Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder

Identifying and evaluating favorable areas is crucial for shale oil exploration and development, well-location deployment, and fracturing design. Traditional machine learning methods struggle to accurately extract the characteristics of favorable shale oil areas with limited labeled data, affecting...

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Bibliographic Details
Published in:	Oil shale (Tallinn, Estonia : 1984) Vol. 42; no. 1; pp. 79 - 114
Main Authors:	Xu, Rui, Yan, Tie, Sun, Shihui, Qu, Jingyu, Hou, Zhaokai
Format:	Journal Article
Language:	English
Published:	Tallinn Estonian Academy Publishers 01.03.2025 Teaduste Akadeemia Kirjastus (Estonian Academy Publishers)
Subjects:	Accuracy autoencoder batch normalization Data analysis Efficiency favorable area Geology Identification Machine learning Methods Neural networks Oil and gas exploration Oil exploration Oil shale Petroleum mining Research methodology Sedimentary rocks Semi-supervised learning Shale gas Shale oil Shales Support vector machines China
ISSN:	0208-189X, 1736-7492
Online Access:	Get full text
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