Distributed Fiber Optic Sensors Research Articles

A critical review of distributed fiber optic sensing applied to geologic carbon dioxide storage

AbstractIn the context of global climate change, carbon capture and storage (CCS) has become a direct and effective measure for reducing greenhouse gases emission. However, injecting CO2 into the subsurface reservoirs may pose risks related to geological hazards. Therefore, monitoring the variations in underground temperature fields, strain fields, and vibration fields induced by CO2 injection is essential for predicting and controlling geological hazards. Distributed fiber optic sensing (DFOS) technology, with its unique features, enables real‐time monitoring of temperature, strain, and vibration. By deploying fiber optic (FO) cables inside wellbores, a DFOS can be used to effectively capture multiple underground response parameters. This paper reviews the applications of DFOS technology in CO2 geological sequestration. The chapter covers aspects such as the literature review, principles and applications of fiber optics, and representative monitoring projects. Finally, the paper discusses the challenges and proposed solutions for DFOS technology in this context. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Methodology for geometry assessment of self-drilling micropiles using distributed fibre optic sensors

Self-drilling micropiles (SDMs) are gaining popularity as bearing foundation elements, as this construction technique allows for considerable time and cost savings. However, the irregular geometry and variability in the bearing capacity present uncertainty and a significant risk that has limited SDM deployment. To date, no reliable technology has been used to measure the SDM geometry. This paper proposes a methodology that uses distributed fibre optics (DFO), heat conduction modelling and inverse analysis to measure the pile geometry, and could also be extended to assess its bearing capacity. The temperature distribution during cement hydration was measured using two DFO investigative technologies and was compared to traditional temperature measurements using point sensors. An inverse analysis of SDM geometry was subsequently carried out based on the DFO measurement of temperature, a finite-element heat conduction model and a calibration pile. Finally, the calculated pile geometry was compared to the geometry of an excavated pile in the uppermost part. The methodology presented here is not only intended as a tool for designing SDMs but can also be deployed as a structural health monitoring system to detect and monitor crack formation.

Tensile behavior of unidirectional thick-Ply all-carbon hybrid laminates: a systematic experimental and numerical study

Unidirectional long fiber reinforced polymers generally exhibit unfavorable abrupt and brittle failure under mechanical stresses without pre-warning which currently limits their use in safety critical applications. The lack of ductility of such composites can be overcome by interlayer hybridization where Low Strain (LS) material is sandwiched between High Strain (HS) material. This results in complex failure mechanisms, including multiple interacting damage modes, such as ply fragmentation and delamination. All-carbon unidirectional hybrid laminates with different layup sequences were designed and manufactured to study the pseudo-ductile behavior. An available analytical model was exploited to predict the damage scenarios of the laminates, both with stress-strain diagrams and damage mode maps. Tensile tests were carried out using different measurement and observation techniques including digital image correlation (DIC), embedded distributed fiber optic sensors (dFOS) and helicoidal X-ray computed tomography (CT). A finite element model was also developed to predict the damage mechanisms. Validated by experimental results, the numerical model was found to accurately predict the tensile damage modes and their evolution in the considered unidirectional thick ply all-carbon hybrid laminates.

A silicone rubber packaged distributed optical fiber sensing tape for strain-crack monitoring based on OFDR technique

A novel silicone rubber (SR)-packaged distributed optical fiber sensor (DOFS) tape based on optical frequency domain reflectometry (OFDR) technique was developed to effectively monitor substrate cracking. The analytical strain distribution of this sensor tape is derived based on the current strain transfer model and then used to determine the parameter sensitivity to provide a scientific basis for the selection of packing parameters and bond length. A calibration test of the sensor tape on steel plates was performed, and the feasibility of the sensing tape for accurately measuring the substrate strain and crack width was demonstrated. A test is conducted to monitor adjacent double cracks, and the test results show that the sensor tape has the ability to identify crack positions with crack spacings greater than 40 mm. The sensor tape is applied to the monitoring of concrete cracking induced by corrosion, and the test results demonstrate that the DOFS sensing tape has advantages for monitoring large cracks. Both the analytical and experimental results show that the novel SR-packaged DOFS tape is effective and feasible.

