Prediction of Petrophysical Properties Through Comparative Post-Stack Inversion Techniques Using Advance Neural Networking

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The sophisticated seismic inversion methodology develops the relationship between the interpreted seismic data and the elastic properties to discerning the desired facies in the reservoir characterization. The study is focused on the C-interval sand of the Lower Goru Formation by utilizing Post-stack Time Migration (PSTM) 3D seismic and borehole logs of six wells. The challenges in the facies discrimination arise due to various complexities, i.e., lithological variability and acquisition sensitivity of recording tools. Various quantitative interpretation techniques such as Band limited inversion, Model-Based inversion, and stochastic inversion are developed to handle the limitations effectively and precisely access the producing facies. A comparative analysis is performed for the heterogeneous sands by employing these QI techniques and evaluating their effectiveness. The inverted seismic attributes are further utilized in the petrophysical properties estimation through Probabilistic Neural Networking (PNN) algorithm to distinguish litho-facies by developing a petro-elastic relationship. Probabilistic Neural Networking works best for the heterogeneous reservoir’s petrophysical properties estimation, especially sand reservoirs with shales intercalation. The concluding results declare the efficiency of all applied techniques for potential sand zones, especially the stochastic inversion. The petrophysical properties volume of clay and effective porosity, estimated from the stochastic inversion attribute, matched with the results of the blind well, resolute precisely the reservoir potential, and delineated the depositional environment of the three segregated sand bodies.