Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves-Reference-Cited by-同舟云学术

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Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves

Published:2018-03-22 Issue:3 Volume:15 Page:1050-1061
ISSN:1742-2132
Container-title:Journal of Geophysics and Engineering
language:
Short-container-title:J. Geophys. Eng.

Author:

Zhu Linqi,Zhang Chong,Zhang Chaomo,Wei Yang,Zhou Xueqing,Cheng Yuan,Huang Yuyang,Zhang Le

Funder

Open Found of Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University)

National Nature Science Foundation of Hubei Province

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Management, Monitoring, Policy and Law,Industrial and Manufacturing Engineering,Geology,Geophysics

Link

http://iopscience.iop.org/article/10.1088/1742-2140/aaa7af/pdf

Reference50 articles.

1. Compromise ratio with weighting functions in a Tabu Search multi-criteria approach to examination timetabling

2. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition

3. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm

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1. An integrated convolutional neural network prediction framework for in situ shale oil content based on conventional logging data;Journal of the Geological Society;2024-08-22

2. Total organic carbon (TOC) estimation using ensemble and artificial neural network methods; a case study from Kazhdumi Formation, NW Persian Gulf;Earth Science Informatics;2024-06-25

3. An integrated geomechanical and petrophysical multiparameter approach for gas reservoir evaluation;Journal of Petroleum Exploration and Production Technology;2024-04-15

4. Super learner approach to predict total organic carbon using stacking machine learning models based on well logs;Fuel;2023-12

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