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

Abstract

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 CCTrepresents the quefrency and saphe cepstra of a signal in quefrency domain.The transform filters the field data recorded in time domain, and recoverslost sub-seismic geologic information in quefrency domain by separatingsource 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 andgeneral platforms, were comparable. The cepstral properties of the channelmodel indicate that cepstral attributes can be utilized as powerful tool inexploration problems to enhance visualization of small scale anomaliesand obtain reliable estimates of wavelet and stratigraphic parameters. Thepractical relevance of this investigation is illustrated by means of sampleresults of spectral and cepstral attribute plots and pseudo-sections of phaseand saphe constructed from the model data. The cepstral attributes revealmore details in terms of quefrency required for clearer imaging and betterinterpretation of subtle edges/discontinuities, sand-shale interbedding, differences in lithology. These positively impact on production as they serveas basis for the interpretation of similar geologic situations in field data.