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 …

Well log prediction of total organic carbon: A comprehensive review

J Lai, F Zhao, Z Xia, 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 …

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 …

Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data

S Iraji, R Soltanmohammadi, GF Matheus… - Geoenergy Science and …, 2023 - Elsevier
This study integrates well log data, routine core analyses, microcomputed X-ray tomography
(μ CT) images, and sedimentary petrography to accurately characterize and evaluate the …

Bridging the gap: Integrating static and dynamic data for improved permeability modeling and super K zone detection in vuggy reservoirs

JCR Gavidia, SM Mohammadizadeh… - Geoenergy Science and …, 2024 - Elsevier
Permeability modeling in fractured and vuggy reservoirs presents significant challenges,
especially in carbonate reservoirs like Brazil's Barra Velha Formation. The interpretation of …

Prediction of hydrogen solubility in aqueous solution using modified mixed effects random forest based on particle swarm optimization for underground hydrogen …

GC Mwakipunda, NA Komba, AKF Kouassi… - International Journal of …, 2024 - Elsevier
This paper aims to enhance the prediction accuracy of hydrogen solubility in aqueous
solution, which is crucial for safe and efficient underground hydrogen storage (UHS). The …

HPO-empowered machine learning with multiple environment variables enables spatial prediction of soil heavy metals in coastal delta farmland of China

Y Song, D Zhan, Z He, W Li, W Duan, Z Yang… - … and Electronics in …, 2023 - Elsevier
Abstract Machine learning (ML) models have been widely used for predicting spatial
variability of soil heavy metals. However, it is impossible to explore the entire …

[HTML][HTML] A survey on multi-objective, model-based, oil and gas field development optimization: Current status and future directions

A Rostamian, MB de Moraes, DJ Schiozer, GP Coelho - Petroleum Science, 2024 - Elsevier
In the area of reservoir engineering, the optimization of oil and gas production is a complex
task involving a myriad of interconnected decision variables shaping the production system's …

[HTML][HTML] Spatial prediction of PM2. 5 concentration using hyper-parameter optimization XGBoost model in China

Y Song, C Zhang, X Jin, X Zhao, W Huang… - … Technology & Innovation, 2023 - Elsevier
High-fine particulate matter (PM 2. 5) pollution has become the main object of damaging the
atmospheric environment and endangering human health. Accurate prediction of the spatial …

Experimental analysis of combustion characteristics of corn starch dust clouds under the action of unilateral obstacles and machine learning modeling based on PSO …

J Zhang, X Cao, C Li, Z Du, S Bao, G Li… - Advanced Powder …, 2024 - Elsevier
Corn starch powder is highly flammable and explosive, presenting significant safety hazards
of dust explosions when encountering obstacles during its production and processing. This …