Keywords
Seismic Inversion
Model Based Inversion
Probabilistic Neural Network
Multi-Layer Feed Forward Network
How to Cite
Funding data
-
University Grants Commission
Grant numbers M-14-0585
Abstract
To estimate petrophysical parameters, seismic inversion techniques have been frequently used for estimating attributes like P-impedance, elastic impedance, S-impedance, density, Vp / Vs ratio, and gamma-ray logs from seismic and well-log data. These characteristics enable us to comprehend subsurface lithology for geo-seismic analysis, including its extent and shape. Four different post-stack inversion techniques, including bandlimited inversion (BLI), colored inversion (CI), maximum likelihood sparse spike inversion (MLSSI), and model-based inversion (MBI), have been applied to the post-stack seismic data from the F3 block in the Netherlands in this study. The objective is to compare the efficacy of these inversion methods over F3 block seismic data. For post-stack inversion, the data was inverted into a very high-resolution P-impedance volume. The analysis depicts that all four inversion methods provide mutually consistent subsurface information with marginally better MBI results. Furthermore, geostatistical techniques have been used intensively for further testifying the results obtained from post-stack inversion methods. The geostatistical techniques use seismic data-derived attributes and inverted impedance-derived attributes as input to estimate P-wave velocity, porosity, and density away from the boreholes. Two geostatistical methods namely probabilistic neural network (PNN) and multilayer feed-forward neural network (MLFN) are used for the analysis. Porosity, density, and P-wave velocity have been predicted using both techniques which highlighted different characteristics of the subsurface with very detailed information. The derived results show that reservoir properties have been better estimated with the combination of MBI and PNN techniques for F3 block data over the other methods used in this study.
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Copyright (c) 2023 Prabodh K. Kushwaha, Raghav Singh, Satya P. Maurya, Piyush Rai