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Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues
Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
Automatic design of machine learning via evolutionary computation: A survey
N Li, L Ma, T **
Petrophysical rock ty** in reservoir characterization is an important input for successful
drilling, production, injection, reservoir studies and simulation. In this study petrophysical …
drilling, production, injection, reservoir studies and simulation. In this study petrophysical …
[HTML][HTML] Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields
Rock samples' TOC content is the best indicator of the organic matter in source rocks. The
origin rock samples' analysis is used to calculate it manually by specialists. This method …
origin rock samples' analysis is used to calculate it manually by specialists. This method …
[HTML][HTML] Total organic carbon content logging prediction based on machine learning: A brief review
The total organic carbon content usually determines the hydrocarbon generation potential of
a formation. A higher total organic carbon content often corresponds to a greater possibility …
a formation. A higher total organic carbon content often corresponds to a greater possibility …
[HTML][HTML] A bibliometric analysis of the application of machine learning methods in the petroleum industry
With the emerge of Artificial Intelligence and Machin learning systems, the petroleum
industry has witnessed a significant progress in its different disciplines to optimize decision …
industry has witnessed a significant progress in its different disciplines to optimize decision …
Application of ensemble machine learning methods for kerogen type estimation from petrophysical well logs
M Safaei-Farouji, A Kadkhodaie - Journal of Petroleum Science and …, 2022 - Elsevier
The current study is the first report of estimating kerogen type from petrophysical well logs
implementing various machine learning techniques. The methodology is explained through …
implementing various machine learning techniques. The methodology is explained through …
Well log prediction of total organic carbon: A comprehensive review
J Lai, F Zhao, Z **a, Y Su, C Zhang, Y Tian… - Earth-Science …, 2024 - Elsevier
Source rocks are fundamental elements for petroleum systems, and Total Organic Carbon
(TOC) is one of the most important geochemical parameters in source rock property …
(TOC) is one of the most important geochemical parameters in source rock property …
Natural fractures characterization by integration of FMI logs, well logs and core data: a case study from the Sarvak Formation (Iran)
Carbonate reservoirs in Iran are the most important and main sources of oil and gas
production. Hydrocarbon flow rates from carbonate reservoirs heavily rely on the …
production. Hydrocarbon flow rates from carbonate reservoirs heavily rely on the …
[HTML][HTML] Total organic carbon (TOC) quantification using artificial neural networks: Improved prediction by leveraging XRF data
This study develops a new artificial neural network (ANN) model for predicting the total
organic carbon (TOC) of an organic-rich carbonate mudstone formation using conventional …
organic carbon (TOC) of an organic-rich carbonate mudstone formation using conventional …