Seismic inversion is a geophysical technique used to estimate subsurface acoustic impedance models from seismic reflection data. The basic concept behind seismic inversion is to establish mathematical assumptions that can relate seismic responses to geological formation properties. This study addresses two types of seismic inversion methods, namely Band-limited inversion (BLI) and Linear programming inversion (LPI) techniques for the delineation of gas reservoirs. The study presents the successful use of the inversion in a real example from a gas sand reservoir in KG-Basin, India. Inversion has led to the removal of ambiguity and revealing clear information about the target area. This work aims to use and compare the results of these two generic seismic post-stack inversion methods for characterizing the reservoir. The inversion reveals the presence of low acoustic impedance around 2320-2440 ms and is inferred as a sandstone layer with a potential hydrocarbon reservoir. One can improve the analysis of the elastic properties for reservoir characterization by using rock physics knowledge to understand better reservoir properties such as water saturation, porosity, and shale volume. This study also uses the Rock Physics Template (RPT) and plots it with elastic properties to aid the efficient interpretation. RPT helps to identify the lithology and fluid content in the area. Results from the seismic study suggest a comparatively better analysis done through the LPI technique over BLI. In the last, the Multi-attribute transform as a part of the Geostatistical method is used to predict porosity away from the borehole. The method shows a very high porosity section and clearly shows the distribution of porosity in and around the region. The interpretation shows that a very high porosity zone (15–25%) is present that is in cross-correlation with a low impedance zone (4000-8000 m/s*g/cc) and confirms the presence of a hydrocarbon zone in-between 2320 and 2440 ms two-way travel time. All seismic inversion and geostatistical methods can retrieve subsurface information with very high-resolution images but the analysis based on linear programming is found slightly better in each step and depicts good prediction results. The study finally gives a flowchart that combines seismic inversion and geostatistical methods to extract petrophysical parameters from seismic data and can help to interpret hydrocarbon-bearing formation in any virgin area.
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