SEISMIC DATA ANALYSIS WITH MATCHING PURSUIT DECOMPOSITION

PROCEEDINGS JOINT CONVENTION BALI 2007
The 32^nd HAGI, The 36^th IAGI, and The 29^th IATMI Annual Conference and Exhibition

SEISMIC DATA ANALYSIS WITH MATCHING PURSUIT
DECOMPOSITION

Fariz Febianto¹⁾, Leonard Lisapaly²⁾

¹⁾ Geophysics Program Study, Physics Department, University of Indonesia, ²⁾ Reservoir Geophysics
Section, Physics Graduate Program, University of Indonesia

ABSTRACT

Many methods have been applied to seismic data in the reservoir characterization. Spectral decomposition has been applied to 3D seismic data interpretation include enhanced resolution, improved visualization of stratigraphic features, thickness estimation for thin beds, noise suppression, and direct hydrocarbon indication. There are a variety of spectral decomposition methods. These include the DFT (dicrete Fourier transform), MEM (maximum entropy method), CWT (continuous wavelet transform), and MPD (matching pursuit decomposition).

Matching pursuit decomposition involves cross-correlation of a wavelet dictionary against the seismic trace. The projection of the best correlating wavelet on the seismic trace is then subtracted from that trace. The wavelet dictionary is then cross-correlated against the residual, and again the best correlating wavelet projection is subtracted. The process is repeated iteratively until the energy left in the residual falls below some acceptable threshold. The output of the process is a list of wavelets with their respective arrival times and amplitudes for each seismic trace. The results of spectral decomposition with MPD method can show distributary channel in Caddo formation more clearly and have good resolution compared DFT method.