Classification of Logging Data Using Machine Learning Algorithms
Abstract
:1. Introduction
- The main results of well log data interpretation using machine learning for different types of deposits are presented;
- A uranium well log (UWL) dataset is presented and described, allowing us to set up machine learning methods for ROZ detection and lithological classification;
- This paper presents the state-of-the-art result in solving ROZ detection and lithological classification tasks obtained using the UWL dataset;
- The influence of floating window size on the quality of classification is investigated.
2. Related Works
- Lithological classification.
- The identification of reservoirs.
- Stratigraphic classification.
- The estimation of rock permeability.
- The identification of reservoir oxidation.
- The technology of extraction dictates the necessity of identifying impermeable layers with a thickness of 20 cm (this is a requirement of the regulatory documentation) within the ore-bearing horizon with a thickness of 60–80 m. In some cases, one identified layer in petroleum geophysics corresponds to the entire interpreted ore-bearing horizon in uranium geophysics [1].
- The set of recorded logging data is much smaller compared to oil and gas fields. In fact, only fairly simple variations in electrical logging (AR, SP, IL) are available. Gamma logging cannot be used for lithological classification because the contribution to the recorded gamma radiation from radium and its decay products is two orders of magnitude greater than that from lithology. Of the neutron methods, only fission neutron logging is used, aimed at the direct determination of uranium [1].
- Difficulties with extracting and tying the core due to the characteristics of the section (sand and clay).
- Use of experts’ assessments, which contain a significant degree of subjectivity [16].
- The regulatory framework, interpretation methods, and standard set of logging methods were inherited from the USSR and underwent only minor changes in Kazakhstan.
- There are no publicly available datasets that allow for a comparative analysis of classification and forecasting methods based on well logging data from uranium deposits.
3. Method
3.1. Data Preprocessing
- ‘up5’:up_w = 5 (size of the top of the data window).
- ‘dn150’:dn_w = 150 (size of the bottom of the data window).
- ‘t5’:test_part = 5 (which part of the dataset will be the test part. In this case, it is the 5th of 10 possible test parts).
- ‘n1’:norm = 1 (whether the input parameters were normalized; 1—normalized, 0—not normalized).
3.2. Training and Evaluating Machine Learning Models
- The dataset is not balanced. It means that the number of objects of different classes in the dataset is different—class 2: 6876; class 1: 35,812;class 8: 28,073 (ROZ).
- In the ROZ classification task, objects of all three classes are equally important for the researcher.
- 3.
- In the lithological classification (LC) problem, the correct classification of low-permeability rocks (clay—7) and the overall accuracy of the classifier are important.
4. Results
4.1. ROZ Identification
4.2. Lithological Classification