Inverse finite element method with energy-based regularization for deformation reconstruction and structural health monitoring

Distributed Fiber Optical Sensing (DFOS) can be used to perform compact, low-invasive and large-scale strain measurements, providing an important tool for strain-based deformation reconstruction and Structural Health Monitoring (SHM). However, most current deformation reconstruction methods are difficult to apply to the sparse uniaxial strain measurement conditions associated with DFOS, especially for structures with complex geometries. This work introduces an energy-based regularization method, i.e., a priori assumption that the actual deformation state is close to the state with the minimum deformation energy under the premise of satisfying the strain constraints, into inverse finite element method to perform load-independently deformation reconstruction under the condition of sparse uniaxial strain measurement. The Inverse Finite Element Method with Energy-based Regularization (iFEM-EBR) can naturally construct the DFOS-based deformation reconstruction model with quasi-forward finite element analysis without introducing additional mathematical processing. The sensitivity of this method to structural characteristics, such as material parameters, makes it a unique prospect in SHM.

Symmetric prism characterisation test to infer heat generation and heat conduction properties of concrete

Performance-based concrete design can accelerate the acceptance of emerging lower carbon concretes to reduce the environmental impact. A semi-adiabatic symmetric concrete prism experimental and analytical approach is proposed as a pathway for concrete thermal characterisation. The prism design promotes 1-D heat flow conditions using a single insulated specimen. The prism dimensions (700 mm ×100 mm ×100 mm) are rationalised to suit usage as a characterisation specimen and mix compositions that replicate those of in-situ concretes can be assessed. The experimental spatial and temporal thermal profiles of a CEM I mix and a high GGBS replacement mix were measured during hydration and through a step change in temperature in the hardened state. Thermocouples and a distributed fibre optic sensor along the central axis of the symmetric prism were used for this purpose. Multi-physics modelling provided a platform to address thermal measurement data uncertainty, extract fundamental concrete thermal parameters and gain insight into the heat generation and heat flow physics in the concrete prism specimens. The complete heat generation and maturity time histories were accurately modelled and ‘concrete thermal footprints’ derived.

Optimization of the installation techniques of distributed optic fiber sensors and measurement accuracy analysis for asphalt pavement based on finite element methods

The accurate measurement strain is critical to evaluating the structural integrity of asphalt pavements. The measurement strain error is caused by the encapsulation and installation of distributed optical fiber sensor (DOFS) for pavement monitoring. This study introduces an innovative approach to correct installation error. Firstly, a DOFS is embedded into asphalt pavement to simulate the mechanical response caused by traffic load. The optimal installation technology for each asphalt layer is proposed. In addition, the strain correction formula considering the pavement temperature is proposed for different asphalt layer. A rutting test is used to evaluate monitoring performance. Finally, a test road was built. A continuous temporal and spatial distribution strain of DOFS caused by traffic load is obtained. The results indicate that the average error rate of strain testing effectively decreases for each asphalt layer. In summary, this paper contributes to obtaining more accurate measurement strain of DOFS in pavement monitoring.

Use of Distributed Fiber Optic Sensors for the Monitoring of an Accelerated Pavement Test

The need to develop more efficient pavement management systems has driven continuous improvement in structural health monitoring (SHM) systems for road infrastructures. Factors such as low energy consumption, minimal disruption (small-sized and lightweight devices), resistance to environmental conditions, high durability, and sensitivity are some of the possible improvements worthy of exploration. Fiber optics appear to have several of these desired characteristics. In this context, this article presents strain measurements performed using fiber optic cables installed at various levels in pavement structures tested on the accelerated pavement testing facility at the Université Gustave Eiffel (or fatigue carousel). The results reveal that strains measured with this technology closely align with those obtained using traditional strain gauges. However, an advantage of fiber optics is that they enable distributed measurements over long distances with high precision (10 m with a measurement interval of 2.6 mm in this study). These measurements highlight a significant variability in pavement strains, seemingly attributable to the heterogeneity of pavement materials and variations in layer thicknesses. As a result of these findings, laboratory-scale investigations have been initiated to gain a better understanding of these sources of variability.

