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.

Information and Communication Technology has been gaining importance in the economy of Kazakhstan, the largest landlocked country. In this paper, we investigate the factors that influence Kazakhstan’s e-Government portal use at the informational stage and describe the challenges encountered by citizens while using the portal. Statistical analysis is performed on a web-based questionnaire survey targeted at citizens of Kazakhstan. The technology acceptance model is used as a methodology to measure attitude towards portal usage. This paper also discusses the barriers that can restrict the successful adoption of e-Government services. The results show that awareness among citizens is high and they perceive the portal to be useful, but only a limited percentage of citizens actually use it regularly. The results of this paper could be used to help the IT managers of the portal to improve the management of informational content and maintain more effective adoption among citizens.