Distributed Temperature Measurements Research Articles

Robust CO2 plume imaging by joint tomographic inversion using distributed pressure and temperature measurements

The scientific community has become increasingly interested in geological CO2 sequestration and CO2 enhanced oil recovery (EOR). The tracking of the CO2 propagation in both space and time during geologic sequestration is necessary to ensure the secure and effective handling of a site for CO2 injection. Our objective is to develop efficient and novel models and monitoring techniques for visualizing CO2 plumes using field measurements. As a first step, the streamline-based data integration approach is extended to include data from distributed temperature sensors (DTS). The DTS and pressure data are then jointly history matched using a hierarchical workflow combining evolutionary and streamline methods. As a final step, we will create maps that visualize CO2 propagation during the sequestration process based on saturation and streamline maps. We validate the extended streamline-based inversion method using a synthetic model. An application of the hierarchical workflow is then made to the CO2 geologic storage test site in Michigan, USA. Monitoring data includes bottom-hole pressure of the injection well, DTS data at the monitoring well, and distributed pressure measurements from several downhole sensors along the monitoring well. Based on the history matching results, the CO2 movement is largely limited to the zones intended for injection, which is in agreement with an independent warmback analysis of the temperature data. The novelty of this work is the extension of the streamline-based inversion algorithm for the DTS data, its field application to the Department of Energy regional carbon sequestration project, and potential extensions to other CO2-EOR and/or associated geological storage projects.

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

Distributed Temperature Sensing through Network Analysis Frequency-Domain Reflectometry.

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to ≈3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal formed by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of attaining high measurand resolution (≈50 mK at 3-cm spatial resolution) thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, unlike the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser and therefore is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double-sideband or single-sideband modulated probe light. The results confirm the validity of the proposed approach.

Distributed Temperature Measurement Utilizing Optical Carrier-Based Microwave Interferometry

Distributed Temperature Measurement Utilizing Optical Carrier-Based Microwave Interferometry

Microwave-free BOTDA based on a continuous-wave self-sweeping laser.

This Letter presents the first demonstration, to our knowledge, of a Brillouin optical time domain analysis (BOTDA) system based on a self-sweeping fiber laser. The unique feature of such laser sources is the generation of a narrowband tunable radiation with a small (6 MHz) tuning step and a reasonably large tuning range (3 GHz) making self-sweeping lasers extremely attractive for use in BOTDA systems. Since the wavelength tuning occurs solely due to internal processes occurring in the laser cavity, there is no need to use complex current/temperature control and feedback systems. This makes it possible to completely eliminate microwave devices, such as electro-optic modulators, from the BOTDA design. In this work, distributed temperature measurements with sensing line length of 25 km, spatial resolution of 10 m, and sensitivity of 2°C is demonstrated in a BOTDA system based on an Er-doped self-sweeping laser. The described approaches can reduce the complexity and overall cost of the BOTDA systems.

High-Spatial-Resolution Dynamic Strain Measurement Based on Brillouin Optical Correlation-Domain Sensors

Brillouin-scattering-based sensors have been widely applied in distributed temperature or strain measurement in recent 20 years. Brillouin optical correlation-domain technology has extensive development and application prospects because of its millimeter-level spatial resolution, distribution measurement, and high accuracy. Traditional Brillouin-scattering-based sensors, requiring a time-consuming frequency-sweep process, struggle to achieve dynamic strain measurement. In this article, Brillouin optical correlation-domain analysis and reflectometry based on fast-sweep frequency and slope-assisted methods will be reviewed. The main merits, drawbacks, and performances of these schemes are compared, and the avenues for future research and development of these two technologies are also explored.

