Neural-network prediction of well-log data using seismic attributes is an important reservoir characterization technique because it allows extrapolation of log properties throughout a seismic volume. The strength of neural-networks in the area of pattern recognition is key in its success for delineating the complex nonlinear relationship between seismic attributes and log properties. We have found that good neural-network generalization of well-log properties can be accomplished using a small number of seismic attributes. This study presents a new method for seismic attribute selection using a genetic-algorithm approach. The genetic algorithm attribute selection uses neural-network training results to choose the optimal number and type of seismic attributes for porosity prediction.
We apply the genetic-algorithm attribute-selection method to the C38 reservoir in the Stratton field 3D seismic data set. Eleven wells with porosity logs are used to train a neural network using genetic-algorithm selected-attribute combinations. A histogram of 50 genetic-algorithm attribute selection runs indicates that amplitude-based attributes are the best porosity predictors for this data set. On average, the genetic algorithm selected four attributes for optimal porosity log prediction, although the number of attributes chosen ranged from one to nine. A predicted porosity volume was generated using the best genetic-algorithm attribute combination based on an average cross-validation correlation coefficient. This volume suggested a network of channel sands within the C38 reservoir.