A Cement Bond Quality Prediction Method Based on a Wide and Deep Neural Network Incorporating Embedded Domain Knowledge
Cement bond quality is critical to ensuring the long-term safety and structural integrity of oil and gas wells. However, due to the complex interdependencies among geological conditions, operational parameters, and fluid properties, accurately predicting cement bond quality remains a considerable ch...
Saved in:
| Main Authors: | Rengguang Liu, Jiawei Yu, Luo Liu, Zheng Wang, Shiming Zhou, Zhaopeng Zhu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/10/5493 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Relational Knowledge Prediction via Dynamic Bi-Mode Embedding
by: Yang Fang, et al.
Published: (2018-01-01) -
SHEAR BOND STRENGTH OF LITHIUM DISILICATE AND HYBRID CERAMIC WITH THREE TYPES OF LUTING CEMENT (A PILOT STUDY)
by: Mariana Yankova, et al.
Published: (2025-07-01) -
Using Optimized Sulphoaluminate Cement to Enhance the Early Strength of Cement-Treated Aggregate Base for Rapid Traffic Opening
by: Lingxiang Kong, et al.
Published: (2025-06-01) -
Impact of Immediate Dentin Sealing With Various Universal Adhesives on Shear Bond Strength of Dual‐Cure Resin Cement
by: Malin Janson, et al.
Published: (2025-08-01) -
Knowledge graph completion based on iteratively learning embeddings and noise-aware rules
by: Jinglin Zhang, et al.
Published: (2025-07-01)