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dc.contributor.authorSingh, Y.
dc.contributor.authorNair, R.R.
dc.contributor.authorSingh, H.
dc.contributor.authorDatta, P.
dc.contributor.authorJaiswal, P.
dc.contributor.authorDewangan, P.
dc.contributor.authorRamprasad, T.
dc.date.accessioned2016-06-14T06:56:00Z
dc.date.available2016-06-14T06:56:00Z
dc.date.issued2016
dc.identifier.citationArabian Journal of Geosciences, vol.9(5); 2016; no.415 doi.:10.1007/s12517-016-2434-6
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/4977
dc.description.abstractStepwise 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
dc.language.isoen
dc.publisherSpringer
dc.rightsAn edited version of this paper was published by Springer. This paper is for R & D purpose and Copyright [2016] Springer.
dc.subjectGEOLOGY AND GEOPHYSICS
dc.subjectCHEMISTRY AND BIOGEOCHEMISTRY
dc.subjectAQUATIC RESOURCES
dc.subjectGEOLOGY AND GEOPHYSICS
dc.titlePrediction of gas hydrate saturation throughout the seismic section in Krishna Godavari basin using multivariate linear regression and multi-layer feed forward neural network approach
dc.typeJournal Article


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