In recent years, 3D volumetric attributes have gained wide acceptance by seismic interpreters. The early introduction of single-trace complex trace attributes was quickly followed by seismic sequence attribute mapping workflows. 3D geometric attributes such as coherence and curvature are also widely used. Most of these attributes correspond to very simple, easy-to-understand measures of a waveform or surface morphology. However, not all geologic features can be so easily quantified. For this reason, simple statistical measures of the seismic waveform such as RMS amplitude and texture analysis techniques prove to be quite valuable in delineating more chaotic stratigraphy. In this paper, we coupled structure-oriented texture analysis based on the gray-level co-occurrence matrix with self-organizing maps clustering technology and applied it to classify seismic textures. By this way, we expect our workflow should be more sensitive to lateral changes rather than vertical changes in reflectivity. We applied the methodology to a 3D seismic survey acquired over OsageCo., OK, USA. and our results indicate that our method can be used to delineate the meandering channels as well as to characterize chert reservoirs.


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