Convolutional autoencoder network lithology recognition based on scratch tests.

ABSTRACT: To address the characteristic of frequent lithological alternations in the continental shale of the Songliao Basin in China and meet the refined requirements of reservoir modeling, it is necessary to establish a higher-precision lithology identification method. This study conducted scratch tests on shale reservoir cores from the 2360m-2409m interval of the Qingshankou Formation in the Songliao Basin, Jilin, obtaining nine mechanical characteristic parameters, including hardness, compressive strength, and Poisson's ratio. By integrating convolutional neural network (CNN) and auto-encode network (AE), a novel lithology identification method based on scratch data was proposed. The optimal lithology identification scale was selected, and the performance of this method was compared with that of

SUBMITTER: Wang S 

PROVIDER: S-EPMC12365223 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature