Publications

Interpretation of seismic data in hydrocarbon exploration often starts with the recognition of patterns, which are important for characterizing subsurface geological features. One method which used in pattern recognition is called Gray Level Co- Occurrence Matrix (GLCM). Early applications of GLCM were for non-seismic purposes, such as remote sensing (Haralick et al., 1973). This method is used to map terrain, vegetation, and the use of land. Its use in seismic interpreation began in late 1990, when this technique was applied in seismic facies analysis (Gao, 2001, 2003, West et al., 2002, Chopra and Alexeev, 2005, Chopra and Marfurt, 2005). They demonstrated the advantages of this method compared to the use of conventional seismic attributes in seismic facies delineation. In order to be able to delineate the pattern of a target area, a good attribute has to be insensitive to local amplitude anomaly or distortion. But in real conditions, the amplitudes of seismic reflections very much depend on various factors such as lithology, thickness, fluid content, porosity, and noise. These elements cause difficulty in doing a direct interpretation from seismic reflection amplitude. Here, GLCM will be applied to the seismic data by using a mathematical approach.