Carbonate lithology Identification Based on Genetic Algorithm Adaptive LightGBM

Jingyue Zhang; Renfeng Zhang; Siyu Zhang; Sijie Li

doi:10.62051/ijcsit.v8n6.03

Authors

Jingyue Zhang College of Computer and Information, College of Arts and Sciences of Hubei Normal University, Huangshi, China
Renfeng Zhang College of Computer and Information, College of Arts and Sciences of Hubei Normal University, Huangshi, China
Siyu Zhang College of Computer and Information, College of Arts and Sciences of Hubei Normal University, Huangshi, China
Sijie Li College of Computer and Information, College of Arts and Sciences of Hubei Normal University, Huangshi, China

DOI:

https://doi.org/10.62051/ijcsit.v8n6.03

Keywords:

Lithological identification, LightGBM, GBDT, Geophysical logging

Abstract

Lithology identification utilizing logging data stands as a pivotal research area within reservoir prediction, integral to the broader context of oil and gas exploration and development. In order to solve the problem that the traditional logging lithology identification method and machine learning method do not have high accuracy for the lithology identification of complex carbonate rocks. In view of the powerful performance of deep learning methods in feature extraction and data analysis, this paper proposes a lithology recognition method based on the Genetic Algorithm-adaptive LightGBM model. Due to the small number of features of the original logging data, the feature shift method is first used to expand the features of each data. Subsequently, to optimize the classification performance of our model, we leverage the Genetic Algorithm (GA) to fine-tune the hyperparameters of the LightGBM model. With the optimal hyperparameter configuration in place, we evaluate the model's performance through rigorous training with logging data. The adaptive LightGBM lithology recognition model based on GA is compared with the lithology identification results of GBDT, Bayes, DNN and DT, and the overall prediction performance is evaluated by using the accuracy and its macroscopic mean. Through the test and verification of the actual well data, the proposed method has achieved very considerable results in lithology identification, showing the broad prospect of LightGBM technology in the field of geophysics.

Downloads

Download data is not yet available.

References

[1] Zhang, C., Zhang, Y., Shi, X., Almpanidis, G., Fan, G., & Shen, X. (2019). On incremental learning for gradient boosting decision trees. Neural Processing Letters, 50(1), 957–987. https://doi.org/10.1007/s11063-019-09999-3

[2] Lee, S. H., Kharghoria, A., & Datta-Gupta, A. (2002). Electrofacies characterization and permeability predictions in complex reservoirs. SPE Reservoir Evaluation & Engineering, 5(3), 237–248. https://doi.org/10.2118/78662-P

[3] Puskarczyk, E. (2020). Application of multivariate statistical methods and artificial neural network for facies analysis from well logs data. Energies, 13(7). https://doi.org/10.3390/en13071548

[4] Li, J., Bao, H., Liu, S., Shi, P., Gao, F., Wei, L., Wang, L., & Yu, H. (2026). Machine learning for lithology identification in electrical image logging: A case study of the Chang 73 Sub-Member, Yanchang Formation, East Gansu region. Earth Science Informatics, 19, 139. https://doi.org/10.1007/s12145-026-02191-x

[5] Liu, X., Zhao, Y., Dai, Y., Wang, W., Cao, Z., Wang, P., & Wu, H. (2026). A Bayesian-optimized hybrid transformer-long short-term memory framework for lithology identification. Journal of Applied Geophysics, 253, 106400. https://doi.org/10.1016/j.jappgeo.2026.106400

[6] Peng, Y., Li, K., Zhu, Y., Xu, Z., Yang, P., & Sun, X. (2023). FL-XGBoost algorithm-based method for identifying sandstone and mudstone: a case study of Niuzhuang area in Shengli Oilfield. Petroleum Geology and Recovery Efficiency, 30(1), 76–85.

[7] Shi, S. M. (2024). Lithology identification of uranium drilling based on SMOTE algorithm logic-CNN. Journal of Biotech Research, 18, 53–64.

[8] Quan, R., Zhang, H., Zhang, D., Zhao, X., & Yan, L. Z. (2022). A novel hybrid method of lithology identification based on k-means++ algorithm and fuzzy decision tree. Journal of Petroleum Science and Engineering, 208. https://doi.org/10.1016/j.petrol.2021.10968

[9] Liang, W., Luo, S., Zhao, G., & Wu, H. (2020). Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM. Mathematics, 8(5), 765. https://doi.org/10.3390/math8050765

[10] Ali, S., Alshboul, O., Al Mamlook, R. E., & Hamedat, O. (2021). Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction, 129. https://doi.org/10.1016/j.autcon.2021.103827

[11] Wang, D. N., Li, L., & Zhao, D. (2022). Corporate finance risk prediction based on LightGBM. Information Sciences, 602, 259–268. https://doi.org/10.1016/j.ins.2022.04.058

[12] Li, L., Yu, Y., Bai, S., & Chen, X. (2018). Towards effective network intrusion detection: A hybrid model integrating Gini index and GBDT with PSO. Journal of Sensors, 2018, 1–9. https://doi.org/1155/2018/1578314

[13] Henan University, Education Information Centre of Henan Province. (2019). On incremental learning for gradient boosting decision trees. Neural Processing Letters, 50(1), 957–987. https://doi.org/10.1007/s11063-019-09999-31

[14] Weng, T., Liu, W., & Xiao, J. (2019). Supply chain sales forecasting based on lightGBM and LSTM combination model. Industrial Management & Data Systems, 120(2), 265–279. https://doi.org/10.1108/imds-03-2019-0170

[15] Zhang, Q., Wang, H., Liu, Z., & Liao, W. (2026). Predicting rare earth extraction efficiency with organophosphorus ligands: A data-driven machine learning approach. Journal of Rare Earths, 44(7), 2204–2214.

[16] Wang, Y. F. (2026). Model, data, and knowledge-driven geophysical inverse problems and intelligent computing. Science China Earth Sciences, 1–38.