Prediction on rock strength by mineral composition from machine learning of ECS logs
Rock strength evaluation is critical in oil and gas exploration, but traditional methods, such as empirical formulas, laboratory tests, and numerical simulations, often struggle with accuracy, generalizability, and alignment with field conditions. This study proposes the use of Random Forest and Tra...
Saved in:
| Main Authors: | Dongwen Li, Xinlong Li, Li Liu, Wenhao He, Yongxin Li, Shuowen Li, Huaizhong Shi, Gaojian Fan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-06-01
|
| Series: | Energy Geoscience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666759225000071 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder
by: JIA Ruilong, et al.
Published: (2024-08-01) -
Characterization of dip effect on strength for gently inclined rock pillar
by: Lijun Sun, et al.
Published: (2025-07-01) -
Benchmark study of three statistical methods for six intact rock failure criteria constrained by different rock strength data
by: Peng-fei He, et al.
Published: (2025-10-01) -
Research on the size effect of rock-filled concrete compressive strength: Considering the influence of rockfill ratios and rock shapes
by: Li-xiu Wu, et al.
Published: (2025-12-01) -
Learning System of Research Ethics for EC Members in Bangladesh
by: Md. Monowarul Islam, et al.
Published: (2025-07-01)