Study on lithology identification using a multi-objective optimization strategy to improve integrated learning models: a case study of the Permian Lucaogou Formation in the Jimusaer Depression
Lithology identification is a critical task in logging interpretation and reservoir evaluation, with significant implications for recognizing oil and gas reservoirs. The challenge in shale reservoirs lies in the similar logging response characteristics of different lithologies and the imbalanced dat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1540035/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Lithology identification is a critical task in logging interpretation and reservoir evaluation, with significant implications for recognizing oil and gas reservoirs. The challenge in shale reservoirs lies in the similar logging response characteristics of different lithologies and the imbalanced data scale, leading to fuzzy lithology classification boundaries and increased difficulty in identification. This study focuses on the shale reservoir of the Permian Lucaogou Formation in the Jimusaer Depression for lithology identification. Initially, a comprehensive sampling model—Smote-Tomek (ST) is used to introduce new feature information into the dataset while removing redundant features, effectively addressing the issue of data imbalance. Then, by combining the multi-objective optimization strategy Artificial Rabbit Optimization (ARO) with the Light Gradient Boosting Machine (LightGBM) model, a new intelligent lithology identification model (ST-ARO-LightGBM) is proposed, aimed at solving the problem of non-optimal hyperparameter settings in the model. Finally, the proposed new intelligent lithology identification model is compared and analyzed with six models: K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightGBM, all after comprehensive sampling. The experimental results show that the ST-ARO-LightGBM model outperforms other classification models in terms of classification evaluation metrics for different lithologies, with an overall classification accuracy improvement of 9.13%. The method proposed in this paper can solve the problem of non-equilibrium in rock samples, and can further improve the classification performance of traditional machine learning, and provide a method reference for the lithology classification of shale reservoirs. |
|---|---|
| ISSN: | 2296-6463 |