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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Summary: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 accuracy and generalization. This study proposes an intelligent method for identifying favorable shale oil areas under semi-supervised learning (SSAE-plus) to identify and evaluate favorable shale oil areas of the Qingshankou Formation in the Songliao Basin. The experimental results show that this method can effectively overcome the favorable area identification model’s reliance on labeled data and can adaptively extract the characteristics of favorable shale oil areas without supervision. The accuracy of model identification is as high as 98.82%. Compared with other methods, the SSAE-plus yields higher accuracy and efficiency, while being more stable and generalizable. The SSAE-plus achieved over 95% accuracy in identifying favorable shale oil areas across six datasets. It has broad application prospects in identifying and evaluating favorable areas, and provides valuable theoretical insights for shale oil development and exploration well layout.
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ISSN:0208-189X
1736-7492
DOI:10.3176/oil.2025.1.04