Effect of multilayered coating of single-mode optical fibers on distributed temperature and strain measurement in mortar specimens

This study aims to characterize the effects of multilayered coatings on the performance of distributed fiber optic sensors (DFOS) when the coatings experience softening and melting at high temperatures. Two strain sensors (B-DFOS and W-DFOS) and one temperature sensor (Y-DFOS) were calibrated. They were either embedded along the centerline of a mortar bar or bonded on the surface of the specimen. The Y-DFOS was found to be no longer strain-free at high temperature since the softened sheath, aramid yarns, buffer, and polymer coatings became viscous and adhered to their surrounding mortar above softening temperatures 263–320 °C. Both the B-DFOS and W-DFOS captured uneven strain distributions along the mortar specimen due to non-uniform temperature distribution, mortar heterogeneity, and temperature-dependent strain transfer efficiency. The W-DFOS showed higher measured strains than the calculated thermal-induced strains at 100 °C - 300 °C due to the high thermal expansion coefficient of the additional buffer layer. The B-DFOS gave smaller measured strains than the thermal-induced strains at 300 °C - 500 °C. The displacement calculated by integrating the measured strains along the length of each mortar specimen was related to the applied temperature by parabolic regression equations.

Towards single-photon Brillouin optical time domain reflectometry.

We investigate a novel distributed Brillouin optical time domain reflectometer (BOTDR) using standard telecommunication fibers based on single-photon avalanche diodes (SPADs) in gated mode, ν -BOTDR, with a range of 120 km and 10 m spatial resolution. We experimentally demonstrate the ability to perform a distributed temperature measurement, by detecting a hot spot at 100 km. Instead of using a frequency scan like conventional BOTDR, we use a frequency discriminator based on the slope of a fiber Bragg grating (FBG) to convert the count rate of the SPAD into a frequency shift. A procedure to take into account the FBG drift during the acquisition and perform sensitive and reliable distributed measurements is described. We also present the possibility to differentiate strain and temperature.

Temperature tracing test and numerical simulation study during leakage of earth-rock dam

The leakage of earth rock dams exhibits characteristics of space-time randomness, concealment, and significant destructiveness. Prompt detection and identification of dam leakage are critical to ensuring the structural safety of these constructions. The distributed optical fiber temperature measurement method, which is based on optical frequency domain reflection (OFDR) technology, possesses attributes such as high spatial resolution, minimal temperature error, short response time, and real-time monitoring. As a result, it is an ideal technique for experimental research on leakage monitoring of earth rock dams. In this study, a model test was conducted on a clay core rockfill dam to investigate the feasibility of utilizing distributed optical fiber for identifying dam leakage. The variation pattern of partial distributed optical fiber temperature measurements in the dam body under the leakage state was compared and analyzed using the gradient method and the thermal pulse method. Furthermore, the heat flow coupled numerical simulation method was employed to verify and expand the findings of the indoor tests. The results of this study indicate that the OFDR distributed optical fiber leakage monitoring method using the thermal pulse approach can entirely reflect the temperature changes that occur during leakage, thereby providing strong reliability and exhibiting considerable potential for leakage location. The study offers valuable insights and a reference for the practical use of distributed optical fiber to monitor earth rock dam leakage.

Toward local core outlet temperature monitoring in gas-cooled nuclear reactors using distributed fiber-optic temperature sensors

Gas-cooled nuclear reactors operate at temperatures up to 950 ℃ with significant spatial variations in power generation and coolant flow through hundreds of parallel flow channels, resulting in complex mixing at the core outlet. The high temperatures and complex mixing can damage downstream components, challenge reactor calorimetry for power determination, and result in significant conservatism in calculated peak fuel temperatures, which ultimately limits the total power output. Directly measuring each gas stream individually is unrealistic using single point thermocouples, requiring more robust sensing techniques. Fiber-optic sensors are resilient to high temperatures (up to 1,000 ℃) and radiation damage. Moreover, distributed measurements can be made along the length of one fiber, making them potential candidates for monitoring local core outlet temperatures to improve core calorimetry and identify hot channels. To the authors’ knowledge, this manuscript is the first to report spatially distributed fiber optic temperature measurements to quantify mixing at the outlet of a relevant orifice plate under prototypic temperature and flow regimes to assess the feasibility of using fiber-optic sensors for distributed measurements of coolant temperature in gas-cooled reactors. A single optical fiber captured dynamic changes in local temperatures, whereas the mixed outlet temperature exhibited a muted response and could not identify which channels were responsible for the change in the mixed outlet temperature. The discussion focuses on potential challenges for deploying distributed optical fibers in gas-cooled reactors, including the effects of vibrations, radiation-induced signal attenuation and drift, and routing of the fiber while minimizing flow obstructions.

Spatial Resolution Optimization Technology of Raman OTDR Using Variable Sampling Rate

The technology to improve the spatial resolution of distributed sensing has always been a research hotspot. In this paper, a ROTDR (Raman-OTDR) system with a variable sampling rate and a spatial resolution optimization algorithm are designed, as shown in the figure, which makes the distribution of sampling points dense and improves spatial resolution. A point selection matrix based on the reconstruction algorithm is generated to reconstruct a one-dimensional high-precision signal. Furthermore, an optimization algorithm based on the reconstruction algorithm is designed, which can select the sampling rate and flexibly set the desired spatial resolution. Sampling rate combination and offset distance are set as the output of this algorithm to improve the response time and eliminate the blind area in the reconstruction process. The simulation results show that the reconstruction algorithm can improve the spatial resolution of the distributed temperature measurement system to 0.25 m at the cost of sampling ten times at different sampling rates (91 MHZ - 100 MHZ), representing a resolution gain of four times compared with the spatial resolution limited to 1 m by the acquisition card. This paper also compares the sampling results at a single sampling rate, random sampling rate combination, and optimized sampling rate combination to certify the effectiveness of this technology. The sampling result at an optimized sampling rate combination has the highest measurement accuracy.

An efficient inversion method to interpret distributed temperature measurement of horizontal wells in shale gas reservoirs

In order to obtain accurate production profile and fracture parameters of horizontal wells in shale gas reservoir from distributed temperature sensing (DTS) data, an efficient inversion method based on improved dichotomy (ID) algorithm has been developed. It has realized quantitative interpretation of production profile and fracture parameters. The synthetic case indicates the inversion calculation of the ID inversion model is 9 times faster than that of conventional inversion algorithm. Finally, the ID inversion model has been applied to a field case to inverse the DTS data. 69 effective artificial fractures have been identified. The production profile and fracture half-length of the field well are obtained. The interpreted fracture half-length indicates that the extension of the hydraulic fractures is extremely uneven. There are obvious dominant fractures in several fracturing stages of which the fracture half-length is more than 120 m, however, the average fracture half-length of the field case is only 61.21 m. The inversion simulated temperature profile matches the measured DTS data well. The interpreted production profile is in good agreement with the PLT testing results that verifies the reliability of the developed ID inversion model.

Experimental analyses of the spatial varying temperature development of type-IV hydrogen pressure vessels in cyclic tests considering different length to diameter ratios

Establishing hydrogen as a reliable energy carrier is closely linked to the performance and safety level of the storage systems. During operation, the storage systems such as composite over-wrapped pressure vessels (COPVs) are exposed to complex physical, mechanical and thermal loads. Since the mechanical and physical properties of the used materials are strongly temperature-dependent, thermal influences must be taken into account for the vessel design. The effect of the vessel geometry, in particular the length-to-diameter ratio, as well as filling conditions on the temperature distribution within the fluid is analysed through the examination of cyclic tests in accordance with ANSI/CSA HGV2 and UN GTR N0.13/ECE R134. The gas temperature development during the cyclic tests is determined using a measuring device that allows a spatially distributed temperature measurement at eight vertical and horizontal positions. Two sizes of vessels are investigated characterised by the same inner and outer diameter but different length. It is observed that the length-to-diameter ratio substantially influences the temperature distribution within the fluid for room as well as elevated ambient temperatures at comparable filling conditions. Furthermore, the mass flow of the gas influences the temperature distribution within the fluid and shows an increased spatial and thermal inhomogeneity at higher gas mass flow. In addition, it can be observed that the temperature increase (ΔT) during filling depends significantly on the vessel temperature distribution. In the transient case of filling directly after emptying the vessel, a temperature increase of 36 K compared to the initially homogeneous vessel temperature has been found. Moreover, gas temperature differences of up to 97 K between the end of filling and the end of emptying can be observed, which is a significant thermal load for the vessels. Thus, the results presented here provide a broad data basis as input and boundary conditions for numerical fluid dynamic and structural analyses of pressure vessels made of carbon-fibre-reinforced plastics (CFRP).

