![]() ![]() ![]() Comparatively, the PNN analysis predicted the targeted property more efficiently and applied its estimations on entire seismic volume. To further characterize the reservoir, geostatistical techniques comprising multiattribute regression and probabilistic neural network (PNN) analysis are applied to predict the effective porosity of reservoir. Inversion results indicated that the relatively lower P-impedance values are encountered along the predicted sand channel. The sparse-spike inversion analysis has efficiently captured the variations in reservoir parameter (P-impedance) for gas prospect. The hydrocarbon bearing zone is well identified through the seismic attribute analysis along a sand channel. This study aims to delineate and characterize the gas saturated zone within the reservoir (Cretaceous C-sand) interval of Sawan gas field, Middle Indus Basin, Pakistan. The integrated study of seismic attributes and inversion analysis can provide a better understanding for predicting the hydrocarbon-bearing zones even in extreme heterogeneous reservoirs. ![]()
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