Enhancement of fault interpretation using multi-attribute analysis and artificial neural network (ANN) approach: a case study from Taranaki Basin, New Zealand

Abstract

Enhanced seismic data conditioning and multi-attribute analysis through non-linear neural processing workflows has been applied to 3D seismic data over 215.10 km2 area of the Opunake prospect located in the south-eastern offshore Taranaki Basin. The present work aims to delineate faults and the related detail of structural features from the study area. Post-stack seismic data conditioning techniques such as dip-steering and structural filtering are applied to enhance the lateral continuity of seismic events and eliminate random noises from the data with the objective of improving the visibility of faults in the data volume. The conditioned data is then used to extract several attributes, such as similarity, dip variance, curvature, energy and frequency, that act as potential contributors for enhancing the fault visibility. A fully connected multilayer perceptron (MLP) network is developed to choose the proper combination of attributes for fault detection. These seismic attributes (known as the test datasets) are then trained at identified fault and non-fault locations using this network. The network comprises of 11, 7, and 2 nodes in the input layer, hidden layer and output layer, respectively. The neural training resulted in an overall minimum root mean square (RMS) misfit and misclassification (%) ranging from 0.54 to 0.67 and 18.67 to 10.42, respectively, between the trained and the test datasets. The neural training generates a fault probability attribute that produces an improved fault visibility capturing the minute details of the seismic volume as compared with the results of individual seismic attribute. Thus, the present work demonstrates an enhanced and robust workflow of fault prediction and visualisation for detail structural interpretation from 3D seismic data volume.