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

TL;DR

This paper introduces lightweight neural networks called CRNs for real-time, autonomous casing collar recognition in downhole environments, achieving high accuracy with minimal computational resources.

Contribution

The paper presents a domain-specific neural network architecture optimized for real-time collar detection with low power and size constraints in downhole tools.

Findings

Achieved an F1-score of 0.972 on field data.

Model runs at 1,000 inferences per second on embedded hardware.

Uses only 1,985 parameters and 8,208 MACs.

Abstract

Casing collar locator (CCL) measurements are widely used as reliable depth markers for positioning downhole instruments in cased-hole operations, enabling accurate depth control for operations such as perforation. However, autonomous collar recognition in downhole environments remains challenging because CCL signals are often corrupted by toolstring- or casing-induced magnetic interference, while stringent size and power budgets limit the use of computationally intensive algorithms and specific operations require real-time, in-situ processing. To address these constraints, we propose Collar Recognition Nets (CRNs), a family of domain-specific lightweight 1-D convolutional neural networks for collar signature recognition from streaming CCL waveforms. With depthwise separable convolutions and input pooling, CRNs optimize efficiency without sacrificing accuracy. Our most compact model achieves an F1-score of 0.972 on field data with only 1,985~parameters and 8,208~MACs, and deployed on an ARM Cortex-M7 based embedded system using TensorFlow Lite for Microcontrollers (TFLM) library, the model demonstrates a throughput of 1,000 inference per second and 343.2 {\mu}s latency, confirming the feasibility of robust, autonomous, and real-time collar recognition under stringent downhole constraints.