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Metrics | Formula | Explanation | |
---|---|---|---|
Regression models | Mean absolute error—MAE | where n is the sample size; the real value of the target variable for the i-th example; calculated value of the i-th example; | |
Determination coefficient | |||
Linear correlation coefficient (or Pearson correlation coefficient) | where | ||
Classification models | Accuracy | where is the number of correct answers and N is the total number of possible answers of the model | |
Precision | where true positive (TP) and true negative (TN) are cases of correct operation of the classifier. Accordingly, false negatives (FNs) and false positives (FPs) are cases of misclassification | ||
Recall | |||
F1 score |
Appendix B
Dw_N | Classifier | Acc | f1_Score_Class1 | f1_Score_Class2 | f1_Score_Class8 | f1_Score_Macro | f1_Score_Micro | Duration |
---|---|---|---|---|---|---|---|---|
5 | LGBM | 0.807 | 0.805 | 0.633 | 0.659 | 0.699 | 0.807 | 22.2 |
5 | RBF SVM | 0.792 | 0.786 | 0.627 | 0.632 | 0.682 | 0.792 | 1596 |
25 | LGBM | 0.808 | 0.805 | 0.661 | 0.673 | 0.713 | 0.808 | 52 |
25 | RBF SVM | 0.794 | 0.782 | 0.630 | 0.650 | 0.688 | 0.794 | 3274 |
50 | LGBM | 0.814 | 0.815 | 0.668 | 0.681 | 0.721 | 0.814 | 81.1 |
50 | RBF SVM | 0.795 | 0.780 | 0.603 | 0.662 | 0.682 | 0.795 | 5743.5 |
Appendix C
F1_Score | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Dw_N | Model | Acc | 1 | 3 | 4 | 5 | 6 | 7 | 9 | f1_Macro | f1_Micro | Duration |
5 | LGBM | 0.55 | 0.695 | 0.295 | 0.168 | 0.29 | 0.015 | 0.643 | 0 | 0.301 | 0.55 | 3.98 |
XGB | 0.565 | 0.705 | 0.292 | 0.159 | 0.433 | 0 | 0.623 | 0 | 0.344 | 0.565 | 120.97 | |
MLP | 0.599 | 0.739 | 0.266 | 0.078 | 0.022 | 0 | 0.671 | 0 | 0.276 | 0.599 | 32.001 | |
10 | LGBM | 0.565 | 0.706 | 0.344 | 0.209 | 0.247 | 0.063 | 0.629 | 0 | 0.314 | 0.565 | 4.731 |
RFC | 0.588 | 0.729 | 0.318 | 0.197 | 0.16 | 0.043 | 0.608 | 0 | 0.324 | 0.588 | 24.25 | |
XGB | 0.577 | 0.715 | 0.348 | 0.19 | 0.337 | 0.089 | 0.628 | 0 | 0.359 | 0.577 | 114.24 | |
MLP | 0.62 | 0.758 | 0.328 | 0.11 | 0.059 | 0 | 0.646 | 0 | 0.3 | 0.62 | 37.518 | |
25 | LGBM | 0.578 | 0.719 | 0.375 | 0.206 | 0.185 | 0.022 | 0.65 | 0 | 0.308 | 0.578 | 9.851 |
RFC | 0.608 | 0.745 | 0.337 | 0.213 | 0.143 | 0.027 | 0.641 | 0 | 0.332 | 0.608 | 58.774 | |
XGB | 0.609 | 0.744 | 0.392 | 0.21 | 0.263 | 0.028 | 0.671 | 0 | 0.364 | 0.609 | 260.25 | |
MLP | 0.641 | 0.774 | 0.402 | 0.18 | 0.142 | 0 | 0.668 | 0 | 0.341 | 0.641 | 50.892 | |
50 | LGBM | 0.61 | 0.754 | 0.4 | 0.241 | 0.178 | 0.012 | 0.644 | 0 | 0.318 | 0.61 | 17.161 |
RFC | 0.639 | 0.773 | 0.405 | 0.224 | 0.17 | 0 | 0.649 | 0 | 0.35 | 0.639 | 104.44 | |
XGB | 0.636 | 0.768 | 0.415 | 0.243 | 0.301 | 0.005 | 0.683 | 0 | 0.381 | 0.636 | 435.20 | |
MLP | 0.659 | 0.792 | 0.454 | 0.228 | 0.198 | 0 | 0.674 | 0 | 0.37 | 0.659 | 81.985 | |
100 | LGBM | 0.634 | 0.776 | 0.434 | 0.253 | 0.153 | 0.011 | 0.665 | 0 | 0.327 | 0.634 | 32.906 |
RFC | 0.656 | 0.79 | 0.431 | 0.245 | 0.216 | 0 | 0.649 | 0 | 0.368 | 0.656 | 195.50 | |
XGB | 0.659 | 0.791 | 0.441 | 0.247 | 0.308 | 0 | 0.692 | 0 | 0.391 | 0.659 | 732.61 | |
