El Feel reservoir is characterized by high impedance fluvio-glacial Ordovician sandstone sealed by soft shales. Recognition of seismic responses at Top reservoir cannot provide information on reservoir quality due to simultaneous changes of reservoir and sealing acoustic properties. Acoustic inversion, integrating well and seismic data was therefore a crucial step to derive a 3D seismic reservoir characterization and a robust prior model is required. A limited number of the utilized wells are vertical and the rest are mostly slanted (borehole dips >40°), therefore acquired velocity measurements are mostly biased by shale anisotropy. Also input seismic data fidelity is an issue; it was managed by adopting CRS-stack volume, superior for interpretative and quantitative uses. A geostatistical-based proprietary methodology was developed to integrate seismic and well impedance trends, horizons interpretation, and conditioning wells. Thomsen’s anisotropy parameters estimates from well data were used to reduce velocity measures to vertical wells conditions. The followed approach improved data reliability for Petro-Acoustic analysis, well-seismic tie, prior model generation, and inversion. Impedance-Porosity analyses suggested inversion results calibration feasibility by linear or non-linear (Neural Network) approaches. Output Porosity cube, useful for reservoir modelling, captured sedimentary features highlighted by visualization techniques.


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