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In the petroleum industry, the seismic inversion techniques have been widely used as a tool to locate hydrocarbon-bearing strata in the subsurface from the seismic data (Bosch et al. Seismic inversion is a procedure that helps extract the high-resolution subsurface model of the physical characteristics of rocks and fluids from low-resolution seismic reflection data with the integration of well log data (Krebs et al. The inverted porosity section shows a high porosity anomaly and a low density anomaly in between 10 ms time intervals which corroborated well with the low impedance zone and confirm the presence of a reservoir. Further, the multi-attribute analysis is performed to estimate porosity and density in the inter-well region. The analysis of the inverted impedance section shows an anomaly zone in between 10 ms time and characterize it as reservoir. Thereafter, the entire seismic section was inverted to acoustic impedance section.
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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. A real data case example shows that the inverted fluid factor, shear modulus and density fit nicely with well log interpretation results, which verifies the effectiveness of the proposed method.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. We obtain a reasonable fluid factor, shear modulus and density even with smooth initial models and moderate Gaussian noise. Finally, a model test shows the superiority of this FMR-AVA inversion method in stability and independence of initial models. Furthermore, it can be applied in heterogeneous reservoirs whose initial models for inversion are not easy to establish. This approach has little dependence on initial models. Secondly, a stable simultaneous AVA inversion approach is presented in a Bayesian scheme. Firstly, an FMR approximation equation of a reflection coefficient is derived based on poroelasticity with P- and S-wave moduli. This method can be used for direct inversion for the fluid factor, shear modulus and density of heterogeneous reservoirs. In this paper, a simultaneous inversion method named FMR-AVA (Fluid Factor, Mu (Shear modulus), Rho (Density)-Amplitude Variation with Angle) is proposed based on partial angle stack seismic gathers. Prestack seismic inversion plays an important role in estimating elastic parameters that are sensitive to reservoirs and fluid underground.