Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method

The coal seams of the Permian Taiyuan-Shanxi formations in the Baode block of the northeastern margin of the Ordos Basin have abundant coalbed methane resources. However, the productivity varies greatly among wells, mainly attributed to the strong heterogeneity caused by regional differences in reservoir brittleness. Rock mechanical parameter method is commonly used to evaluate reservoir brittleness. Studying rock mechanical parameters and brittleness can provide an important basis for fracturing modification. However, current methods mostly rely on empirical formulas, leading to limited evaluation accuracy. In this study, a convolutional neural network (CNN) was utilized to construct a conversion model between experimentally obtained elastic modulus, Poisson's ratio, and multi-logging curves. Based on this method, rock mechanical profiles were further established, enabling quantitative evaluation of brittleness. The results indicated that CNN-based predictions of rock mechanical parameters had good applicability for coal-bearing layers. The brittleness indices the main coal seams, 4+5# and 8+9#, in the Baode block were generally low. The brittleness index of the 4+5# seam was slightly higher than that of the 8+9# seam. Both seams exhibited similar spatial distributions, with low brittleness values in the central and southeastern parts of the study area. Differences in mineral composition affected rock brittleness. Higher quartz content was linearly correlated with greater elastic modulus and brittleness index.