Using Wavelet Transform and Self Organizing Maps for Seismic Reservoir Characterization of a Deep-water Field, Campos Basin, Offshore Brazil

Abstract

Seismic facies analysis is not a deterministic and simple task. Usually, facies analysis is performed through the following steps [1]: 1. Geological oriented spatial and temporal segmentation of seismic traces (input space); 2. Seismic attributes selection (variable space); 3. Choosing the optimal number of classes (facies) and algorithm iterations; 4. Training and classification of the selected attributes using some statistical or neural networks methods (pattern space); 5. Building and interpreting facies map. Normally, the geological oriented spatial and temporal segmentation should be carefully done, because any horizon interpretation error could lead to wrong or very noisy facies results. The attributes selection is another complex task, because they should be physically consistent and statistically independent. It’s common to use the whole seismic trace amplitudes around the region of interest [2]. Nowadays, Self Organizing Maps (SOM), or Kohonen Maps [3], has become one of the most popular tools to build seismic facies maps [4]. But, it’s still empirical how to choose the number of classes and the best seismic attributes to discriminate geological features from seismic data. This project presents a new alternative to extract seismic pattern attributes and a new methodology to build seismic facies maps. We propose using Wavelet Transform to identify singularities in each geological oriented segment of the temporal seismic trace and then using SOM in a two-level approach [5]. To illustrate this technique it was applied to real data from a deep-water field in the Campos Basin, Brazil.