Abstract

Seismic attributes and supervised and unsupervised neural networks have become increasingly popular in recent years in the realm of quantitative interpretation for hydrocarbon exploration. The main emphasis is on the qualitative and quantitative measurement of stratigraphic and lithologic effects on seismic signatures. We used the concept of fractals for the identification of seismic reflectors with special emphasis to thin bed delineation, which are generally overlooked during standard data processing. A new fractal analysis scheme is applied on both the synthetic and real field seismic data. The fractal dimensions of the three seismic attributes, amplitude, phase, and instantaneous frequency have been analyzed. A change in fractal dimension is found to occur whenever there is a reflection. The resolution in the delineation of reflectors, however, varies depending on the attribute we are considering and the method of fractal dimension estimation we have used. The fractal analysis is performed on both the noise free and noisy synthetic data to establish the noise tolerance limit for both the