Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs-Reference-Cited by-同舟云学术

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Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs

Published:2021-01 Issue: Volume:146 Page:104626
ISSN:0098-3004
Container-title:Computers & Geosciences
language:en
Short-container-title:Computers & Geosciences

Author:

Bai Yang,Tan Maojin

Funder

National Natural Science Foundation of China

National Major Projects ‘Development of Major Oil&Gas Fields and Coal Bed Methane

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Information Systems

Reference38 articles.

1. A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: an example from Kangan and Dalan reservoirs in South Pars Gas Field, Iran;Ali;Comput. Geosci.,2009

2. Total organic carbon prediction in shale gas reservoirs from well logs data using the multilayer perceptron neural network with levenberg marquardt training algorithm: application to barnett shale;Aliouane;Arabian J. Sci. Eng.,2015

3. Committee neural networks for porosity and permeability prediction from well logs;Alpana;Geophys. Prospect.,2008

4. A new method to estimate total organic carbon (TOC) content, an example from goldwyer shale formation, the canning basin;Alshakhs;Open Petrol. Eng. J.,2017

5. Principal component analysis (PCA) based hybrid models for the accurate estimation of reservoir water saturation;Asante-Okyere;Comput. Geosci.,2020

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2. 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

3. A new method based on multiresolution graph-based clustering for lithofacies analysis of well logging;Computational Geosciences;2024-03-23

4. A Comprehensive Approach to Total Organic Carbon Prediction: Dual-Output Neural Networks and Ensemble Learning;2023-12-21

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

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