A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments

Accurate downhole positioning is critical in oil and gas operations but is often compromised by signal degradation in traditional surface-based Casing Collar Locator (CCL) monitoring. To address this, we present an in-situ, real-time collar recognition system using embedded neural network. We introduce lightweight "Collar Recognition Nets" (CRNs) optimized for resource-constrained ARM Cortex-M7 microprocessors. 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. Hardware validation confirms an average inference latency of 343.2 μs, demonstrating that robust, autonomous signal processing is feasible within the severe power and space limitations of downhole instrumentation.