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

被引:4
|
作者
Singh, Yudhvir [1 ]
Nair, Rajesh R. [1 ]
Singh, Harmandeep [2 ]
Datta, Prattya [1 ]
Jaiswal, Priyank [3 ]
Dewangan, Pawan [4 ]
Ramaprasad, T. [4 ]
机构
[1] IIT Madras, Dept Ocean Engn, Chennai 600036, Tamil Nadu, India
[2] IIT Kharagpur, Dept Geol & Geophys, Kharagpur, W Bengal, India
[3] Oklahoma State Univ, Noble Res Ctr, Boone Pickens Sch Geol, Stillwater, OK 74078 USA
[4] Natl Inst Oceanog, Panaji 403004, Goa, India
关键词
Gas hydrate saturation; Multivariate linear regression; Multi-layer feed forward neural network; ELASTIC PROPERTIES; INVERSION; SEDIMENTS; IMPEDANCE; MARGIN; SITES;
D O I
10.1007/s12517-016-2434-6
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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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页数:10
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