A Comprehensive and Low-Cost Wellbore Tubing and Pipeline Inspection Tool Using Electromagnetic Coupling and Machine Learning

Abstract

Abstract This work provides an advanced prototype for pipeline and wellbore tube inspection using electromagnetic (EM) resonance coupling, electromagnetic (EM) coupling, and machine learning. Utilizing only two transmitters and eight sensor coils, the described device can detect and characterize inner, outer, and total metal loss in a pipe's body. A defect in the pipe body alters the impedance of the transmitter and receiver coils and the mutual coupling between them, resulting in a relative change in one or more Rx outputs. A framework for artificial neural networks (ANN) is designed to assess the eight outputs and generate a two-dimensional map of the pipe cross-section. The ANN is trained using a finite-difference time-domain electromagnetic forward solver. The designed prototype is tested and validated through simulations and an experimental setup. The results demonstrate that the tool, with the assistance of the ANN, could not only detect single and double flaws with either complete or partial metal loss, but also define the defect's size, location, and depth.