Intelligent lithologic identification of sandy conglomerate reservoirs in District No.7 of Karamay oilfield
The sandy conglomerate reservoirs in Karamay are characterized by diverse lithology and interlayers. The cost of the conventional coring methods is high, and the identification accuracy in non-coring section is low, which leads to difficulty in reservoir classification. In order to achieve rapid and...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Science Press (China Science Publishing & Media Ltd.)
2023-05-01
|
| Series: | Shenzhen Daxue xuebao. Ligong ban |
| Subjects: | |
| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2525 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The sandy conglomerate reservoirs in Karamay are characterized by diverse lithology and interlayers. The cost of the conventional coring methods is high, and the identification accuracy in non-coring section is low, which leads to difficulty in reservoir classification. In order to achieve rapid and accurate identification of lithology, the lithology of the target area is classified into mudstone, coarse sandstone, medium-fine sandstone, coarse conglomerate, medium-fine conglomerate, and coal seam based on geological data. Firstly, the principal component analysis method is adopted to establish the lithology identification cross plot based on sensitivity analysis of well log data with an accuracy rate of 81.37%. Secondly, a lithology identification model is proposed based on the combination of k-means synthetic minority oversampling technique (KMSMOTE) and random forest to improve minority identification accuracy. The model improves the identification accuracy to achieve the accuracy of about 92.94% for oversampling minority samples. The two methods are applied to adjacent wells for comparative analysis of lithology identification. The accuracy of the KMSMOTE random forest is 95.71%, better than that of the cross plot method of 82.91%. The accuracy of minority sample identification is higher than that of the traditional random forest model, with excellent applicability and generalization ability on imbalanced lithology identification. The research results provide a new intelligent idea for imbalanced lithology sample identification. |
|---|---|
| ISSN: | 1000-2618 |