Attribute amalgamation-aiding interpretation of faults from seismic data: An example from Waitara 3D prospect in Taranaki basin off New Zealand

A suitable combination of seismic attributes amalgamated with interpreter's acquaintances results into a meta-attribute that augments interpretation of geological discontinuities from seismic data. Application to time migrated 3D seismic data, acquired over the Waitara prospect covering an area of ~122 km2 in the Taranaki basin off New Zealand, clearly demonstrates this fact. We condition the seismic data to make the geologic structures free from noise, and then use it to define a set of attributes grouped into three different cases e.g., Case I, II and III. Finally, the attribute sets are trained over example locations selected from the data volume through a fully connected multilayer perceptron (MLP) based on artificial neural network (ANN) to generate a meta-attribute called as fault cube (FC) for each case. It is observed that the FCs obtained from these cases efficiently illuminate geological discontinuities. However, the FC, obtained from Case III attribute amalgamation, brings out the thinned and sharpened fault images from seismic data. This demonstrates the efficacy of amalgamation of suitable attributes for an efficient interpretation of geologic structures from seismic data.