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.
翻译：在油气作业中，精确定位井下位置至关重要，但传统基于地面的套管接箍定位器（CCL）监测常因信号衰减而影响精度。为此，我们提出了一种采用嵌入式神经网络的原位实时接箍识别系统。我们引入了专为资源受限的ARM Cortex-M7微处理器优化的轻量级“接箍识别网络”（CRNs）。通过利用时序卷积和深度可分离卷积，我们最紧凑的模型将计算复杂度降低至仅8,208次乘加运算（MACs），同时保持0.972的F1分数。硬件验证证实平均推理延迟为343.2微秒，表明在井下仪器严苛的功耗与空间限制下，实现鲁棒、自主的信号处理是可行的。
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