| Citation: | LU Yan, LIU Zongbin, LIAO Xinwu, LI Chao, LI Yang. Automatic classification of pore structures of low-permeability sandstones based on self-organizing-map neural network algorithm[J]. Bulletin of Geological Science and Technology, 2024, 43(6): 318-330. doi: 10.19509/j.cnki.dzkq.tb20240056
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The pore system of low-permeability sandstone reservoirs is intricate, and the distribution of pore-throat sizes is highly variable. The microscopic pore structure significantly influences the reservoir′s petrophysical properties and plays a critical role in controlling fluid flow within sandstone reservoirs. Traditional approaches for evaluating pore structures primarily rely on morphological analyses of pore throat size distributions or regression analyses of pore structure parameters. These methods are significantly affected by human bias and often lack precise evaluation frameworks.
Poroperm analysis, mercury injection capillary pressure, nuclear magnetic resonance (NMR) measurements, and X-ray computed tomography (X-ray CT) scanning experiments were performed to characterize the pore structures of the E
The findings reveal that the Type Ⅰ pore structure predominantly features large pore throats, with a median throat radius (
The self-organizing map neural network algorithm effectively classifies pore structure types in cases involving multiple parameters. The classification results are not affected by inaccurate user-defined information, and there is no limitation on the number of parameters involved in the training process, making the application effect in pore structure classification remarkable. The established pore structure evaluation scheme, which is based on a self-organizing feature map neural network algorithm, is vital for investigating the microscopic seepage behavior and reservoir quality of low-permeability sandstones.
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