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Multi-scale reconstruction of porous media from low-resolution core images using conditional generative adversarial networks Научная публикация

Журнал Journal of Natural Gas Science and Engineering
ISSN: 1875-5100
Вых. Данные Год: 2022, Том: 99, Номер статьи : 104411, Страниц : DOI: 10.1016/j.jngse.2022.104411
Ключевые слова Deep learning; Digital rock; Generative adversarial network; Multi-scale; Porous media reconstruction
Авторы Yang Y. 1,2 , Liu F. 1,2 , Yao J. 1,2 , Iglauer S. 3 , Sajjadi M. 4 , Zhang K. 2 , Sun H. 1,2 , Zhang L. 1,2 , Zhong J. 1,2 , Lisitsa V. 5
Организации
1 Key Laboratory of Unconventional Oil & Gas Development (China University of Petroleum (East China)), Ministry of Education, Qingdao, 266580, China
2 Research Center of Multiphase Flow in Porous Media, School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580, China
3 Petroleum Engineering Department, Edith Cowan University, Joondalup, WA 6027, Australia
4 Institute of Petroleum Engineering, College of Engineering, University of Tehran, Tehran, 1439956191, Iran
5 Institute of Mathematics SB RAS, Novosibirsk, 630090, Russian Federation

Информация о финансировании (1)

1 Министерство науки и высшего образования РФ
Математический центр в Академгородке
075-15-2019-1613, 075-15-2022-281

Реферат: Various rocks such as carbonate, coal or shale contain both micro- and macro-pores. To accurately predict the fluid flow and mechanical properties of these porous media, a multi-scale characterization of the pore space is of key importance. Hybrid superposition methods perform well in such multi-scale reconstructions, however, input images with two resolutions (high and low) and different reconstruction methods are required. In addition, the superposition algorithms are complex and human factors can introduce serious bias. Here we thus propose an effective approach based on conditional generative adversarial network (cGAN) for efficient and reliable multi-scale digital rock reconstruction based only on low-resolution core images. High-resolution core images with narrow field of view (FOV) and their corresponding large structure images were thus used to train the cGAN model. The model was validated with real sample images, and the model-generated images exhibited great agreement with the real pore structures. We also demonstrate that the proposed method can generate images independent of the structure size to some extent. This work provides an advanced image-generating model based on deep learning, and therefore aids in better and wider pore-scale characterization and process modeling, to improve understanding of subsurface science and engineering processes. © 2022 Elsevier B.V.
Библиографическая ссылка: Yang Y. , Liu F. , Yao J. , Iglauer S. , Sajjadi M. , Zhang K. , Sun H. , Zhang L. , Zhong J. , Lisitsa V.
Multi-scale reconstruction of porous media from low-resolution core images using conditional generative adversarial networks
Journal of Natural Gas Science and Engineering. 2022. V.99. 104411 . DOI: 10.1016/j.jngse.2022.104411 WOS Scopus РИНЦ OpenAlex
Идентификаторы БД:
Web of science: WOS:000788705600005
Scopus: 2-s2.0-85122629986
РИНЦ: 47910399
OpenAlex: W4205768804
Цитирование в БД:
БД Цитирований
Scopus 42
Web of science 32
OpenAlex 46
РИНЦ 30
Альметрики: