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Prediction of gas hydrate saturation throughout the seismic section in Krishna Godavari basin using multivariate linear regression and multi-layer feed forward neural network approach

DRS at CSIR-National Institute of Oceanography

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Title Prediction of gas hydrate saturation throughout the seismic section in Krishna Godavari basin using multivariate linear regression and multi-layer feed forward neural network approach
 
Creator Singh, Y.
Nair, R.R.
Singh, H.
Datta, P.
Jaiswal, P.
Dewangan, P.
Ramprasad, T.
 
Subject GEOLOGY AND GEOPHYSICS
CHEMISTRY AND BIOGEOCHEMISTRY
AQUATIC RESOURCES
GEOLOGY AND GEOPHYSICS
 
Description Stepwise linear regression, multi-layer feed forward neural (MLFN) network method was used to predict the 2D distribution of P-wave velocity, resistivity, porosity, and gas hydrate saturation throughout seismic section NGHP-01 in the Krishna-Godavari basin. Log prediction process, with uncertainties based on root mean square error properties, was implemented by way of a multi-layer feed forward neural network. The log properties were merged with seismic data by applying a non-linear transform to the seismic attributes. Gas hydrate saturation estimates show an average saturation of 41 % between common depth point (CDP) 600 and 700 and an average saturation of 35 % for CDP 300-400 and 700-800, respectively. High gas hydrate saturation corresponds to high P-wave velocity and high resistivity except in a few spots, which could be due to local variation of permeability, temperature, fractures, etc
 
Date 2016-06-14T06:56:00Z
2016-06-14T06:56:00Z
2016
 
Type Journal Article
 
Identifier Arabian Journal of Geosciences, vol.9(5); 2016; no.415 doi.:10.1007/s12517-016-2434-6
http://drs.nio.org/drs/handle/2264/4977
 
Language en
 
Rights An edited version of this paper was published by Springer. This paper is for R & D purpose and Copyright [2016] Springer.
 
Publisher Springer