A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

Timur Merembayev, Darkhan Kurmangaliyev, Bakhbergen Bekbauov and Yerlan Amanbek
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Timur Merembayev: Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
Darkhan Kurmangaliyev: KazMunayGas Engineering LLP, Nur-Sultan 010000, Kazakhstan
Bakhbergen Bekbauov: KazMunayGas Engineering LLP, Nur-Sultan 010000, Kazakhstan
Yerlan Amanbek: Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan

Energies, 2021, vol. 14, issue 7, 1-16

Abstract: 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.

Keywords: machine learning; well log data; lithology classification (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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