[2512.22901] A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments

Electrical Engineering and Systems Science > Systems and Control arXiv:2512.22901 (eess) [Submitted on 28 Dec 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments Authors: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 View a PDF of the paper titled A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments, by Si-Yu Xiao and 10 other authors View PDF HTML (experimental) 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 com...