Search Results (66)

Hydraulic fracture diagnostics are essential for characterizing fracture geometry, connectivity, and effectiveness in unconventional reservoirs. However, the diversity of available techniques and fragmented understanding of their physical mechanisms hinder multidisciplinary communication and lead to inconsistent field decisions. This review provides a systematic assessment of diagnostic methods, focusing on their physical foundations, applicability, and limitations, and proposes a unified reference framework. Direct diagnostics, including microseismic monitoring, fiber-optic sensing (DTS and DAS), and tiltmeter measurements, are evaluated in terms of data characteristics, interpretation challenges, and field applicability. Indirect methods based on pressure, production, and tracer data—such as DFITs, pressure interference tests, and tracer analysis—are examined for their roles in fracture closure evaluation and interwell connectivity. The review further distinguishes between single-well and multi-well applications, providing a structured classification framework. It highlights that individual methods are constrained by non-uniqueness, modeling assumptions, and non-ideal field conditions, especially in complex fracture networks. Therefore, reliable characterization requires integrating multiple diagnostics with physics-based modeling and uncertainty-aware interpretation. Recent advances in AI and machine learning are also briefly discussed as tools to enhance automated analysis and support real-time, predictive diagnostics. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

In response to the challenges in monitoring the production profile during the development of the Qingcheng shale oil field in the Changqing Oilfield, this study systematically investigates the application mechanism and practical effectiveness of Distributed Temperature Sensing (DTS) technology for dynamic monitoring in horizontal wells. By establishing a coupled model of fracture–matrix dual-porosity media flow and wellbore thermodynamics, which integrates mass, momentum, and energy conservation equations solved via the finite difference method, an interpretation method for the production profile based on the Joule–Thomson effect is proposed. The model was calibrated using shut-in temperature data and validated by comparing simulated temperature profiles with DTS measurements under constant-rate production. Field tests conducted in six horizontal wells in the Qingcheng oil field enabled the quantitative analysis of cluster-level production contributions along the horizontal section, with a water-producing zone localization accuracy of ±3.5 m. The results indicate that shale oil wells exhibit a non-uniform production characteristic of “high at the front and low at the rear” during the early production stage, where the production contribution from fully fractured segments can be up to 2.8 times that of adjacent segments. Inversion of the fiber-optic monitoring data reveals that differences in the conductivity of hydraulic fractures are the primary cause of flow heterogeneity. This research provides a theoretical foundation and technical support for the efficient development of shale oil, contributing to the transition of China’s continental shale oil development from “experience-driven” to “data-driven.” Full article

Distributed fiber optic temperature sensing (DTS) monitoring technology is increasingly widely applied in oil reservoir water injection development. However, existing DTS interpretation methods for layered water injection processes have insufficiently considered the effects of multilayer injection and reservoir damage. To address this issue, this paper takes into account interlayer heterogeneity and reservoir damage and, based on the laws of conservation of mass and energy, comprehensively incorporates the effects of friction, the Joule–Thomson effect, thermal convection, and thermal expansion. By coupling wellbore pipe flow with formation seepage, a temperature profile prediction model for multilayer water absorption under steady-state water injection conditions is established. Comparative validation against classical models such as those by Babak and Nowak demonstrates that the proposed model achieves high computational accuracy. Using this model, the influence patterns of injection rate, tubing diameter, formation coefficient, and skin factor on wellbore temperature distribution are systematically analyzed: a higher injection rate leads to a smaller temperature rise in the injected water; a larger tubing diameter results in a greater temperature rise; the formation coefficient affects the temperature profile by regulating interlayer water absorption distribution, while reservoir damage (skin factor) has a relatively limited direct impact on the temperature profile. The model is applied to interpret DTS field data from Well A, and the water absorption rate of each sublayer is quantitatively obtained: the main water absorbing intervals are 1878.7–1897.5 m and 1919.5–1950.6 m, with water absorption accounting for 30.57% and 24.28% of the total injection rate, respectively, while the remaining intervals exhibit secondary water absorption. These interpretation results are in good agreement with earlier oxygen activation tests. This study provides a theoretical basis and analytical method for applying distributed fiber optic temperature measurement technology to monitor water absorption profiles in multilayer injection wells. Full article

Raman Distributed Temperature Sensing (DTS) provides spatially distributed temperature measurements along optical fibers and is increasingly used for monitoring large-scale infrastructures and experimental facilities, enabling three-dimensional reconstruction of temperature fields. However, such measurements involve specific implementation constraints and may be affected by significant errors, with uncertainties influenced by factors such as calibration, environmental conditions, spatial resolution effects, and fiber positioning. Ensuring the metrological validity of Raman-based DTS measurements therefore requires a rigorous quantification of the associated measurement uncertainties. In this work, a complete uncertainty analysis of Raman-based DTS measurements is performed following the principles of the Guide to the Expression of Uncertainty in Measurement (GUM). A measurement model describing the relationship between Raman backscattered signals and temperature is established, and all relevant uncertainty sources are identified and quantified. The methodology is applied to a full-scale experimental facility equipped with a DTS interrogator and a dedicated calibration setup. Uncertainty propagation is performed using both first-order Taylor series expansion and Monte Carlo simulation, providing consistent results. The analysis shows that calibration uncertainty, spatial dispersion of the temperature field and fiber positioning within the reconstructed temperature field represent the dominant contributions to the combined uncertainty. The proposed approach provides a rigorous framework for the metrological qualification of Raman DTS systems and offers practical guidance for improving measurement reliability in distributed temperature monitoring applications. Full article

