title: ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð½ÐµÑÑÑÐ½Ð¾Ð¹ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾ÑÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Data mining in the oil industry
Creators: name: ÐÑÐ°Ð½Ð°ÑÑÐµÐ²Ð°, ÐÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
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description: The subject of the graduate qualification work is Â«Data mining in the oil industryÂ». The given work is devoted to the study of various machine learning methods to accelerate the interpretation of well logging data. The research set the following goals: 1. Review relevant literature materials for research in the chosen field, including articles, abstracts, educational materials and manuals. 2. Review of tasks in the oil industry, performed by machine learning methods. 3. Analyze the main methods of data mining. 4. Build multiclass and binary classification models for interpreting data using various data analysis algorithms. 5. Analyze the results of applying classification methods. A comparative analysis of mining methods was carried out for the classification of rocks and reservoirs based on well logging data of 20 the Norwegian shelf wells. The work was carried out using the built-in functions of the sklearn library of the Python language. Based on the processed data, 6 machine learning models were built: a naive Bayesian classifier, support vector machines, decision trees, a random forest, logistic regression, and the k-nearest neighbor method. As a result of comparison of the accuracy of prediction and training time of the models, the method of logistic regression, decision tree and random forest were determined by the optimal methods.