Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network
Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations...
Saved in:
| Main Authors: | Kai Bai, Siyi Jin, Zhaoshuo Zhang, Shengsheng Dai |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/21/6966 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting depression risk in middle-aged and elderly adults in China using CNN-BiLSTM-Attention mechanism and LSTM+SHAP framework
by: Shengxian Bi, et al.
Published: (2025-08-01) -
An attention based hybrid approach using CNN and BiLSTM for improved skin lesion classification
by: Ayesha Shaik, et al.
Published: (2025-05-01) -
Paraphrase detection for Urdu language text using fine-tune BiLSTM framework
by: Muhammad Ali Aslam, et al.
Published: (2025-05-01) -
BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis
by: Anas W. Abulfaraj
Published: (2025-01-01) -
Prediction Model for Time-varying Safety Factor for Gravity Dam Stability Based on CNN-BiLSTM-Attention
by: CAO Yuxin, et al.
Published: (2025-04-01)