An Impedance-Loaded Surface Acoustic Wave Corrosion Sensor for Infrastructure Monitoring.

Passive surface acoustic wave (SAW) devices are attractive candidates for continuous wireless monitoring of corrosion in large infrastructures. However, acoustic loss in the aqueous medium and limited read range usually create challenges in their widespread use for monitoring large systems such as oil and gas (O&G) pipelines, aircraft, and processing plants. This paper presents the investigation of impedance-loaded reflective delay line (IL-RDL) SAW devices for monitoring metal corrosion under O&G pipeline-relevant conditions. Specifically, we studied the effect of change in resistivity of a reflector on the backscattered signal of an RDL and investigated an optimal range through simulation. This was followed by the experimental demonstrations of real-time monitoring of Fe film corrosion in pressurized (550 psi) humid CO2 conditions. Additionally, remote monitoring of Fe film corrosion in an acidic solution inside a 70 m carbon steel pipe was demonstrated using guided waves. This paper also suggests potential ways to improve the sensing response of IL-RDLs.

Multi-parameter distributed fiber optic sensing using double-Brillouin peak fiber in Brillouin optical time domain analysis.

In this paper, we demonstrate a multi-parameter fiber sensing system based on stimulated Brillouin scattering in a double-Brillouin peak specialty fiber with enhanced Brillouin gain response. The amplitude level of the second Brillouin gain peak, which originated from the higher-order acoustic modes, has been improved with an approximately similar amplitude level to the first Brillouin gain peak from the fundamental acoustic mode. Compared to other multi-Brillouin peak fibers presented in the literature, the proposed fiber significantly reduces the measured Brillouin frequency shift error, thus improving strain and temperature accuracies. By utilizing the sensitivity values of the strain and temperature associated with each Brillouin gain spectrum (BGS) peak, a successful discriminative measurement of strain and temperature is performed with an accuracy of ±13 µÉ›, and ±0.5 °C, respectively. The proposed double-Brillouin peak fiber appears to be a possible alternative to other multi-BGS peak fibers, for instance, large effective area fiber and dispersion compensating fibers, which are inherently accompanied by large measurement errors due to the weak Brillouin gain values originating from the higher-order acoustic modes. The demonstrated results show different strain and temperature coefficients of 47 kHz/µÉ›, 1.15 MHz/°C for peak 1 and 51 kHz/µÉ›, 1.37 MHz/°C for peak 2. Moreover, the enhanced BGS peak gains having nearly the same amplitude levels enable the discriminative measurement of strain and temperature. Such fibers in Brillouin interrogation eliminate the need for complex monitoring setups and reduce measurement errors. We recommend that for long-distance natural gas pipeline monitoring, where discriminative strain and temperature measurement is crucial, the proposed double-Brillouin peak fiber can be highly beneficial.

Pilot-scale testing of natural gas pipeline monitoring based on phase-OTDR and enhanced scatter optical fiber cable.

In this paper, we present the results of lab and pilot-scale testing of a continuously enhanced backscattering, or Rayleigh enhanced fiber cable that can improve distributed acoustic sensing performance. In addition, the Rayleigh-enhanced fiber is embedded within a tight buffered cable configuration to withstand and be compatible for field applications. The sensing fiber cable exhibits a Rayleigh enhancement of 13 dB compared to standard silica single-mode fiber while maintaining low attenuation of ≤ 0.4 dB/km. We built a phase-sensitive optical time domain reflectometry system to interrogate the enhanced backscattering fiber cable both in lab and pilot-scale tests. In the laboratory experiment, we analyzed the vibration performance of the enhanced backscattering fiber cable and compared it with the standard single-mode telecom fiber. Afterward, we field validated for natural gas pipeline vibration monitoring using a 4-inch diameter steel pipeline operating at a fixed pressure level of 1000 psi, and a flow rate of 5, 10, 15, and 20 ft/s. The feasibility of gas pipeline monitoring with the proposed enhanced backscattering fiber cable shows a substantial increase in vibration sensing performance. The pilot-scale testing results demonstrated in this paper enable pipeline operators to perform accurate flow monitoring, leak detection, third-party intrusion detection, and continuous pipeline ground movement.

Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification.

This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning.

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.

Investigation of Goubau Wave Propagation on Large Pipes for Sensing Applications.

We examine the application of guided waves on a single conductor (Goubau waves) for sensing. In particular, the use of such waves to remotely interrogate surface acoustic wave (SAW) sensors mounted on large-radius conductors (pipes) is considered. Experimental results using a small-radius (0.0032 m) conductor at 435 MHz are reported. The applicability of published theory to conductors of large radius is examined. Finite element simulations are then used to study the propagation and launching of Goubau waves on steel conductors up to 0.254 m in radius. Simulations show that waves can be launched and received, although energy loss into radiating waves is a problem with current launcher designs.

Super-resolution demodulation for fiber sensor arrays based on the MUSIC algorithm.

This paper studies the use of MUltiple SIgnal Classification (MUSIC) as a super-resolution algorithm to improve demodulation results for intrinsic Fabry-Perot interferometer (IFPI) sensor arrays. Through distinction between noise and signal subspaces in an observation matrix, this paper shows that a 38-fold improvement in the full width at half maximum (FWHM) estimation of IFPI optical path differences (OPD) can be achieved using this algorithm. Based on this improved method, this paper demonstrates that a tunable laser with a 1.3-nm tuning range can achieve the same sensor demodulation performance as a tunable laser with a 50-nm tuning range if a conventional Fourier transform-based algorithm is used. This paper presents a new approach to analyzing optical signals produced by multiple multiplexed interferometers with similar OPDs with potential applications for both single-mode and multiple-mode devices.

Corrosion Sensors for Structural Health Monitoring of Oil and Natural Gas Infrastructure: A Review.

Corrosion has been a great concern in the oil and natural gas industry costing billions of dollars annually in the U.S. The ability to monitor corrosion online before structural integrity is compromised can have a significant impact on preventing catastrophic events resulting from corrosion. This article critically reviews conventional corrosion sensors and emerging sensor technologies in terms of sensing principles, sensor designs, advantages, and limitations. Conventional corrosion sensors encompass corrosion coupons, electrical resistance probes, electrochemical sensors, ultrasonic testing sensors, magnetic flux leakage sensors, electromagnetic sensors, and in-line inspection tools. Emerging sensor technologies highlight optical fiber sensors (point, quasi-distributed, distributed) and passive wireless sensors such as passive radio-frequency identification sensors and surface acoustic wave sensors. Emerging sensors show great potential in continuous real-time in-situ monitoring of oil and natural gas infrastructure. Distributed chemical sensing is emphasized based on recent studies as a promising method to detect early corrosion onset and monitor corrosive environments for corrosion mitigation management. Additionally, challenges are discussed including durability and stability in extreme and harsh conditions such as high temperature high pressure in subsurface wellbores.