TY - JOUR
T1 - A Comparison of Machine Learning Algorithms in Predicting Lithofacies:Case Studies from Norway and Kazakhstan
AU - Merembayev, Timur
AU - Kurmangaliyev, Darkhan
AU - Bekbauov, Bakhbergen
AU - Amanbek, Yerlan
N1 - Funding Information:
Funding: This work was partially supported by the Nazarbayev University, Grant No. 110119FD4502, the SPG fund and the Ministry of Education and Science of the Republic of Kazakhstan, Grant No. AP08052762.
Funding Information:
Acknowledgments: T.M. and Y.A. wish to acknowledge the research grant, No AP08052762, from the Ministry of Education and the Nazarbayev University Faculty Development Competitive Research Grant (NUFDCRG), Grant No 110119FD4502.
Publisher Copyright:
Copyright: © 2021 by the authors.
PY - 2021/3/29
Y1 - 2021/3/29
N2 - Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.
AB - Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.
KW - Lithology classification
KW - Machine learning
KW - Well log data
UR - http://www.scopus.com/inward/record.url?scp=85105070666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105070666&partnerID=8YFLogxK
U2 - 10.3390/en14071896
DO - 10.3390/en14071896
M3 - Article
AN - SCOPUS:85105070666
VL - 14
JO - Energies
JF - Energies
SN - 1996-1073
IS - 7
M1 - 1896
ER -