«Automation and Informatization of the fuel and energy complex»

Application of deep learning methods in actual tasks of core samples microcomputer tomography (micro-CT) processing. Solution of the inverse problem, interpolation of sparse sinograms, image slice filtration

UDC: 004.8
DOI: 10.33285/2782-604X-2023-10(603)-48-58

Authors:

¹ National University of Oil and Gas "Gubkin University", Moscow, Russia

Keywords: micro-CT, deep learning, machine learning, computer vision, digital core, inverse Radon transformation, sinogram interpolation, filtering of slice images

Annotation:

Petro-physical parameters are critical for building oil and gas field models. The predicted indicators of oil production depend on the accuracy, amount, and representativeness of petro-physical parameters, used in the mathematical models of development processes. Non-destructive methods, such as microcomputer tomography (micro-CT), allow obtaining high-resolution 3D images of rocks, which, enables more accurate assessment of petro-physical parameters. Deep learning was successfully applied in various scientific fields, including processing of micro-CT core data. This area attracts the attention of researchers. The authors of the article overview the recent results of the deep learning methods application when processing micro-CT core sinograms and post-processing of the obtained slice images.

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