EGU25-122, updated on 14 Mar 2025
https://doi.org/10.5194/egusphere-egu25-122
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Quantitative classification evaluation model for tight sandstone reservoirs based on machine learning
1,2,- 1Department of Geology, Northwest University, Xi’an 710069, China (2462733280@qq.com)
- 2State Key Laboratory of Continental Dynamics, Northwest University, Xi’an 710069, China
- 3School of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
- 4Engineering Research Center of Development and Management for Low to Ultra-Low Permeability Oil & Gas Reservoirs in West China, Ministry of Education, Xi’an 710065, China
- 5Xi’an Key Laboratory of Tight Oil (Shale Oil) Development, Xi’an 710065, China
- 6PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, People’s Republic of China
- 7School of Petroleum Engineering and Environmental Engineering, Yan’an University, Yan’an 716000, China
How to cite: Song, X., Feng, C., Li, T., Zhang, Q., Pan, X., Sun, M., and Ge, Y.: Quantitative classification evaluation model for tight sandstone reservoirs based on machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-122, https://doi.org/10.5194/egusphere-egu25-122, 2025.