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Original Paper | Open Access

Classification of shale oil micro-occurrence types and flow behavior: Combining QSGSM, pore network, machine learning, and LBM

Yi-Fan Yina,cZhi-Xue Sunb,c,d( )Jing WangeKai-Jun TongfYong-Fei Yangb,c,d
Department of Chemical Engineering, The University of Manchester, Manchester, M13 9PL, UK
State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao, 266580, Shandong, China
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong, China
Key Laboratory of Unconventional Oil and Gas Development (China University of Petroleum (East China)), Ministry of Education, Qingdao, 266580, Shandong, China
PipeChina Engineering Technology Innovation Co., Ltd., Tianjin, 300450, China
CNOOC International Limited, Beijing, 100028, China

Edited by Yan-Hua Sun

Peer review under the responsibility of China University of Petroleum (Beijing).

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Abstract

Shale oil occurs in varying forms within porous media, with diverse micro-occurrences exhibiting intricate topological characteristics within the pores. The impact of shale oil presence on fluid flow patterns in these complex environments remains uncertain. This study investigates how different shale oil micro-occurrence types and saturation levels influence pore network characteristics and fluid flow behavior in shale reservoirs. Firstly, the shale digital core is established using the focused ion beam scanning electron microscope (FIB-SEM) method, and the pore skeleton is segmented via the watershed method. Utilizing the quartet structure generation set method, we constructed 12 digital cores representing floating, cementing, and coating shale oil micro-occurrence types at different oil saturations based on the real digital core model. The pore network analysis, conducted using the maximum ball method, revealed that increasing oil saturation generally leads to a reduction in pore radius and an increase in pore quantity due to pore subdivision, with the cementing type showing the most significant effect. Additionally, machine learning techniques, including random forest, support vector machine (SVM), and K-nearest neighbors (KNN), were employed to classify micro-occurrence types of shale oil based on topological features such as volume ratio, cross-sectional area ratio, three-dimensional shape factor, and Euler number. The support vector machine (SVM) method achieved the highest predictive accuracy (93%), outperforming the random forest (91%) and KNN (75%) methods, indicating its effectiveness in discerning subtle differences between micro-occurrence types. Lattice Boltzmann simulations were employed to study the impact of shale oil micro-occurrence type and saturation on fluid flow properties within the pore networks. Results showed that coating shale oil exhibited the highest permeability, followed by floating, whereas cementing shale oil caused a notable reduction in permeability due to pore blockage. This study enhances the understanding of the intricate relationship between shale oil geometric micro-occurrence patterns, pore structure, and fluid flow behavior within a digital-core framework, providing valuable insights into pore-scale fluid dynamics in shale reservoirs.

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Petroleum Science
Pages 5819-5835

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Cite this article:
Yin Y-F, Sun Z-X, Wang J, et al. Classification of shale oil micro-occurrence types and flow behavior: Combining QSGSM, pore network, machine learning, and LBM. Petroleum Science, 2026, 23(9): 5819-5835. https://doi.org/10.1016/j.petsci.2026.06.007

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Received: 10 February 2025
Revised: 02 June 2026
Accepted: 03 June 2026
Published: 08 June 2026
© 2026

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).