[HTML][HTML] Total organic carbon content logging prediction based on machine learning: A brief review

L Zhu, X Zhou, W Liu, Z Kong - Energy Geoscience, 2023 - Elsevier
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 review on intelligent recognition with logging data: tasks, current status and challenges

X Zhu, H Zhang, Q Ren, L Zhang, G Huang… - Surveys in …, 2024 - Springer
Geophysical logging series are valuable geological data that record the physical and
chemical information of borehole walls and in-situ formations, and are widely used by …

An advanced prediction model of shale oil production profile based on source-reservoir assemblages and artificial neural networks

Y Liu, J Zeng, J Qiao, G Yang, W Cao - Applied Energy, 2023 - Elsevier
Over the past decade, hydrocarbon production from shale oil reservoirs has become
increasingly common, and successful shale oil exploration and development depends …

A new approach for predicting oil mobilities and unveiling their controlling factors in a lacustrine shale system: Insights from interpretable machine learning model

E Wang, Y Fu, T Guo, M Li - Fuel, 2025 - Elsevier
Petroleum remains a vital component of the global energy supply, and the exploration and
development of shale petroleum present significant opportunities for growth. The production …

Key factors of marine shale conductivity in southern China—Part II: The influence of pore system and the development direction of shale gas saturation models

L Zhu, Y Ma, J Cai, C Zhang, S Wu, X Zhou - Journal of Petroleum Science …, 2022 - Elsevier
This is the second part of our study on the resistivity curve responses of marine shale gas
reservoirs. The characteristics of the effects of low-porosity systems on electrical conductivity …

[HTML][HTML] A deep-learning-based prediction method of the estimated ultimate recovery (EUR) of shale gas wells

YY Liu, XH Ma, XW Zhang, W Guo, LX Kang, RZ Yu… - Petroleum Science, 2021 - Elsevier
The estimated ultimate recovery (EUR) of shale gas wells is influenced by many factors, and
the accurate prediction still faces certain challenges. As an artificial intelligence algorithm …

[HTML][HTML] Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

L Goliatt, CM Saporetti, LC Oliveira, E Pereira - Petroleum, 2024 - Elsevier
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 …

Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale

A Amosu, M Imsalem, Y Sun - Journal of Applied Geophysics, 2021 - Elsevier
Successful hydrocarbon production in the Eagle Ford relies on technological advances such
as directional geosteering, horizontal drilling, and hydraulic fracturing, as well as the …

Novel method for total organic carbon content prediction based on non-equigap multivariable grey model

X **ao, H Zhu, J Li, C Rao, Y Kang - Engineering Applications of Artificial …, 2024 - Elsevier
Predicting total organic carbon content plays a crucial role in shale gas reservoir evaluation.
To address multi-variable, imperfect logging data and non-equigap characteristics, this study …

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 …