Probing inhomogeneity of electrical-thermal distribution on electrode during fast charging for lithium-ion batteries

With the emerging demands for precise control in next-generation battery managements systems (BMSs), more fundamental understanding of external characteristics for lithium-ion batteries (LIBs) is urgently required. The electrical-thermal distribution across electrode is closely coupled with cell configuration and operating conditions. Especially during fast charging, substantial temperature rise can lead to inhomogeneous in-plane thermal distribution with large thermal gradients, causing uneven local lithium (Li) plating on the anode surface, which highly undermines the safety of LIBs. In this study, aiming at probing the electrical-thermal inhomogeneity on electrode of pouch cells, distributed temperature measurements are conducted under various charge rates and ambient temperatures. A stacked layer model is established and further verified with measured anode potentials. Detailed temperature distribution inside the pouch cell and the temperature field evolution at different charge stages are thoroughly elucidated. Electrode current density distribution that results in thermal inhomogeneity is revealed, and three thermal distribution patterns coupled with various operating conditions are summarized. Then, the effects of different convection methods on electrical-thermal distribution are analysed, and enhanced convection is found to effectively reduce the thermal inhomogeneity but increase the risk for Li plating. Under uneven convection scenarios, Li plating preferably occurs on the colder region under fast charging. This study provides novel insights in electrical-thermal inhomogeneity inside LIBs, highly underpinning the smart control strategy development of next-generation BMSs.

Embedded Distributed Temperature Sensing Enabled Multistate Joint Observation of Smart Lithium-Ion Battery

Accurate monitoring of the internal statuses is highly valuable for the management of the lithium-ion battery (LIB). This article proposes a thermal-model-based method for multistate joint observation, enabled by a novel smart battery design with an embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to coestimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical-model-based method regarding the accuracy and the robustness to battery aging.

Single-photon detector-based long-distance Brillouin optical time domain reflectometry

We present a long-range Brillouin optical time domain reflectometer (BOTDR) based on photon counting technology. We demonstrate experimentally the ability to perform a distributed temperature measurement, by detecting a hot spot in a thermal bath at 100 km, and the possibility to achieve measurement until 120 km with a spatial resolution of 10 m. We use the slope of a fiber Bragg grating (FBG) as a frequency discriminator, to convert count rate variation into a frequency shift. A performance study of our distributed sensor as a function of spatial resolution is also presented.

Comparison of Raman, Brillouin, and Rayleigh Distributed Temperature Measurements in High-Rate Wells

With the maturity of and demand for fiber-optic sensing technology growing steadily over the last few years across multiple basins, operators are seeking fiber-optic sensing solutions that address the technology challenges associated with the life-of-field monitoring of subsea developments. Single-ended distributed temperature sensing (DTS) measurements have been acquired for decades now, typically using Raman optical time-domain reflectometry (OTDR) on multimode fiber. However, for topside interrogation of subsea completions, Raman DTS performs poorly. This is due to the available optical power budget and the potential wavelength dependency of optical attenuation across multiple connectors and splices comprising the optical subsea infrastructure. Any wavelength-dependent attenuation as the signals pass through connectors, splices, and optical feedthrough systems will generate step changes in the measured Raman DTS temperature profile. Brillouin OTDR can provide a DTS alternative that overcomes these challenges and operates on single-mode fiber. Brillouin OTDR operates with a large dynamic range to measure a wavelength (frequency) shift of the Stokes/anti-Stokes components that is proportional to both strain and temperature. Since downhole cables are manufactured with optical fibers suspended in a gel and with appropriate extra fiber length (EFL), any fiber strain relaxes, and the Brillouin wavelength shift is an absolute temperature measurement. An additional alternative is also explored here. We typically associate coherent Rayleigh OTDR with distributed acoustic sensing (DAS) on single-mode fibers, but low frequencies also contain a relative temperature dependence. The low-pass filtering of DAS data can then be used as a form of Rayleigh DTS with appropriate data processing. In this paper, we report on a comparison of Raman, Brillouin, and Rayleigh DTS simultaneously acquired in the same high-rate producer and injector wells. We validate that, with appropriate cable design, Brillouin DTS can be simultaneously operated on the same single-mode fiber with DAS and can deliver absolute temperature measurements suitable for production analysis. We also use a laboratory experiment to show that Rayleigh DTS provides an accurate measure of temperature changes comparable in precision to a traditional thermocouple. We conclude with a discussion about the implementation of this DAS-DTS solution for sensing subsea completions.

