# Classification of Logging Data Using Machine Learning Algorithms
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extracted Resources** & **Task** & **Model** & **Results** & **Ref.** \\ \hline Oil & 1, 2 & Bidirectional LSTM & Acc = 92.69\% & [36] \\ \hline Oil, gas & 1 & DT, RF & f1 = 0.97 (RF), f1 = 0.94 (DT) & [37] \\ \hline Oil & 1, 2 & UL, SL & UL = 80\%, SL = 90\% & [22] \\ \hline Oil & 1, 2 & XGBoost and RF & Acc = 0.882 (XGB) & [38] \\ \hline Oil, gas & 1 & kNN, RF, XGB, MLP & Acc = 0.79 & [39] \\ \hline Coal & 1 & XGB, RF, ANN & Acc = 0.99 (RF) & [40] \\ \hline Oil & 4 & DFFNN, XGB, LR & R\({}^{2}\) = 0.9551 (LR) & [41] \\ \hline Oil, gas & 1 & ANN & Acc = 0.88 (ANN) & [11] \\ \hline Oil, gas & 1 & Hybrid model based on CNN and LSTM & Acc = 87.3\% (CNN-LSTM) & [42] \\ \hline Oil, gas & 2 & XGB, LogR & ROC AUC = 0.824 & [43] \\ \hline Coal & 1 & LR, SVM, ANN, RF, XGB & Acc \textgreater{} 0.9 & [44] \\ \hline Coal & 1 & SVM, MLP, DT, RF, XG & Acc = 0.8 & [7] \\ \hline Oil, gas & 1 & MLP, SVM, XGB, RF & Acc = 0.868 (XGB) and Acc = 0.884 (RF) & [45] \\ \hline Geothermal wells & 1 & kNN, SVM, XGB & Acc = 0.9067 (XGB) & [9] \\ \hline \multirow{2}{*}{Sulfide ore} & \multirow{2}{*}{1} & \multirow{2}{*}{RF} & R \textgreater{} 0.66 between calculated and \\  & & & measured Na concentration in core & [46] \\ \hline Oil & 1 & UL & Acc = 0.5840 & [47] \\ \hline Oil & 1 & RF & F1 = 0.913 & [48] \\ \hline Uranium & 1, 3 & RF, kNN, XGB & Acc = 0.65 (1), Acc = 0.95 (3) & [49] \\ \hline Oil, gas & 1 & SVM, RF & Acc = 0.9746 & [50] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of applying machine learning models to some well logging data processing tasks. Types of solved problems in the Table 2:  
1. Lithological classification. 2. The identification of reservoirs. 3. Stratigraphic classification. 4. The estimation of rock permeability. 5. The identification of reservoir oxidation. The analysis shows that the majority of works on the topic of logging data interpretation are devoted to reservoir identification and lithological classification at oil and gas fields. The characteristic features of this task are the large thickness of the identified layers (tens of meters) and their stability within the horizon. Sometimes they are even assigned alphanumeric designations, for example, in [41]. Moreover, at least 6-7 geophysical parameters recorded during logging are usually analyzed, including electrical, neutron, and gamma methods. All these factors allow for achieving the classification accuracy at oil and coal deposits of more than 80-90%. The task of interpreting the logging data at sandstone-type uranium deposits has a number of significant differences:  
1. 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]. 2. 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]. 3. Difficulties with extracting and tying the core due to the characteristics of the section (sand and clay). 4. Use of experts' assessments, which contain a significant degree of subjectivity [16]. 5. The regulatory framework, interpretation methods, and standard set of logging methods were inherited from the USSR and underwent only minor changes in Kazakhstan. 6. 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.