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Integrating 3D Seismic Attributes and Post-stack Inversion Techniques: A Granite Wash Play Illustration


Seismic inversion and seismic attributes analysis have become increasingly important for static reservoir properties predictions. This work evaluates these methods through a case study on a complex-natured formation that has been inherently challenging to characterize due to limited seismic resolution – The Pennsylvanian Granite Wash, Anadarko Basin, Texas. The 3D seismic survey of the study area suffers migration aliasing and relatively low vertical resolution and was subsequently passed through structure-oriented filtering to improve the signal-to-noise ratio. Time-structure, thickness maps as well as co-rendered mixes of different attributes variants such as coherent energy, most positive and negative curvature and energy ratio similarity were computed from the post-stack seismic data to aid reservoir delineation. Acoustic impedance (AI) computed from the seismic amplitude volume integrated with sonic and density logs provided good images of reservoir heterogeneity. Moreover, by combining different geometric attributes with inverted AI, it is possible to build geomorphological model and also delineate lithological heterogeneity within the wash. The use of multi-attributes also made it possible to identify fan deposits in the areas. Based on this analysis, low acoustic impedance can be associated with either higher porosity or hydrocarbon vs water saturation. It is hoped that this research direction would extend the petro-physical and seismic expression of the granite wash.