Machine learning technique for the prediction of shear wave velocity using petrophysical logs-Reference-Cited by-同舟云学术

Machine learning technique for the prediction of shear wave velocity using petrophysical logs

Published:2019-03 Issue: Volume:174 Page:306-327
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Anemangely Mohammad,Ramezanzadeh Ahmad,Amiri Hamed,Hoseinpour Seyed-Ahmad

Funder

National Iranian South Oil Company

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference40 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. Predicting rock mechanical properties of carbonates from wireline logs (A case study: arab-D reservoir, Ghawar field, Saudi Arabia);Ameen;Mar. Petrol. Geol.,2009

3. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: a case study from Ab-Teymour Oilfield;Anemangely;J. Nat. Gas Sci. Eng.,2017

4. Determination of constant coefficients of Bourgoyne and Young drilling rate model using a novel evolutionary algorithm;Anemangely;J. Min. Environ.,2017

5. Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network;Anemangely;J. Geophys. Eng.,2018

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