Application of LP and ML sparse spike inversion with probabilistic neural network to classify reservoir facies distribution - A case study from the Blackfoot field, Canada-Reference-Cited by-同舟云学术

Application of LP and ML sparse spike inversion with probabilistic neural network to classify reservoir facies distribution - A case study from the Blackfoot field, Canada

Published:2018-12 Issue: Volume:159 Page:511-521
ISSN:0926-9851
Container-title:Journal of Applied Geophysics
language:en
Short-container-title:Journal of Applied Geophysics

Author:

Maurya S.P.,Singh N.P.

Funder

Science and Engineering Research Board, Department of Science and Technology, Government of India

Publisher

Elsevier BV

Subject

Geophysics

Reference26 articles.

1. Committee neural networks for porosity and permeability prediction from well logs;Bhatt;Geophys. Prospect.,2002

2. Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review;Bosch;Geophysics,2010

3. Lp - norm deconvolution;Debeye;Geophys. Prospect.,1990

4. Porosity from seismic data - a geostatistical approach;Doyen;Geophysics,1988

5. Avo analysis to extract rock parameters on the Blackfoot 3c-3d seismic data;Dufour,1998

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