Machine Learning Algorithms for Lithofacies Classification of the Gulong Shale from the Songliao Basin, China

Mingqiu Hou, Yuxiang Xiao, Zhengdong Lei, Zhi Yang, Yihuai Lou and Yuming Liu
Additional contact information
Mingqiu Hou: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Yuxiang Xiao: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Zhengdong Lei: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Zhi Yang: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Yihuai Lou: Center for Hypergravity Experimental and Interdisciplinary Research, Zhejiang University, Hangzhou 310058, China
Yuming Liu: College of Geosciences, China University of Petroleum, Beijing 102249, China

Energies, 2023, vol. 16, issue 6, 1-19

Abstract: Lithofacies identification and classification are critical for characterizing the hydrocarbon potential of unconventional resources. Although extensive applications of machine learning models in predicting lithofacies have been applied to conventional reservoir systems, the effectiveness of machine learning models in predicting clay-rich, lacustrine shale lithofacies has yet to be tackled. Here, we apply machine learning models to conventional well log data to automatically identify the shale lithofacies of Gulong Shale in the Songliao Basin. The shale lithofacies were classified into six types based on total organic carbon and mineral composition data from core analysis and geochemical logs. We compared the accuracy of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest models. We mitigated the bias of imbalanced data by applying oversampling algorithms. Our results show that ensemble methods (XGBoost and Random Forest) have a better performance in shale lithofacies identification than the other models do, with accuracies of 0.868 and 0.884, respectively. The organic siliceous shale proposed to have the best hydrocarbon potential in Gulong Shale can be identified with F1 scores of 0.853 by XGBoost and 0.877 by Random Forest. Our study suggests that ensemble machine learning models can effectively identify the lithofacies of clay-rich shale from conventional well logs, providing insight into the sweet spot prediction of unconventional reservoirs. Further improvements in model performances can be achieved by adding domain knowledge and employing advanced well log data.

Keywords: machine learning models; ensemble methods; XGBoost; random forest; shale lithofacies; well log; Songliao basin; Gulong sag (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/6/2581/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/6/2581/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:6:p:2581-:d:1092051

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().