The Application of Complex Seismic Attributes in Thin Bed Reservoir Analysis

Abstract

Geologic models of hydrocarbon reservoirs facilitate enhanced visualization, volumetric calculation, well planning and prediction of migration path for fluid. In order to obtain new insights and test the mappability of a geologic feature, spectral decomposition techniques i.e. Discrete Fourier Transform (DFT), Short-Time Fourier Transform (STFT), Maximum Entropy, Hilbert Transform (HT), etc. can be employed. This paper presents the results of the application of DFT and HT to a two dimensional, 50Hz low impedance wedge model, representing typical geologic environment around a prospective hydrocarbon zone. While the DFT represents the frequency and phase spectra of a signal, assumes stationarity and highlights the average properties of its dominant portion, assuming analytical, the HT computes the imaginary part and assumes non-stationarity of the signal. Our algorithm is based on fast Fourier convolution technique. It was developed from first principles and outside oil industry’s interpretational platform using standard processing routines such as Matlab, Gnuplot, programs. The results of the algorithm, when implemented on both oil industry (e.g. Kingdom Suite) and general platforms, were comparable. The spectral properties of the wedge model indicate that complex attributes can be utilized as powerful tool in exploration problems to enhance visualization of small scale anomalies and obtain reliable estimates of wavelet and stratigraphic parameters. The practical relevance of this investigation is illustrated by means of cross-sections and maps constructed from the model data. These provide enhanced images of the subtle features of the model and serve as basis for the interpretation of similar geologic situations in field data.

Keywords: Discrete Fourier transform, Hilbert transform, Maximum Entropy, Short time Fourier transform, Spectral decomposition