| Abstract: | One of the first stages of the three‐dimensional (3D) subsurface modeling process involves collation and analysis of available borehole and/or outcrop data to identify individual subsurface units, usually distinguished by the grain size of the sediment, and the elevation of their bounding contacts. Input data can come from a variety of sources and may be categorized according to their reliability and/or quality. The output from the 3D model is a prediction of subsurface conditions based on these data and the reliability of the output model is highly dependent on both the quality of input data and the types of interpolation methods used. This article presents a new quality weighting methodology that allows the user to assign a differential weighting factor to data points of variable quality in the modeling process. Input data are categorized into high and low quality datasets which are then recombined using a grid math process in which a differential “weighting” factor is applied. This allows the 3D modeling program to maximize the use and effectiveness of data from all available sources while giving high quality data greater influence on the final model output, and will result in the generation of more accurate and reliable 3D subsurface models. |
