APPLICATION OF MACHINE LEARNING METHODS AND NEURAL NETWORKS IN THE OBJECTIVES OF LITHOFACIES MAPPING AND RESERVOIR PROPERTIES ASSESSMENT: ANALYSIS AND SELECTION OF METHODS
ПРИМЕНЕНИЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ И НЕЙРОННЫХ СЕТЕЙ В ЗАДАЧАХ КАРТИРОВАНИЯ ЛИТОФАЦИЙ И ОЦЕНКИ КОЛЛЕКТОРСКИХ СВОЙСТВ: АНАЛИЗ И ВЫБОР МЕТОДОВ
LİTOFASİYALARIN XƏRİTƏLƏNDİRİLMƏSİ VƏ KOLLEKTOR XÜSUSİYYƏTLƏRİNİN QİYMƏTLƏNDİRİLMƏSİ TAPŞIRIQLARINDA MAŞIN ÖYRƏNMƏSİ VƏ NEYRON ŞƏBƏKƏ METODLARININ TƏTBİQİ: METODLARIN TƏHLİLİ VƏ SEÇİMİ
Abetov A.E. Seitzhanov A.K. Samenov Y.R.
2025Geology and Geophysics Institute at Azerbaijan National Academy of Sciences (ANAS)
ANAS Transactions, Earth Sciences
2025Issue 142 - 51 pp.
The paper discusses the application of artificial intelligence (AI) methods to address challenges in lithofacies mapping and the assessment of reservoir properties. The choice of an AI method depends on the nature of the data, objectives of the study (such as classification, regression, clustering, or image segmentation), and requirements on the final interpretation and modeling results. An analysis of various machine learning (ML) algorithms including the support vector machine (SVM), random forest (RF), neural networks, etc. were conducted. Evaluated effectiveness of each method was evaluated on the basis of open-source data and geological datasets. Advantages and disadvantages of these methods were analyzed and factors influencing on the selection of an appropriate AI method were identified. Classification of geological problems and corresponding AI methods, encompassing SVM, RF, linear and polynomial regression, k-means clustering, hierarchical clustering, and convolutional neural networks (CNN) were presented. The article also introduces open-source ML platforms such as TensorFlow, PyTorch, and Keras along with factors influencing on the selection of the optimal AI method for lithofacies analysis and reservoir property assessment. Recommendations to select the most suitable AI methods for specific objectives were provided. The importance of data volume and quality in selection of AI method and prevention of model overfitting was emphasized.
artificial intelligence , classification of geological problems , clusterization of lithofacies , linear and polynomial regression , machine learning , reservoir properties
Text of the article Перейти на текст статьи
Satbayev University, 22, Satpayev Str., Almaty, 050013, Kazakhstan
Kazakh-British Technical University, 59, Tole Bi Str., Almaty, 050000, Kazakhstan
Satbayev University
Kazakh-British Technical University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026