Monitoring restrained shrinkage and cracks of ultra-high-performance concrete (UHPC) using distributed fiber optic sensors

This paper presents a method to measure the shrinkage and cracks of ultra-high-performance concrete (UHPC) using distributed fiber optic sensors. UHPC prisms and rings were prepared and instrumented with fiber optic cables which served as distributed sensors to assess restrained shrinkage based on the Optical Frequency Domain Reflectometry (OFDR) technology with a 0.65-millimeter resolution. The initiation and development of shrinkage cracks were detected, located, quantified, and visualized from the strain distributions. The effect of steel fibers, lightweight sand, and shrinkage reducing agent on shrinkage distribution and crack pattern was investigated. The results indicate that the restrained shrinkage of UHPC decreased from 809 µɛ to 245 µɛ by using steel fibers, lightweight sand, and shrinkage reduce agent. The maximum crack width decreased from 1.605 mm to 0.065 mm. This research enhances the capability of monitoring shrinkage and cracks and supports the development of low-shrinkage UHPC.

Distributed fiber optic sensors for structural health monitoring of composite pressure vessels

Abstract In this paper, we present a comprehensive overview of our research in the field of distributed fiber optic sensors for structural health monitoring of hydrogen composite pressure vessels. Specifically, we demonstrate how the integration of fiber optic sensors into composite pressure vessels enhances safety while simultaneously reducing maintenance costs. The small size of optical fibers enables their integration into composite structures during the manufacturing process, allowing continuous monitoring and precise detection and localization of structural damages during service life. We also discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. Our applications of fiber optic sensors demonstrate their potential to contribute significantly to the energy transition towards renewable sources.

Crack width measurement with OFDR distributed fiber optic sensors considering strain redistribution after structure cracking

Crack width measurement with OFDR distributed fiber optic sensors considering strain redistribution after structure cracking

Slickline-Deployed Fiber-Optic Cable Provides First Production Profile for High-Temperature Gas Well

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215512, “Slickline-Deployed Fiber-Optic Cable Provides First-Ever Production Profile for High-Temperature Gas Well,“ by Stuart Berry, SPE, Expro, and Danu Dirya, SPE, and Graeme Cowie, SPE, TotalEnergies, et al. The paper has not been peer reviewed. _ Distributed fiber-optic sensing (DFOS) allowed the continuous gathering of flow-profile information from a high-temperature, high-rate gas well. The objective of the case study described in the complete paper is to demonstrate that thermal inversion modeling can be used to produce a production-flow profile in an environment where conventional production logging is not possible. As a result of deploying DFOS, data can be acquired at more-realistic rates. Through performing of thermal inversion of distributed temperature sensing (DTS) data and analysis of distributed acoustic sensing (DAS) data, a more-accurate flow profile was achieved. Introduction Culzean is a high-pressure/high-temperature gas condensate field in the central North Sea. The main productive interval for the field is the Triassic Joanne sandstone deposited in a semiarid continental fluvial environment. The Jurassic Pentland sandstone deposited by meandering fluvial systems also contains significant resources. Initial reservoir pressure was 936 bar; the temperature was 170°C. The development consists of six production wells, five from the Joanne and one from the Pentland. While the reservoir interval is relatively thick, the depositional environment created a very layered reservoir with significant variability in reservoir quality. Flow-profile data over the reservoir has traditionally been acquired using production-logging tools with spinner velocity and pressure and temperature measurements. Production-log deployment can be problematic in high-rate wells, particularly when tubing is restricted because of pipe deformation or deposits in the tubing. In the Culzean field, several wells were selected for flow-profile monitoring. Previous slickline operations indicated that conventional production logging would be ineffective. Modeling showed significant tool-lift risk, meaning that the well could only be flowed at approximately 25% of its normal operating rate. A previous attempted production-logging campaign in the well resulted in no valid data acquisition but incurred a direct cost of $700,000 and a deferral of 10 days production estimated at a cost of $10.8 million. An alternative acquisition method was investigated to obtain production-profile data while minimizing risks associated with conventional production-log deployment. DFOS provides a continuous measurement over the entire length of the well for temperature and acoustic noise. A high-temperature slickline DFOS wire could allow Culzean well data acquisition under flowing conditions. Technology Selection Data acquisition was divided into a shut-in phase and a flowing phase. For the shut-in phase, a simplified production-logging toolstring was used to measure any crossflow over the reservoir section. The flowing phase would consist only of DAS data over the reservoir section, with a simple pressure/temperature memory tool in the sump of the well. The DFOS data would gather shut-in data before opening the well up to flow. For the conventional tool shut-in string, an inline caged spinner was selected.

Localizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing

Ensuring the safe transportation of energy relies heavily on the timely safety inspection and pigging of energy pipelines using in-pipe inspection robots known as Smart/Intelligent PIGs. Addressing the potential risks of PIG block incidents and the limitations of excessive speed in the maintenance, real-time localization and tracking of these inspection robots has become imperative. However, traditional localization and tracking methods present a challenge due to their resource-intensive nature, requiring significant manpower and resources. To overcome this limitation and enhance the intelligence of the robot operation monitoring, this paper proposes an innovative artificial intelligence (AI) integration algorithm framework based on distributed optical fiber sensing (DOFS) for real-time localization and tracking of robots. It adopts noise reduction and reconstruction techniques in signal processing, effectively enhancing the quality of optic fiber vibration signals. Notably, the integration of two distinct features that complement each other enables dual verification of tracking detection, ultimately bolstering the system’s effectiveness and trustworthiness. Besides, a logical reasoning-based localization decision strategy further enhances the system’s capabilities, allowing for controlled tracking intervals and step sizes that can be tailored to meet the task requirements under diverse working conditions. The collaboration of three modules makes it feasible to monitor dynamic changes of the detection robot in the pipeline along the direction of operation. The experimental results convincingly demonstrate that the integrated framework possesses several key advantages of remarkable robustness, real-time performance, and minimal error. It underscores the system’s potential to ensure energy pipeline safety efficiently and effectively.

Attention-ResNet-AR-LSTM: An intelligent method for PCCP deformation prediction via structure monitoring based on distributed fiber optics

The prestressed concrete cylinder pipeline (PCCP) is extensively employed in long water diversion projects due to its commendable performance and cost-effectiveness. Nonetheless, the continuous application of load on the prestressed steel wire during the production phase can amplify the creep deformation in both the concrete and steel cylinder of the PCCP. This phenomenon may potentially lead to the development of cracks at the socket end of the PCCP, posing a significant risk to the safe operational integrity of the pipeline. In this study, the strain development prediction model of PCCP concrete and steel cylinder is constructed through the integration of ResNet, AR, and LSTM modules, and the attention mechanism is also applied. Based on the prototype test of PCCP structure monitoring, the strain values of steel cylinder and inner concrete were recorded by pre-set distributed fiber optic sensors on the 1st, 3rd, 5th, 7th, 9th, 11th, 13th, 15th, 17th, and 20th day after the production of PCCP. Then an Attention-ResNet-AR-LSTM model is constructed to predict the strain development of PCCP steel cylinder and concrete based on monitoring data on the 20th day using the data of 1st to 11th days. For steel cylinder and concrete structures, the R2 of the proposed models is above 98%, indicating that the proposed models can capture the strain changes of different structures well. Compared with other deep models and machine learning models, the proposed model also shows a significant predictive ability. This model introduces a novel approach to forecast strain evolution during the manufacturing of PCCP.

MI-MZI based distributed optical fiber sensor for location and pattern recognition.

This research addressed the drawbacks of the conventional hybrid structure and processing technique by presenting a novel distributed fiber optic sensor based on a hybrid Michelson and Mach-Zehnder interferometer. The sensor can achieve blind spot free positioning and has a wide response frequency, additionally its structure is not complex. It can obtain two phase signals from each of the two interferometers by using a demodulation method that uses a 3 × 3 optical coupler. To determine the position of the disturbance, we computed cross-correlations on the two signals following basic mathematical techniques. Markov Transition Field was used to transform the phase signals-which had been filtered by a band pass filter-into two-dimensional images. Tagged photos built a dataset, which is then fed into a neural network to identify patterns. Experiments have shown that the frequency response capacity of the structure was verified, and it was able to achieve location within 0-30 km with location errors of ±85 m. In a six-category pattern recognition, the testing set accuracy was 98.74%.

