Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data-Reference-Cited by-同舟云学术

Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data

Published:2021-12 Issue:6 Volume:13 Page:1466-1477
ISSN:1674-7755
Container-title:Journal of Rock Mechanics and Geotechnical Engineering
language:en
Short-container-title:Journal of Rock Mechanics and Geotechnical Engineering

Author:

Miah Mohammad Islam

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology

Reference92 articles.

1. Prediction of shear wave velocity using artificial neural network technique, multiple regression and petrophysical data: a case study in Asmari reservoir (SW Iran);Akhundi;Open J. Geol.,2014

2. Investigating the dependence of shear wave velocity on petrophysical parameters;Al-Dousari;J. Petrol. Sci. Eng.,2016

3. Seismic velocity estimation from well log data with genetic algorithms in comparison to neural networks and multilinear approaches;Aleardi;J. Appl. Geophys.,2015

4. Intelligent data-driven analytics to predict shear wave velocity in carbonate formations: comparison between recurrent and conventional neural networks;Alkinani,2019

5. Predicting rock mechanical properties of carbonates from wireline logs (A case study: arab-D reservoir, Ghawar field, Saudi Arabia);Ameen;Mar. Petrol. Geol.,2009

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