Digital core reconstruction of tight carbonate rocks based on SliceGAN
The pore structures of the Majiagou Formation in the Ordos Basin are complex, featuring micro- and nano-scale intra-crystalline and inter-crystalline pores that significantly impact hydrocarbon storage and flow. Precisely characterizing the rock internal structures is crucial for reservoir exploration and development. However, it is difficult to accurately characterize the pore structure of rock using traditional imaging methods to meet the simulation requirements. In this context, this study focuses on high-resolution 3D digital core reconstruction using the SliceGAN model. Specifically, the Modular Automated Processing System (MAPS) image and Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) image were combined to divide MAPS into three categories: pore, dolomite, and calcite. Then, through the SliceGAN algorithm, the 3D digital core was reconstructed. To evaluate the reconstruction, the auto-correlation function, two-point probability function, porosity, mineral content, and specific surface area were employed. The results show that the SliceGAN can effectively capture the micro-features in the core, and the internal structure of the generated core was consistent with that of the original core. This study provided a new sight for reconstructing cores with complex pore structures and strong heterogeneity and innovatively supports tight carbonate reservoir characterization and evaluation. • The high-resolution MAPS and QEMSCAN images were used as raw data, the SliceGAN model was employed to reconstruct the 3D core. • The effectiveness of the reconstructed core was evaluated using the auto-correlation function, two-point probability function and Minkowski. • This method not only solved the dimension-expansion but also provided a new sight for reconstructing cores with complex pore structures and strong heterogeneity.
Authors
- Fang‐Zhen Teng (ORCID: https://orcid.org/0000-0003-3415-6137)
- Ying Zhou (ORCID: https://orcid.org/0000-0001-9368-3979)
- Xin Nie (ORCID: https://orcid.org/0000-0003-1820-9999)
- Wenjing Zhang (ORCID: https://orcid.org/0000-0002-6076-8017)
- Taiping Zhao
Institutions
- Yangtze University (CN)
- Research Institute of Petroleum Exploration and Development (CN)
Publication Details
- Journal
- Artificial Intelligence in Geosciences
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1016/j.aiig.2025.100116
- Citations
- 2
- Primary Topic
- Hydrocarbon exploration and reservoir analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.73
Funders
- National Natural Science Foundation of China