[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 …

[HTML][HTML] Evaluation of postgraduate academic performance using artificial intelligence models

Y Baashar, Y Hamed, G Alkawsi, LF Capretz… - Alexandria Engineering …, 2022 - Elsevier
Institutions of higher learning are currently facing the challenging task of attracting new
students who can effectively meet their diverse academic demands. With these demands …

Deep learning integrated approach for hydrocarbon source rock evaluation and geochemical indicators prediction in the Jurassic-Paleogene of the Mandawa basin …

CN Mkono, S Chuanbo, AK Mulashani… - Energy, 2023 - Elsevier
The world's energy demands are growing at an unprecedented rate, and the exploration of
new hydrocarbon sources is more important than ever. Therefore, the objective of this study …

Unsupervised contrastive learning for few-shot TOC prediction and application

H Wang, S Lu, L Qiao, F Chen, X He, Y Gao… - International Journal of …, 2022 - Elsevier
Total organic carbon (TOC) content is significant for “sweet spots” prediction and resource
calculation. Traditional physical methods and machine learning methods, however, only use …

Characterization of brittleness index of gas shale and its influence on favorable block exploitation in southwest China

G Liu, D Shang, Y Zhao, X Du - Frontiers in Earth Science, 2024 - frontiersin.org
The microstructure, mineral composition, total organic carbon content, etc., of gas shale are
crucial parameters for shale reservoirs, which can directly/indirectly affect shale brittleness …

Integrated metaheuristic approaches for estimation of fracture porosity derived from fullbore formation micro-imager logs: Reaping the benefits of stand-alone and …

AG Vijouyeh, MR Hamoudi, DA Bayz… - … Applications of Artificial …, 2025 - Elsevier
Fracture porosity is one of the most effective parameters for reservoir productivity and
recovery efficiency. This study aims to predict and improve the accuracy of fracture porosity …

A Novel Hybrid Machine Learning Approach and Basin Modeling for Thermal Maturity Estimation of Source Rocks in Mandawa Basin, East Africa

CN Mkono, C Shen, AK Mulashani, MR Ngata… - Natural Resources …, 2024 - Springer
Basin modeling and thermal maturity estimation are crucial for understanding sedimentary
basin evolution and hydrocarbon potential. Assessing thermal maturity in the oil and gas …

Machine Learning Approach to Predict the Illite Weight Percent of Unconventional Reservoirs from Well-Log Data: An Example from Montney Formation, NE British …

A Barham, NS Zainal Abidin - Applied Sciences, 2023 - mdpi.com
Shale mineralogy is critical for the proper design and execution of hydraulic fracturing
operations and for evaluating production potential. There has been relatively little research …

TOC prediction of source rocks based on the convolutional neural network and logging curves–A case study of Pinghu Formation in Xihu Sag

Y Jingwen, H Wenxiang, G Xiaoyang, H Yong - Open Geosciences, 2024 - degruyter.com
The total organic carbon (TOC) content is an important index for source rock evaluation.
However, due to the scarcity of rock samples, the vertical continuous TOC change curve …

The novel Artificial Neural Network assisted models: A review

B Srivastav - 2021 - mpra.ub.uni-muenchen.de
Neural networks are one of the methods of artificial intelligence. It is founded on an existing
knowledge and capacity to learn by illustration of the biological nervous system. Neural …