TY - GEN
T1 - Enhanced Lithology Classification in Well Log Data Using Ensemble Machine Learning Techniques
AU - Garini, Sherly Ardhya
AU - Shiddiqi, Ary Mazharuddin
AU - Utama, Widya
AU - Jabar, Omar Abdul
AU - Insani, Alif Nurdien Fitrah
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Reservoirs have a strategic role in areas where energy resources must be discovered and sustained. Information related to subsurface lithology is one of the important factors in formation evaluation and reservoir characterization. Conventional lithological interpretation has several drawbacks, such as long interpretation time and bias. These drawbacks are caused by geological heterogeneity, complex data sets, and large data volumes. Therefore, this research introduces an approach for lithology classification by integrating the Synthetic Minority Oversampling Technique (SMOTE) and machine learning models such as K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), and soft ensemble voting technique, which combines KNN, RF, and DT machine learning models. This research highlights that the KNN and Soft Ensemble Voting lithology classification models outperformed the other models, with nearly equal evaluation scores across all metrics. The KNN model consistently achieved the highest evaluation metrics, with an accuracy of 94.06%, precision of 94.13%, recall of 94.06%, and F1 Score of 93.57%. These results show that with appropriate preprocessing techniques and meticulous model tuning, KNN can independently achieve excellent accuracy, as demonstrated by the research results. The results of this research indicate the effectiveness of the proposed method in lithology classification, highlighting its potential to significantly improve conventional interpretation methods in the geophysical domain.
AB - Reservoirs have a strategic role in areas where energy resources must be discovered and sustained. Information related to subsurface lithology is one of the important factors in formation evaluation and reservoir characterization. Conventional lithological interpretation has several drawbacks, such as long interpretation time and bias. These drawbacks are caused by geological heterogeneity, complex data sets, and large data volumes. Therefore, this research introduces an approach for lithology classification by integrating the Synthetic Minority Oversampling Technique (SMOTE) and machine learning models such as K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), and soft ensemble voting technique, which combines KNN, RF, and DT machine learning models. This research highlights that the KNN and Soft Ensemble Voting lithology classification models outperformed the other models, with nearly equal evaluation scores across all metrics. The KNN model consistently achieved the highest evaluation metrics, with an accuracy of 94.06%, precision of 94.13%, recall of 94.06%, and F1 Score of 93.57%. These results show that with appropriate preprocessing techniques and meticulous model tuning, KNN can independently achieve excellent accuracy, as demonstrated by the research results. The results of this research indicate the effectiveness of the proposed method in lithology classification, highlighting its potential to significantly improve conventional interpretation methods in the geophysical domain.
KW - classification
KW - lithology
KW - machine learning
KW - well log
UR - http://www.scopus.com/inward/record.url?scp=85193802163&partnerID=8YFLogxK
U2 - 10.1109/AIMS61812.2024.10512485
DO - 10.1109/AIMS61812.2024.10512485
M3 - Conference contribution
AN - SCOPUS:85193802163
T3 - International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
BT - International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
Y2 - 22 February 2024 through 23 February 2024
ER -