MLP | 0.656 | 0.796 | 0.432 | 0.253 | 0.188 | 0 | 0.666 | 0 | 0.368 | 0.656 | 102.92 | |
200 | LGBM | 0.65 | 0.794 | 0.471 | 0.277 | 0.091 | 0.019 | 0.636 | 0 | 0.327 | 0.65 | 46.825 |
RFC | 0.681 | 0.81 | 0.489 | 0.282 | 0.199 | 0 | 0.633 | 0 | 0.381 | 0.681 | 658.43 | |
XGB | 0.694 | 0.819 | 0.492 | 0.295 | 0.28 | 0.008 | 0.705 | 0 | 0.41 | 0.694 | 1319.5 | |
MLP | 0.649 | 0.789 | 0.455 | 0.234 | 0.18 | 0 | 0.652 | 0 | 0.364 | 0.649 | 157.25 |
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№ | Classifier | Abbreviated Name | References |
---|---|---|---|
1. | Light gradient boosting machine | LGBM | [23,24,25] |
2. | Random forest classifier | RF | [26] |
3. | Extreme gradient boosting | XGB | [27] |
4. | k-nearest neighbors | kNN | [28] |
5. | Decision tree | DT | [29] |
6. | Artificial neural network or multilayer perceptron | MLP or ANN | [30,31] |
7. | Naive Bayes classifier | NB | [32] |
8. | Support vector machines with linear kernel | Linear SVM | [33] |
9. | Support vector machines with rbf kernel | RBF SVM | [33] |
10. | Long short-term memory | LSTM | [34] |
11. | Convolution neural network | CNN | [35] |
Extracted Resources | Task | Model | Results | Ref. |
---|---|---|---|---|
Oil | 1, 2 | Bidirectional LSTM | Acc = 92.69% | [36] |
Oil, gas | 1 | DT, RF | f1 = 0.97 (RF), f1 = 0.94 (DT) | [37] |
Oil | 1, 2 | UL, SL | UL = 80%, SL = 90% | [22] |
Oil | 1, 2 | XGBoost and RF | Acc = 0.882 (XGB) | [38] |
Oil, gas | 1 | kNN, RF, XGB, MLP | Acc = 0.79 | [39] |
Coal | 1 | XGB, RF, ANN | Acc = 0.99 (RF) | [40] |
Oil | 4 | DFFNN, XBG, LR | R2 = 0.9551 (LR) | [41] |
Oil, gas | 1 | ANN | Acc = 0.88 (ANN) | [11] |
Oil, gas | 1 | Hybrid model based on CNN and LSTM | Acc = 87.3% (CNN-LSTM) | [42] |
Oil, gas | 2 | XGB, LogR | ROC AUC = 0.824 | [43] |
Coal | 1 | LR, SVM, ANN, RF, XGB | Acc > 0.9 | [44] |
Coal | 1 | SVM, MLP, DT, RF, XG | Acc = 0.8 | [7] |
Oil, gas | 1 | MLP, SVM, XGB, RF | Acc = 0.868 (XGB) and Acc = 0.884 (RF) | [45] |
Geothermal wells | 1 | kNN, SVM, XGB | Acc = 0.9067 (XGB) | [9] |
Sulfide ore | 1 | RF | R > 0.66 between calculated and measured Na concentration in core | [46] |
Oil | 1 | UL | Acc = 0.5840 | [47] |
Oil | 1 | RF | F1 = 0.913 | [48] |
Uranium | 1, 3 | RF, kNN, XGB | Acc = 0.65 (1), Acc = 0.95 (3) | [49] |
Oil, gas | 1 | SVM, RF | Acc = 0.9746 | [50] |
Uranium | 4 | ANN, XGB | R = 0.7 (XGB) | [1] |
Uranium | 4 | XGB, LGBM, RF, DFFNN, SVM | R2 = 0.710, R = 0.845 (LGBM) | [51] |
Uranium | 1 | kNN, LogR, DT, SVM, XGB, ANN, LSTM | Acc = 0.54 (XGB) | [52] |
Uranium | 5 | SVM, ANN, RF, XGB, LGBM | f1_weighted = 0.72 (XGB) | [19] |
Code | Rock Name | AR (Ohm*m) | Filtration Coefficient (m/Day) |
---|---|---|---|
1 | gravel, pebbles | medium | 12–20 |
2 | coarse sand | medium | 8–15 |
3 | medium-grained sand | medium | 5–12 |
4 | fine-grained sand | medium | 1–7 |
5 | sandstones | high | 0–0.1 |
6 | silt, siltstone | low | 0.8–1 |
7 | clay | low | 0.1–0.8 |
8 | gypsum, dolomite | high | 0–0.1 |
9 | carbonate rocks | high | 0–0.1 |
№ | Classifier | Acc | f1_Score_Class1 | f1_Score_Class2 | f1_Score_Class8 | f1_Score_Macro | f1_Score_Micro | Duration |
