1 河海大学地球科学与工程学院, 南京211100；2 National Institute of Oceanography and Applied Geophysics (OGS), Trieste, Italy 34010；3 中国石油集团东方地球物理勘探有限责任公司物探技术研究中心，涿州072751
Prediction of carbonate permeability from multiresolution CT scans and deep learning
Zhang Lin, Chen Guang-dong, Ba Jing*, José M. Carcione, Xu Wen-hao, and Fang Zhi-jian
1 School of Earth Science and Engineering, Hohai University, Nanjing 211100
2 National Institute of Oceanography and Applied Geophysics (OGS), Trieste, Italy 34010
3 BGP INC., China National Petroleum Corporation, Zhuozhou 072751, China
Abstract：
The low-resolution CT scan images obtained from drill core generally struggle with problems such as insufficient pore structure information and incomplete image details. Consequently, predicting the permeability of heterogeneous reservoir cores relies heavily on high-resolution CT scanning images. However, this approach requires a considerable amount of data and is associated with high costs. To solve this problem, a method for predicting core permeability based on deep learning using CT scan images with different resolutions is proposed in this work. First, the high-resolution CT scans are preprocessed and then cubic subsets are extracted. The permeability of each subset is estimated using the Lattice Boltzmann Method (LBM) and forms the training set for the convolutional neural network (CNN) model. Subsequently, the highresolution images are downsampled to obtain the low-resolution grayscale images. In the comparative analysis of the porosities of different low-resolution images, the low-resolution image with a resolution of 10% of the original image is considered as the test set in this paper. It is found that the permeabilities predicted from the low-resolution images are in good agreement with the values calculated by the LBM. In addition, the test data are compared with the results of the Kozeny-Carman (KC) model and the measured permeability of the whole sample. The results show that the prediction of the permeability of tight carbonate rock based on deep learning using CT scans with different resolutions is reliable.
作者简介: Zhang Lin received his Ph.D. degree in Exploration Geophysics from Hohai University in 2020, and is working as a associate professor in the School of Earth Sciences and Engineering, Hohai University, since 2020. His research interests are the elastic wave propagation theories of porous media and pore microstructure characterization.
2024, Vol. 21 
