UDK 55 Геология. Геологические и геофизические науки
This article investigates the application of machine learning methods in seismic interpretation for assessing hydrocarbon potential. The study aims to analyze the effectiveness of machine learning algorithms compared to traditional geophysical data processing methods. Methods such as random forest, gradient boosting, and neural networks were used in the research. The findings demonstrate that machine learning can speed up and simplify data processing, improving the quality and speed of seismic interpretation. Special attention is given to the analysis of well logging diagrams and predicting lithology without prior data normalization. The conclusions emphasize the importance of integrating new technologies into geological exploration to enhance its efficiency.
machine learning, seismic interpretation, hydrocarbon potential, geophysical data, lithology, well logging diagrams
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