Hydrocarbon-charged sediments detection and characterization are the main concern of petroleum explorers and producers. Over the past decades, the Amplitude Variation with Offset (AVO) technique had proved to be a very successful tool in hydrocarbon identification and recognition. AVO attributes have been used particularly to extract information about the lithology (sand, shale, etc.) and fluid content of the subsurface. It unraveled successful discoveries in many sedimentary basins around the globe. The success was documented mainly in the West Delta deep marine and analogous shallow unconsolidated rocks of Pliocene age worldwide. However, due to the fact that most of the hydrocarbon potential reserves in those basins and in easily explored areas have been discovered, the attention of the oil industry has been directed to enhance recovery from producing formations and exploring more difficult and complex environments (e.g. deep offshore, deep closely stacked thin bedding reservoirs, thin narrow and meandering reservoir channels, etc.). Thus, AVO attributes are insufficient for achieving the goals reached in earlier time. With the advent of seismic attributes technology, innovative seismic attributes appeared and proved to have potential in achieving information which used to be extracted using AVO. Of these innovative attributes, Spectral Decomposition (SD) proved to be a powerful tool in revealing subtle details that aseismic broadband may burry. Over the last decades, numerous published works have discussed how this attribute can be used to differentiate both lateral and vertical lithologic and pore-fluid changes; as well as delineating stratigraphic traps and identifying subtle frequency variations caused by hydrocarbons from world different environments and geological settings. The first section of this chapter focuses principally on the definition of seismic attributes and their applications in reservoir studies from the literature.


The objective of this paper is to develop a framework under which we can improve the clastic reservoir characterization by using the pre-stack inversion and the neural-network analysis. The aim is to go beyond the limitations of full-stack seismic data and reduce the uncertainty as much as possible.

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