in RockWorks for interpolation of your data into a regularly-spaced grid model. Each operates differently, and each has strengths and weaknesses. Click on a link below for more information.
Closest Point: Each grid node is simply assigned the value of the closest control point.
Cumulative: Assigns grid node values by adding the z-values for all control points that reside with the corresponding grid cell.
Dip: Biases the surface modeling based on the dip-direction and dip-angle of the control points.
Directional: Uses Inverse-Distance with a directional weighting bias.
Distance to Point: Each grid node is assigned a value that represents its distance, in your X,Y map units, to the closest control point.
Inverse-Distance: A common method using a weighted average approach to compute node values.
Kriging: Its strength is in identifying patterns across the data, including directional trends.
Plane: Uses a "best fit" algorithm to fit a flat plane to the data. It is primarily designed for modeling potentiometric surfaces based on three points/wells. There are no options to define.
Sample Density: Grid nodes represent the counted number of control points within the corresponding grid cell.
Trend Polynomial: It finds regional trends in your data.
Trend Residuals: It determines local differences from regional trends.
Triangulation: It uses a network of triangles to determine grid node values.
Hybrid: It allows you to use a combination of gridding methods and apply a weighting factor to each.