Thereafter, the entire seismic section was inverted to acoustic impedance section. The statistical analysis demonstrates good performance of the algorithm. The results depict that the inverted AI matches very well with the well log AI. The methods were first applied to the composite trace close to well locations and were inverted for acoustic impedance (AI). These methods are applied to the Blackfoot seismic reflection data to estimate reservoir.
Among many approaches that have been made to improve interpretation of post-stack seismic data, a great effort has been made to use maximum likelihood (ML), sparse spike inversion (SSI) along with multi-attribute analysis (MAA) aimed to increase the resolution power of interpreting seismic reflection data and mapping into the subsurface lithology. Seismic inversion involves extracting qualitative as well as quantitative information from seismic reflection data that can be analyzed to enhance geological and geophysical interpretation which is more subtle in a traditional seismic data interpretation.
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