Improved wavelet modulus maximum method for distributed optical fiber temperature sensing

The temperature measurement accuracy of the Raman distributed optical fiber temperature sensing system is an important metric. Aimed at the reduction of the temperature measurement error, which is caused by the noises in the sensor system, an improved wavelet modulus maximum method, which combine the 3sigm criterion with the traditional wavelet modulus maximum algorithm is proposed. In this proposed algorithm, the 3sigm value is used as the threshold for the highest decomposition layer of the wavelet modulus maximum method to judge whether it is a signal or noise, instead of using the ratio of the modulus maximum of the highest decomposition level to the number of decomposition levels to determine the threshold. We conduct distributed temperature measurement on a 10-km long multimode fiber, and analyze the experimental data by using the improved algorithm. The results show that the temperature measurement fluctuation obtained by improved method is 0.497°C on average. It is better than the 1.13°C that is obtained by the traditional wavelet modulus maximum algorithm. Compared with the traditional algorithms, the 3sigm criterion algorithm has improved the temperature measurement accuracy significantly. The improved algorithm provides a new idea for the selection of the threshold value, which makes the threshold selection more statistically.

Raman scattering-based distributed temperature sensors: A comprehensive literature review over the past 37 years and towards new avenues

Over the last 37 years, particular interest has been directed towards exploring the characteristics of the optical environment for sensing, giving rise to what would now be one of the largest applications of well-known optical fibers, typically employed to transmit data at high rates. Sensing temperature, pressure, liquid level, deformation, and other physical parameters utilizing optical fibers has become a growing branch of research and a business competing with well-established electrical sensors in the industry. Optical fiber sensors have all the inherent characteristics of a fiber optic cable, such as electromagnetic immunity, small size and weight, multiplexing, and so on. These exclusive features have made fiber sensors so versatile as to become a transformative technology by enabling several industrial processes to be carried out with higher reliability. Nowadays, there are several optical sensors, including fiber Bragg grating, interferometric, polarimetric, polymer fiber, distributed, and several others. Specifically, Raman-based distributed temperature sensor (RDTS) is a class of fiber optic sensors broadly employed in temperature measurement of large structures such as oil and gas wells, tunnels, and pipelines. Since 1985, many techniques have been proposed to break through the barriers of exploring Raman scattering as a distributed temperature measurement method. Range, spatial and temperature resolutions have been the most investigated parameters. In this perspective, this paper presents a comprehensive review focused on the progress of the RDTS technology over the past 37 years (1985–2022), covering an analysis of over 500 journal papers. First, a brief introduction to fiber optic sensor technology is presented as a theoretical basis, discussing the emergence of distributed sensors. Subsequently, Raman scattering in optical fibers is introduced, as well as how this nonlinear effect can be used to build temperature sensors. Next, RDTS technology is detailed, followed by a discussion of its applications and evolution over nearly four decades of development. Lastly, future perspectives are addressed in this review for the advancements in distributed temperature sensor technologies.

The borehole thermal energy storage at Emmaboda, Sweden: First distributed temperature measurements

Xylem in Emmaboda, Sweden, has one of the first borehole thermal energy storage (BTES) sites storing excess heat and has been previously thoroughly studied and monitored. Here, the results from distributed temperature sensing (DTS) measurements in observation boreholes, UB1, 10 m outside the BTES, and UB46, inside the BTES, are presented. The measurements were performed in February and March to September 2019. DTS combined with geological and hydrogeological knowledge give qualitative insights into Emmaboda’s heat transfer in operation. To analyze the DTS measurements, knowledge about the borehole deviations and relative physical locations among boreholes is necessary. The measured temperature profile in UB1 is parallel to the geothermal gradient, follows BTES temperature, and does not seem disturbed by groundwater flow and production boreholes. The flat terrain and several rivers and dams in the area, together with results from the thermal response test following the mineralogical composition of the bedrock, verify no regional groundwater flow. Emmaboda’s heat loss is conductive only. In UB46, the temperature irregularities are interpreted as contact points or vicinity to production boreholes. Larger temperature responses in heat charging than in extraction mode are probably due to higher temperatures. Three-dimensional to four-dimensional design, documentation and visualization should further examine the influence of borehole deviation.