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Volume 19
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Article

Identification and Application of Flow Units in Tight Sandstone Reservoirs Under Complex Structural Settings Based on the SSOM Algorithm: A Case Study of the Shaximiao Formation in Southern Sichuan Basin

by
Hanxuan Yang
1,
Jiaxun Lu
1,
Yani Deng
1,
Zhiwei Zheng
1,
Lin Jiang
1,
Hui Long
1,
Lei Zhang
2 and
Xinrui Wang
2,*
1
Shunan Division, PetroChina Southwest Oil & Gasfield Company, Luzhou 646000, China
2
School of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
*
Author to whom correspondence should be addressed.
Energies 2026, 19(6), 1397; https://doi.org/10.3390/en19061397
Submission received: 3 February 2026 / Revised: 22 February 2026 / Accepted: 27 February 2026 / Published: 10 March 2026
(This article belongs to the Section H: Geo-Energy)
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Abstract

To address the challenges of strong tectonic stress anisotropy, multi-scale pore networks, and complex seepage pathways in the tight sandstone reservoirs of the Shaximiao Formation, southern Sichuan Basin, this study integrates petrophysical analysis with machine learning techniques to develop an intelligent flow unit identification methodology applicable to complex structural settings. Based on core petrophysical properties, mercury injection capillary pressure (MICP) data, and production dynamics, the reservoirs were classified into a fracture-type plus four conventional-type (I–IV) flow unit system. Quantitative identification of flow units was achieved using conventional well-logging curves (Gamma Ray, Spontaneous Potential, Caliper, etc.—eight curves total) using the Gradient Boosting Decision Tree (GBDT), Backpropagation Neural Network (BPANN), and Supervised Self-Organizing Map (SSOM) algorithms. Key findings include the following: The SSOM algorithm delivered optimal performance, achieving a 90.1% average accuracy on the test set, significantly outperforming GBDT (87.8%) and BPANN (85.5%), particularly in capturing nonlinear responses of fracture-type reservoirs and class-overlapping samples. Flow unit spatial distribution exhibits dual sedimentary-structural control: High-quality units (Types I/II) are enriched at the base of distributary channels in deltaic plain facies (J2S12), while fracture-type units cluster near fault peripheries. Strong planar heterogeneity is observed in the J2S13 sub-member: Near-source areas (south/southwest) develop banded Type I/II units, whereas distal regions are dominated by Type IV units. This methodology provides a theoretical foundation and intelligent technological pathway for the efficient development of highly heterogeneous tight sandstone reservoirs.
Keywords: tight sandstone; flow units; SSOM; Sichuan Basin tight sandstone; flow units; SSOM; Sichuan Basin

Share and Cite

MDPI and ACS Style

Yang, H.; Lu, J.; Deng, Y.; Zheng, Z.; Jiang, L.; Long, H.; Zhang, L.; Wang, X. Identification and Application of Flow Units in Tight Sandstone Reservoirs Under Complex Structural Settings Based on the SSOM Algorithm: A Case Study of the Shaximiao Formation in Southern Sichuan Basin. Energies 2026, 19, 1397. https://doi.org/10.3390/en19061397

AMA Style

Yang H, Lu J, Deng Y, Zheng Z, Jiang L, Long H, Zhang L, Wang X. Identification and Application of Flow Units in Tight Sandstone Reservoirs Under Complex Structural Settings Based on the SSOM Algorithm: A Case Study of the Shaximiao Formation in Southern Sichuan Basin. Energies. 2026; 19(6):1397. https://doi.org/10.3390/en19061397

Chicago/Turabian Style

Yang, Hanxuan, Jiaxun Lu, Yani Deng, Zhiwei Zheng, Lin Jiang, Hui Long, Lei Zhang, and Xinrui Wang. 2026. "Identification and Application of Flow Units in Tight Sandstone Reservoirs Under Complex Structural Settings Based on the SSOM Algorithm: A Case Study of the Shaximiao Formation in Southern Sichuan Basin" Energies 19, no. 6: 1397. https://doi.org/10.3390/en19061397

APA Style

Yang, H., Lu, J., Deng, Y., Zheng, Z., Jiang, L., Long, H., Zhang, L., & Wang, X. (2026). Identification and Application of Flow Units in Tight Sandstone Reservoirs Under Complex Structural Settings Based on the SSOM Algorithm: A Case Study of the Shaximiao Formation in Southern Sichuan Basin. Energies, 19(6), 1397. https://doi.org/10.3390/en19061397

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