Lithology identification of tight sandstone reservoirs based on SSMO-SSA-LGBM algorithm

SUN Jing; ZHAO Junlong; ZHANG Yuchen; JIN Lirui; CUI Wenjie; CHEN Jiaxin

doi:10.12097/gbc.2024.06.016

2025 Vol. 44, No. 5

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Article navigation > Geological Bulletin of China > 2025 > 44(5): 935-948

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SUN Jing, ZHAO Junlong, ZHANG Yuchen, JIN Lirui, CUI Wenjie, CHEN Jiaxin. 2025. Lithology identification of tight sandstone reservoirs based on SSMO-SSA-LGBM algorithm. Geological Bulletin of China, 44(5): 935-948. doi: 10.12097/gbc.2024.06.016

Citation:

SUN Jing, ZHAO Junlong, ZHANG Yuchen, JIN Lirui, CUI Wenjie, CHEN Jiaxin. 2025. Lithology identification of tight sandstone reservoirs based on SSMO-SSA-LGBM algorithm. Geological Bulletin of China, 44(5): 935-948. doi: 10.12097/gbc.2024.06.016

Lithology identification of tight sandstone reservoirs based on SSMO-SSA-LGBM algorithm

1.
School of Earth Science and Engineering, Xi'an University of Petroleum, Xi'an 710000, Shaanxi, China
2.
Key Laboratory of Oil and Gas Reservoir Geology in Shaanxi Province, Xi'an University of Petroleum, Xi'an 710000, Shaanxi, China

More Information

Corresponding author: ZHAO Junlong, zjl1970@163.com

Received Date: 16 October 2024

Revised Date: 13 January 2025

Publish Date: 15 May 2025

Abstract

Abstract

Objective
Existing lithology logging identification methods face challenges of imbalanced lithology class processing and insufficient sensitivity when applied to tight sandstone reservoirs.
Methods
This study proposes the SSMO−SSA−LGBM model. First, the SVM−SMOTE oversampling algorithm (abbreviated as SSMO) is used to balance samples with fewer lithology data in the training set by generating synthetic samples. These synthetic samples are combined with the original training set to form a new training dataset for constructing the LightGBM (LGBM) model. Given the numerous hyperparameters in LGBM, the Sparrow Search Algorithm (SSA) is employed to optimize hyperparameters and obtain the optimal combination. The model is trained using logging data from the Yan 10 tight sandstone reservoir in the Huachi S Block, and compared with KNN, Adaboost, Random Forest, and other models.
Results
After SSMO balancing, the LGBM model exhibits enhanced recognition performance for minority lithology classes. The SSA algorithm achieves global optimization with fewer iterations, obtaining the optimal hyperparameters for LGBM. The SSMO−SSA−LGBM model demonstrates superior predictive performance, with lithology identification results on validation wells showing high consistency with core data.
Conclusions
The SSMO algorithm effectively mitigates the adverse effects of lithology class imbalance on prediction accuracy. The SSA algorithm efficiently identifies the optimal hyperparameter combination for LGBM through limited iterations, maximizing model performance. The proposed model achieves satisfactory application results in the Huachi S Block.
- SSMO-SSA-LGBM algorithm /
- imbalanced data /
- lithology recognition /
- tight sandstone reservoir /
- Huachi, Gansu

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References

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