Deep Learning-based Segmentation Method for Organic Matter Identification in Oil Shale CT Images

Abstract

Purposes The density of organic matter in oil shale is much lower than that of other rock matrix, so the gray value of organic matter in CT images is often close that of pore fractures, which results in problems such as inconspicuous difference in gray value and blurred boundary between organic matter and rock in the images. In order to accurately identify the organic matter in the segmented oil shale CT images, the image segmentation methods in the field of deep learning are studied, and the OM-Unet semantic segmentation network architectures describing the organic matter segmentation is built independently. Methods By introducing a hybrid null convolution module, a coarse-to-fine deployment strategy, and a lightweight adaptive feature fusion module into the traditional Unet model, the convolutional neural network is used to identify and segment organic matter in oil shale CT images, and its segmentation effect is evaluated by combining MIoU and other evaluation indexes. Findings The MIoU of the OM-Unet model is 80.66%, which is higher than that of the three-phase segmentation methods, Unet, CBAM-Unet, DeepLabV3, HDC-Unet, and LAFF-Unet models by 8.01%, 17.68%, 9.5%, 2.54%, 2.83%, and 9.13%, respectively. The MPA of OM-Unet model is 89.16%, which is higher than that of the three-phase segmentation method, Unet, CBAM-Unet, DeepLabV3, HDC-Unet, and LAFF-Unet models by 12.85%, 20.62%, 15.82%, 8.81%, 9.55%, and 15.34%, respectively. Conclusions The results demonstrate that the OM-Unet model can effectively improve the accuracy of oil shale organic matter partitioning, more accurately determine the variation patterns of organic matter volume percentage and organic matter cluster number with temperature or pyrolysis conditions, and provide basic theoretical data for in situ oil shale development.

Keywords

• deep learning
• oil shale
• organic matter
• hybrid hole convolution
• semantic segmentation