Seismic-guided estimation of log properties (Part 2: Using artificial neural networks for nonlinear attribute calibration)

Abstract

We saw in Part 1 that when we have 3-D data together with a number of logged wells, we can look at possible relationships between some attributes of the seismic data and various properties measured on the logs. At multiple well locations, where we have both seismic and log data, we can look for trends in these two data types on crossplots. If we see a trend, we can quantify it with a derived or specified functional relationship. This functional relationship can be used to convert the attribute values to log properties, and when followed by a residual correction, provide a means to estimate the distribution of these properties away from the wells.