Results 11 to 20 of about 2,839,750 (318)

Study on lithology identification using a multi-objective optimization strategy to improve integrated learning models: a case study of the Permian Lucaogou Formation in the Jimusaer Depression

open access: yesFrontiers in Earth Science
Lithology identification is a critical task in logging interpretation and reservoir evaluation, with significant implications for recognizing oil and gas reservoirs.
Xili Deng   +3 more
doaj   +2 more sources

An optimized identification method of coal-bearing stratum lithology

open access: yesGong-kuang zidonghua, 2020
In view of difficulties in obtaining stratum information parameters and low accuracy of lithology identification in existing lithology identification method of coal-bearing stratum in coal mine underground, an optimized identification method of coal ...
ZHANG Ning, ZHANG Youzhen, YAO Ke
doaj   +2 more sources

A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China

open access: yesOpen Geosciences
The identification of shale lithology is of great importance for the exploration and development of shale reservoirs. The lithology and mineralogical composition of shale are closely related, but a small number of laboratory core analysis samples are ...
Xu Liying   +8 more
doaj   +2 more sources

A Hierarchical Multi-Feature Point Cloud Lithology Identification Method Based on Feature-Preserved Compressive Sampling (FPCS) [PDF]

open access: yesSensors
Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges such ...
Xiaolei Duan   +6 more
doaj   +2 more sources

Research on Lithology Identification based on Machine Learning

open access: yesAcademic Journal of Science and Technology
Machine learning has great potential in lithology identification. Through supervised learning, unsupervised learning, semi-supervised learning, deep learning and other methods, features can be automatically extracted from complex seismic data and logging data to achieve efficient and accurate lithology classification. These methods not only improve the
Kunkun Li
openaire   +2 more sources

Identification of Complicated Lithology with Machine Learning

open access: yesApplied Sciences
Lithology identification is one of the most important research areas in petroleum engineering, including reservoir characterization, formation evaluation, and reservoir modeling.
Liangyu Chen   +6 more
doaj   +2 more sources

Lithology Identification Based on Improved Faster R-CNN

open access: yesMinerals
In the mining industry, lithological identification is crucial for ensuring the safety of equipment and personnel, as well as for improving production efficiency. Traditional ore identification methods, such as visual inspection, physical testing, and chemical analysis, have many limitations in terms of their operational complexity and applicability ...
Peng Fu, Jiyang Wang
openaire   +2 more sources

Application of neural architecture search in lithology identification

open access: yesJournal of Petroleum Exploration and Production Technology
Identifying rock types is the essential step in geological exploration because it guides reservoir description and development planning. Conventional methods that rely on empirical correlations or elementary machine learning approaches frequently produce
Yuhao Zhang   +7 more
doaj   +2 more sources

Large Igneous Province Record Through Time and Implications for Secular Environmental Changes and Geological Time‐Scale Boundaries

open access: yesGeophysical Monograph Series, Page 1-26., 2021

Exploring the links between Large Igneous Provinces and dramatic environmental impact

An emerging consensus suggests that Large Igneous Provinces (LIPs) and Silicic LIPs (SLIPs) are a significant driver of dramatic global environmental and biological changes, including mass extinctions.
Richard E. Ernst   +8 more
wiley  

+18 more sources

Intelligent lithologic identification of sandy conglomerate reservoirs in District No.7 of Karamay oilfield

open access: yesShenzhen Daxue xuebao. Ligong ban, 2023
The sandy conglomerate reservoirs in Karamay are characterized by diverse lithology and interlayers. The cost of the conventional coring methods is high, and the identification accuracy in non-coring section is low, which leads to difficulty in reservoir
LU Ji, LIN Botao, SHI Can, ZHANG Jiahao
doaj   +1 more source

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