Classification of Well Log Data Using Vanishing Component Analysis

Lithium in coal, as a new type of associated mineral resource, has considerable potential for exploration. Exploration of high-lithium coal seams is essential for developing and using the associated lithium resources. To explore the distribution of lithium resources in the early stages of development in coal seams, the relationship between coal seam logging data and lithium content was analyzed by taking Guojiadi Coal Mine (China) as example. By analyzing the correlation between the different logging curves and the lithium content in coal and combining the K-means algorithm to identify the logging characteristics of different lithium-containing coal seams, we finally obtained the logging identification characteristics of high-lithium coal seams. The results reveal differences in the logging curves of coal seams with different lithium contents. The natural gamma and lateral resistivity of high-lithium coal seams are approximately 80 API and 100 Ω.M, respectively. Our study shows that the early identification of high-lithium coal seams can be evaluated from a logging perspective. We propose a preliminary identification method of high-lithium coal seam based on logging curve parameters by clustering analysis of borehole logging data to achieve accurate prediction.

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Classification of Well Log Data Using Vanishing Component Analysis

Cited by 2 publications

References 17 publications

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Logging response prediction of high-lithium coal seam based on K-means clustering algorithm

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