The Application of Generalized Hilbert Transform for Faults and Stratigraphic Features in Niger Delta

Abstract

Faults are critical to the accumulation of hydrocarbon and manifest themselves as abrupt, gradual or gentle changes of seismic amplitude. However an important element of entrapment is the presence of numerous subtle faults whose identification with computer–based algorithm poses a major challenge. Traditionally, edge detection techniques such as coherence, semblance, Hilbert transform (HT), etc are employed in evaluating faulted hydrocarbon prospects by examining trace to trace similarity in data and to unmask subtle events. However, these have high sensitivity to noise and suffer from computational truncation, and are therefore unreliable. This study focuses on the application of generalized Hilbert transform (GHT) of seismic amplitude data in the interpretation of 3D seismic data from the Niger Delta. The GHT is a windowed conventional Hilbert transform. It extends the HT by introducing a window and an order. HT is of order one, and one of the possible orders of implementation of the GHT. The GHT is less sensitive to noise and gives better resolution of subtle features than conventional edge detection techniques. The algorithm adopted is based on fast Fourier transform techniques and was developed from basics and outside oil-industry interpretational platforms using standard processing routines. Preliminary results of the algorithm, when implemented on both oil-industry and general interpretational platforms gave convincing images. The generalized Hilbert transform of the thin bed reservoir revealed subtle faults and provided an enhanced level of evaluating a prospect. This is capable of improving reservoir production and performance.

Keywords: Fourier, Hilbert transform, Spectral decomposition