---|---|---|---|---|---|---|---|---|
1 | LGBM | 0.868 | 0.88 | 0.668 | 0.891 | 0.813 | 0.868 | 5.757 |
2 | RFC | 0.844 | 0.859 | 0.57 | 0.869 | 0.766 | 0.844 | 94.193 |
3 | XGB | 0.859 | 0.87 | 0.657 | 0.882 | 0.803 | 0.859 | 161.549 |
4 | kNN | 0.666 | 0.71 | 0.396 | 0.658 | 0.588 | 0.666 | 134.631 |
5 | DT | 0.721 | 0.762 | 0.419 | 0.753 | 0.645 | 0.721 | 18.003 |
6 | MLP | 0.809 | 0.825 | 0.628 | 0.825 | 0.759 | 0.809 | 54.788 |
7 | NB | 0.479 | 0.404 | 0.279 | 0.678 | 0.454 | 0.479 | 0.98 |
8 | Linear SVM | 0.750 | 0.775 | 0.461 | 0.757 | 0.664 | 0.750 | 3322.33 |
9 | RBF SVM | 0.799 | 0.812 | 0.560 | 0.818 | 0.730 | 0.799 | 1156.59 |
Dw_N | Acc | f1_Score_Class1 | f1_Score_Class2 | f1_Score_Class8 | f1_Score_Macro | f1_Score_Micro | Duration |
---|---|---|---|---|---|---|---|
0 | 0.79 | 0.828 | 0.505 | 0.685 | 0.672 | 0.79 | 1.651 |
5 | 0.825 | 0.843 | 0.636 | 0.698 | 0.726 | 0.825 | 6.386 |
25 | 0.82 | 0.839 | 0.656 | 0.69 | 0.729 | 0.82 | 15.56 |
50 | 0.827 | 0.842 | 0.664 | 0.696 | 0.734 | 0.827 | 26.56 |
100 | 0.836 | 0.847 | 0.67 | 0.723 | 0.747 | 0.836 | 48.94 |
200 | 0.837 | 0.815 | 0.68 | 0.73 | 0.741 | 0.837 | 73.61 |
Dw_N | Acc | f1_Score_Class1 | f1_Score_Class2 | f1_Score_Class8 | f1_Score_Macro | f1_Score_Micro | Duration |
---|---|---|---|---|---|---|---|
0 | 0.779 | 0.803 | 0.498 | 0.647 | 0.649 | 0.779 | 0.902 |
5 | 0.807 | 0.805 | 0.633 | 0.659 | 0.699 | 0.807 | 2.503 |
25 | 0.808 | 0.805 | 0.661 | 0.673 | 0.713 | 0.808 | 5.833 |
50 | 0.814 | 0.815 | 0.668 | 0.681 | 0.721 | 0.814 | 10.47 |
100 | 0.818 | 0.815 | 0.668 | 0.697 | 0.727 | 0.818 | 18.99 |
200 | 0.826 | 0.815 | 0.679 | 0.739 | 0.744 | 0.826 | 31.5 |
F1_Score | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Dw_n | Acc | 1 | 3 | 4 | 5 | 6 | 7 | 9 | F1_Macro | F1_Micro | Duration |
5 | 0.565 | 0.705 | 0.292 | 0.159 | 0.433 | 0 | 0.623 | 0 | 0.344 | 0.565 | 120.973 |
10 | 0.577 | 0.715 | 0.348 | 0.19 | 0.337 | 0.089 | 0.628 | 0 | 0.359 | 0.577 | 114.243 |
25 | 0.609 | 0.744 | 0.392 | 0.21 | 0.263 | 0.028 | 0.671 | 0 | 0.364 | 0.609 | 260.255 |
50 | 0.636 | 0.768 | 0.415 | 0.243 | 0.301 | 0.005 | 0.683 | 0 | 0.381 | 0.636 | 435.202 |
100 | 0.659 | 0.791 | 0.441 | 0.247 | 0.308 | 0 | 0.692 | 0 | 0.391 | 0.659 | 732.618 |
200 | 0.694 | 0.819 | 0.492 | 0.295 | 0.28 | 0.008 | 0.705 | 0 | 0.41 | 0.694 | 1319.534 |
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Mukhamediev, R.; Kuchin, Y.; Yunicheva, N.; Kalpeyeva, Z.; Muhamedijeva, E.; Gopejenko, V.; Rystygulov, P. Classification of Logging Data Using Machine Learning Algorithms. Appl. Sci. 2024, 14, 7779. https://doi.org/10.3390/app14177779
Mukhamediev R, Kuchin Y, Yunicheva N, Kalpeyeva Z, Muhamedijeva E, Gopejenko V, Rystygulov P. Classification of Logging Data Using Machine Learning Algorithms. Applied Sciences. 2024; 14(17):7779. https://doi.org/10.3390/app14177779
Chicago/Turabian StyleMukhamediev, Ravil, Yan Kuchin, Nadiya Yunicheva, Zhuldyz Kalpeyeva, Elena Muhamedijeva, Viktors Gopejenko, and Panabek Rystygulov. 2024. "Classification of Logging Data Using Machine Learning Algorithms" Applied Sciences 14, no. 17: 7779. https://doi.org/10.3390/app14177779