To identify a thin tight carbonate reservoir facies is one of the most challenging exploration task due to its spatial variation in terms of depostional settings, tectonics and diagenesis. The gross depositional environment plays a crucial role for insitu carbonate reservoir facies. However, the reservoir facies preservation depends on subsequent carbonate diagenesis. Though, the degree of diagenesis sometimes enhances the porosity but occasionally it ceases the porosity. However, the usage of the conventional seismic data analysis as well as state of the art tools like quantitative seismic inversion based reservoir characterization, geostatistical approach of waveform classification and the advent of the new machine learning tools like probabilistic fault likelihood, thin likelihood abetted to encompass the spatial variation, to identify the presence and hetrogeneity of the reservoir facies. Here, we have utilised these seismic tools through an integrated approach with other geological, geophysical and drilling data for futher hydrocarbon exploration and delineation.


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