Coal Body Structure Detection Based on Logging and Seismic Data and Its Impacts on Coalbed Methane Development: A Case Study in the Dahebian Block, Western Guizhou, Southern China

Shu, Yongqian; Sang, Shuxun; Zhou, Xiaozhi; Zhao, Fangming

doi:10.1007/s11053-023-10168-6

Cited by 5 publications

(1 citation statement)

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“… 4 Early research on coal texture prediction based on logging curves primarily employed cluster analysis 5 and the Protodyakonov’s coefficient method. 6 Subsequently, methods such as Archie’s formula, 7 coal texture index, 8 brittleness index, 9 geological strength index (GSI), 10 K-means algorithm, 11 principal component analysis, 12 neural network, 13 , 14 Fisher discriminant analysis, 15 and other quantitative classification techniques were gradually introduced. Although many logging interpretation models for coal texture have already been built, there are still some shortcomings in coal texture correction and interpretation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method of Coal Texture Considering Longitudinal Resolution of Logging Curves and Its Application─Taking No. 15 Coal Seam in the Shouyang Block as an Example

et al. 2023

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The quantitative identification of the coal texture is of great importance as a crucial parameter for coalbed methane (CBM) reservoir evaluation. This study combined drilling core data, electrical imaging logging data, and four conventional logging data, namely, compensation density (DEN), natural γ (GR), deep lateral resistivity (RD), and acoustic time difference (AC), to achieve accurate inversion of coal texture in the Shouyang Block. Meanwhile, wavelet analysis and Fisher discriminant analysis were introduced to the inversion process to further improve the accuracy. Through the utilization of software packages, such as Matlab and SPSS, the establishment of the coal texture logging interpretation chart of the No. 15 coal seam in the Shouyang block was successfully realized. The outcome of this comprehensive study reveals that the coal texture logging interpretation chart is an effective tool for the identification and classification of each coal texture and gangue. Moreover, the validity and reliability of this method were tested and confirmed using wells CS-8 and CS-9 in the region, achieving an accuracy of 97.1 and 93.2%, respectively. This innovative method has significant prospects for predicting and evaluating the coal texture in the Shouyang Block, which can be further applied to other regions.

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