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Optimization of distributed fiber optic sensors for pavements by combining DEM-FDM coupled numerical simulation method and response surface method

The cooperative deformability of sensors embedded into a host material and the measurement accuracy of these sensors affect pavement health monitoring. In consideration of these concerns, this study combined numerical simulation and the response surface method to optimize sensor design and standardize the distributed fiber optic sensors (DFOSs) used in pavement engineering. Numerical models were developed by coupling the discrete element method and the finite difference method. First, on the basis of the discrete-continuous coupled numerical simulation method, two working conditions of unembedded and embedded DFOSs in asphalt mixtures were set up. Virtual specimen models were established, and the numerical simulation method was verified by implementing the indoor four-point bending beam test. Second, the effects of DFOSs on the physical field of asphalt mixtures were analyzed by comparing the two conditions, and optimized evaluation indexes of DFOSs for road use were proposed based on the stress, displacement, and strain difference degrees in consideration of cooperative deformation and measurement accuracy. Last, the Box-Behnken response surface method was used to optimize the cross-section size and elastic modulus of the encapsulation material of the DFOSs, and the optimal design parameter range of the DFOSs was determined. In summary, this study revealed the cooperative deformation law between embedded sensors and asphalt mixtures to facilitate the development and design of DFOSs with high measurement accuracy for pavement applications.

Temperature monitoring in levees for detection of seepage

Improving knowledge of existing levees through investigation and monitoring is an important step in evaluating their safety and that of the surrounding area. Nevertheless, these activities are complex due to the considerable levee length and the high spatial variability of soil composing the body and foundation, especially when paleo-rivers are present. In order to investigate the reliability of new advanced techniques proposed for characterizing the soil stratigraphy and the seepage condition within the levee foundation, a new test site was realized along the Adige River in Bolzano Province (Italy). Here, five boreholes, drilled in a 20-m-side square area straddling the embankment, host four different types of monitoring equipment, among which some are Distributed Fiber Optical Sensors (DFOS), here used for detecting the temperature variations along the well. The present paper focuses on the critical analysis of the preliminary results obtained with DFOS and their comparison with data obtained using traditional pressure and temperature probes. The monitoring data collected in the field during the passage of a flood that occurred on 5th August 2021 are used to better understand the hydraulic behavior and the safety conditions of the levee but also to fully assess the reliability and potential of DFOS.

Low-coherence interferometry with homodyne demodulation capabilities for distributed fiber-optical sensors

Distributed fiber-optical sensors (DFOS) achieve the best results while interference registration schemes are used when controlled physical field lead to the fiber length and phase difference. DFOS based on fiber-optical phase-amplitude filters (FOPAF) have a simple and reliable design, consisting of distributed sensing elements (DSE) set. However, the main problem is the stabilization of its characteristics in different areas of DSE. On the other hand, the possibilities of low-coherence interferometry with homodyne light demodulation show the possibilities of stabilizing working points, but in the Michelson fiber-optical interferometric tandem scheme. The purpose is to substantiate the option of stabilizing the operation of DSE based on FOPAF, using low-coherence interferometry with homodyne demodulation in monitoring spatially distributed physical fields. The appropriate methods are considered. The analysis of the possible experimental setup based on the low-coherence interferometry with homodyne demodulation in the interferometric tandem scheme in comparison with the variants, based on the low-mode fiber-optical fibers is established. Promising scheme of the experimental installation is presented and the requirements for components are justified. The prospects of using low-coherence interferometry with homodyne demodulation for DFOS based on DSE with FOPAF are shown. The proposed low-coherence interferometry scheme with homodyne demodulation for DFPS on DSE with FOPAF has high sensitivity, is built on affordable equipment and, accordingly, has a low specific cost for the subsequent development of monitoring system.

Machine learning-assisted intelligent interpretation of distributed fiber optic sensor data for automated monitoring of pipeline corrosion

Distributed fiber optic sensor (DFOS) offers unique capabilities of monitoring corrosion for long pipelines. However, manually interpreting DFOS data is labor-intensive and time-consuming. To address this challenge, this paper presents a machine learning approach for real-time automatic interpretation of DFOS data used to monitor both uniform and non-uniform corrosion in pipeline. A machine learning model is developed to automatically detect corrosion based on DFOS data, and a corrosion quantification method is developed based on the output of the machine learning model. The proposed approaches are evaluated using laboratory experiments in terms of accuracy and robustness to pipeline diameter, spatial resolution of DFOS, type of fiber optic cable, and sensor installation methods. The results show that the F1 score for corrosion detection and the R2 value for corrosion quantification are 0.986 and 0.953, respectively. This research will facilitate pipeline corrosion monitoring by enabling automatic distributed sensor data interpretation.