Direct fracture detection using surface seismic data is challenging due to seismic resolution, signal-to-noise ratio, structural setting and rock properties among many others causes. The ability to detect sub-seismic faults and fracture rich zones on the seismic image requires advanced workflows. The result of being able to identify small fault and fractures enables optimal well path designs and de-risking of target locations.

This presentation will demonstrate a multi-attribute workflow which includes volumetric curvature parameter optimization, directional GLCM (gray-level co-occurrence matrix) based attributes, and post processing enhancement with 3D Log-Gabor filtering. Comparison between volumetric curvature and texture classification based on GLCM is carried out in order to get an estimation of fracture corridor orientations. An array of sectors response from GLCM-based attributes can be calculated in different directions by inspecting and comparing these different sectors. Areas with clear tendency to directional variations might be associated with fractured zones, changes in lithology, or seismic facies changes.

Based on these analyzes it was possible to identify highly fractured zones congruous with the geological model. The main trend shows parallelism with the main faults and a secondary is interpreted as a direct expression of the salt influence. These observations are consistent with modeled anticline generated by salt tectonics and limited by main sealing faults.

This methodology was used to predict the fractured zones on top of the Cretaceous brecciated carbonates in the Gulf of Mexico that are the most important reservoirs in the Salinas Basin.


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