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Article history Received: 23 June 2020 Accepted: 8 July 2020 Published Online: 30 July 2020 Seismic edge detection algorithm unmasks blurred discontinuity in an image and its efficiency is dependent on the precession of the processing scheme adopted. Data-driven modeling is a fast machine learning scheme and a formal automatic version of the empirical approach in existence for a long time and which can be used in many different contexts. Here, a desired algorithm that can identify masked connection and correlation from a set of observations is built and used. 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), etc and Cepstral decomposition techniques, i.e Complex Cepstral Transform (CCT), etc can be employed. Cepstral decomposition is a new approach that extends the widely used process of spectral decomposition which is rigorous when analyzing very subtle stratigraphic plays and fractured reservoirs. This paper presents the results of the application of DFT and CCT to a two dimensional, 50Hz low impedance Channel sand model, representing typical geologic environment around a prospective hydrocarbon zone largely trapped in various types of channel structures. 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 CCT represents the quefrency and saphe cepstra of a signal in quefrency domain. The transform filters the field data recorded in time domain, and recovers lost sub-seismic geologic information in quefrency domain by separating source and transmission path effects. Our algorithm is based on fast Fourier transform (FFT) techniques and the programming code was written within Matlab software. It was developed from first principles and outside oil industry’s interpretational platform using standard processing routines. The results of the algorithm, when implemented on both commercial and general platforms, were comparable. The cepstral properties of the channel model indicate that cepstral 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 sample results of spectral and cepstral attribute plots and pseudo-sections of phase and saphe constructed from the model data. The cepstral attributes reveal more details in terms of quefrency required for clearer imaging and better interpretation of subtle edges/discontinuities, sand-shale interbedding, differences in lithology. These positively impact on production as they serve as basis for the interpretation of similar geologic situations in field data.

Seismic Edge Detection by Application of Cepstral Decomposition to Data Driven Modeled Geologic Channel Feature in Niger Delta.