Pp. 306-313 B. Das, R. Chatterjee POROSITY MAPPING FROM INVERSION OF POST-STACK SEISMIC DATA B. Das, R. Chatterjee Indian Institute of Technology (Indian School of Mines), Dhanbad, India Abstract. <...> A seismic section oriented N-S passing through well “W” is considered for porosity prediction in offshore of Krishna-Godavari (K-G) basin. <...> Porosity is mapped from transformation of acoustic impedance (AI). <...> Post-stack inversion of seismic data is routinely carried out to derive AI and hence petrophysical properties in an area. <...> We have been introducing here an uncommon approach of inverting post-stack seismic data into porosity from porosity log. <...> The post-stack inversion for estimation of direct porosity is performed by utilizing an estimated porosity wavelet, low frequency model and model based inversion. <...> In K-G basin, the porosity in Raghavapuram Shale varies from 13 to 30 % with maximum value of 40 % is observed in Paleocene sediments. <...> The shales/unconsolidated sediments measure a high porosity with low impedance and the more porous sands are in an intermediate range. <...> The predicted impedance and porosity values may be erroneous beyond the drilled depth because of non availability of well log data for calibration. <...> Keywords: Krishna-Godavari basin, Porosity, Seismic Inversion, Raghavapuram Shale DOI: 10.18599/grs.18.4.8 For citation: Baisakhi Das, Rima Chatterjee Porosity Mapping from Inversion of Post-Stack Seismic Data. <...> Well information are available at hundreds of meters apart, therefore the objective of seismic inversion method for reservoir characterization is to delineate petrophysical properties for the interwell region or adjacent to the wells. <...> In contrast, seismic method provides usual areal sampling but with noticeably lower vertical resolution. <...> The petrophysical parameters are generally predicted from seismic 306 GEORESURSY inversion properties such as AI using multivariate statistics modelling, non-linear methods including neural network (e.g. Hampson et al., 2001; Leiphart, Hart, 2001;Walls et al., 2002; Pramanik et al., 2004; Calderon, 2007; Singha, Chatterjeee 2014; Singha et al., 2014). <...> Objectives of this paper are to (a) transformation of AI to porosity mapping, (b) development of relation between porosity and acoustic reflectivity from post <...>