Real-time Casing Collar Recognition with Embedded Neural Networks
Paper#AI in Oil and Gas🔬 Research|Analyzed: Jan 3, 2026 19:27•
Published: Dec 28, 2025 12:19
•1 min read
•ArXivAnalysis
This paper addresses a practical problem in oil and gas operations by proposing an innovative solution using embedded neural networks. The focus on resource-constrained environments (ARM Cortex-M7 microprocessors) and the demonstration of real-time performance (343.2 μs latency) are significant contributions. The use of lightweight CRNs and the high F1 score (0.972) indicate a successful balance between accuracy and efficiency. The work highlights the potential of AI for autonomous signal processing in challenging industrial settings.
Key Takeaways
- •Proposes a real-time casing collar recognition system using embedded neural networks.
- •Employs lightweight 'Collar Recognition Nets' (CRNs) optimized for resource-constrained environments.
- •Achieves high accuracy (F1 score of 0.972) with low computational complexity (8,208 MACs).
- •Demonstrates real-time performance with an average inference latency of 343.2 μs.
- •Highlights the feasibility of autonomous signal processing in downhole instrumentation.
Reference / Citation
View Original"By leveraging temporal and depthwise separable convolutions, our most compact model reduces computational complexity to just 8,208 MACs while maintaining an F1 score of 0.972."