Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs-Reference-Cited by-同舟云学术

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Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs

Published:2020-09 Issue: Volume:192 Page:107234
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Zhang Yan,Zhong Hong-Ru,Wu Zhong-Yuan,Zhou Heng,Ma Qiao-Yu

Funder

China Major State S&T Special Project

China University of Geosciences

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference48 articles.

1. Designing cyclic pressure pulsing in naturally fractured reservoirs using an inverse looking recurrent neural network;Artun;Comput. Geosci.,2012

2. 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

3. Model-based shear-wave velocity estimation versus empirical regressions;Arild;Geophys. Prospect.,1999

4. Prediction of compressional, shear and stoneley wave velocities from conventional well log data using a committee machine with intelligent systems;Asoodeh;Rock Mech. Rock Eng.,2012

5. Support vector regression based determination of shear wave velocity;Bagheripour;J. Petrol. Sci. Eng.,2015

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