Abstract:
A number of interpolation techniques exist that are designed to interpolate data that is sparse in nature over a field to the entire range. These include schemes such as Inverse-Distance Weighting (IDW), Kriging and others. The limitation to these schemes is that they depend only on the location and value of the sparse points.
In some applications, however, the need for a guiding function that is dense in nature is felt. One application, which happens to be the one we are working on, is the guidance of well data (sparse) by seismic date (dense). This is needed because the location of wells alone is insufficient to determine the orientation of layers – the collected seismic data is needed to be able to “guide” the prediction of the layers. As a result, a new technique is needed to be able to meet these requirements.
We propose a technique known as Image Guided Blended Near-Neighbour Interpolation, and aim to define it mathematically, as well as to introduce an algorithm for its fast computation. The properties of well data that can be interpolated include velocity, density, temperature, viscosity and others.
This technique would not only have a computational advantage over existing methods but it would also lead to a reduction in time and cost giving it an edge over all previously known techniques.
