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 |
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| Authors: | Yudhvir Singh Rajesh R Nair Harmandeep Singh Prattya Datta Priyank Jaiswal Pawan Dewangan T Ramaprasad |
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| Institution: | 1.Department of Ocean Engineering,IIT Madras,Chennai,India;2.Department of Geology and Geophysics,IIT Kharagpur,Kharagpur,India;3.Boone Pickens School of Geology, Noble Research Centre,Oklahoma State University,Stillwater,USA;4.National Institute of Oceanography,Panaji,India |
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| Abstract: | 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. |
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