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🧠 AI⚪ NeutralImportance 4/10
A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments
arXiv – CS AI|Si-Yu Xiao, Xin-Di Zhao, Xiang-Zhan Wang, Tian-Hao Mao, Ying-Kai Liao, Xing-Yu Liao, Yu-Qiao Chen, Jun-Jie Wang, Shuang Liu, Tu-Pei Chen, Yang Liu||3 views
🤖AI Summary
Researchers developed Collar Recognition Nets (CRNs), lightweight neural networks for real-time recognition of casing collar signatures in downhole oil/gas operations. The system achieves 97.2% accuracy with only 1,985 parameters and processes 1,000 inferences per second on embedded ARM hardware.
Key Takeaways
- →CRNs use 1-D convolutional neural networks optimized for resource-constrained downhole environments with depthwise separable convolutions.
- →The system achieves an F1-score of 0.972 on field data while using only 1,985 parameters and 8,208 MACs.
- →Deployed on ARM Cortex-M7 hardware, the model processes 1,000 inferences per second with 343.2 microsecond latency.
- →The solution addresses magnetic interference issues that corrupt traditional casing collar locator signals.
- →Real-time autonomous collar recognition enables more accurate depth control for critical operations like perforation.
#neural-networks#embedded-ai#oil-gas#real-time-processing#edge-computing#industrial-ai#tensorflow-lite#downhole-technology
Read Original →via arXiv – CS AI
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