Classification of macerals and microfractures in deep coal seams based on ResNet:A case study of the No.8 coal seam of the Carboniferous Benxi Formation in the Ordos Basin
Macerals and microfractures are identified as important microscopic characteristics of coal reservoirs,as they are factors affecting the reservoirs'gas production capacity and mechanical properties.Based on coal samples from the No.8 coal seam of the Carboniferous Benxi Formation in deep coalbed methane wells in the Ordos Basin,we investigate the developmental characteristics of macerals and microfractures using a residual neural network(ResNet).With 305 maceral and 65 microfracture sample points from the sampled coal,we develop a ResNet-based methodology for identifying macerals and microfractures in coals,and construct an identification and classification model for macerals and microfractures in deep coal reservoirs through the inversion of microscopically observed data using the ResNet technique.The results indicate that the model is reliable,as jointly corroborated by geological characteristics and clustering algorithm-derived results.The model demonstrates a prediction accuracy of 0.90 for macerals and 0.80 for microfractures,enabling the effective prediction of macerals and microfractures in coals.The identification and prediction results of the model reveal correlations between fracture morphologies and macerals.Notably,the fracture formation is the most closely correlated with vitrinites in macerals,with the predicted fracture types and numbers agreeing well with macerals.
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