In-well hydraulic fracture monitoring based on joint low-frequency distributed acoustic sensing (LF-DAS)/distributed temperature sensing (DTS) enables the acquisition of optical fiber mechanical strain data, which reflect fracture propagation and rock deformation during hydraulic fracturing. This paper presents an analytical method to interpret the mechanical strain profile measured by in-well LF-DAS/DTS during the fracturing process based on strain transfer theory and the Sneddon solution for fracture propagation. The analytical method is validated by a numerical model that simulates the strain field induced by fracture propagation. The sensitivity of the fiber strain to key factors, such as fracture geometry parameters and gauge length, is analyzed. The results indicate that compressive strain in the formation adjacent to the propagating fracture remains observable from the mechanical strain profile under the low fiber lag parameter condition. The presented method is applied to analyze the mechanical strain profile measured from a fractured horizontal well. Considering the reactivation of the pre-existing fracture, the location of the fractures is identified, and the fractures’ geometric parameters are inverted. This study provides a quantitative evaluation method for fracture geometry characterization based on joint LF-DAS/DTS fracturing monitoring. Full article

With the exponential growth of natural gas reserves and utilization scale in China, underground gas storage (UGS) facilities—critical infrastructure within the natural gas production-supply-storage-sales system—have entered a phase of rapid expansion. As the core component connecting subsurface reservoirs with surface systems, wellbore integrity directly influences operational safety and service lifespan of UGS facilities. However, current leakage detection and integrity analysis methodologies for gas storage wellbores remain deficient in effective real-time monitoring capabilities. Traditional methods, however, are constrained by limited spatial coverage and insufficient precision, rendering them inadequate for comprehensive, continuous safety monitoring requirements. To address this industry challenge, this study proposes a real-time wellbore integrity monitoring framework based on distributed fiber optic sensing technology, integrating distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) devices into a synergistic monitoring system. The DTS component enables preliminary localization of potential leakage points through detection of minute temperature anomalies along the wellbore, while the DAS unit accurately identifies acoustic signatures caused by gas leakage within casings via monitoring of acoustic vibration signals propagating along the optical fiber. Through joint analysis of DTS and DAS data streams, real-time diagnosis of wellbore leakage events and integrity status can be achieved. Field trials demonstrated that this hybrid monitoring system achieved leakage localization accuracy within 1.0 m, effectively distinguishing normal operational signals from abnormal leakage characteristics. During actual monitoring operations, no indications of wellbore integrity compromise were detected; only minor noise and interference signals originating from surface construction activities were observed. Full article

Under the global trend of population aging, providing a safe and reliable living environment for the elderly who live at home has become a major social issue. This study reports a monitoring technology for elderly-friendly residential buildings based on distributed acoustic sensing (DAS) and distributed temperature sensing (DTS), which is used to monitor and identify the physical behaviors of residents and temperature changes at different locations in the space. The results show that the distributed acoustic sensing (DAS) system can initially identify typical behavioral states such as walking, squatting, and falling. The fiber DTS technology can not only monitor the temperature distribution at different locations indoors, but also be used for the monitoring and early warning of local fires in different areas of the room. The sensing probes of the monitoring system proposed in this paper are linear optical cables, which have the advantages of easy installation, strong anti-interference ability, intrinsic explosion-proof, less likely to leak residents’ privacy, all-weather operation, precise event location, and low cost for large-scale distributed measurement systems. By integrating the sensing optical cables, fiber signal processing systems, and application software introduced in this paper, an intelligent management and early warning platform for elderly-friendly residential buildings can be established, providing a new solution for remote supervision of the living safety of the elderly. Full article

The low-frequency distributed acoustic sensing (LF-DAS) data acquired through fiber-optic cables cemented behind the fracturing well casing can dynamically capture the hydraulic fracturing process. After removing the thermal effect, the LF-DAS data can reveal the strain evolution induced by the initiation of hydraulic fractures. This paper presented an improved strain–temperature decoupling method for LF-DAS measurements based on joint LF-DAS/distributed temperature sensing (DTS) monitoring. The decoupling method was based on strain change and temperature change pre-processed from the raw DAS and DTS data to avoid the enhancement of DTS data noise. The moving window function method and the image processing parameter cosine similarity was introduced to cope with the differences in temporal and spatial resolution between LF-DAS and DTS data. The region significantly affected by temperature change could be identified automatically and the mechanical strain change could be extracted. The tensile strain response generally reached a local peak at perforation clusters and increased significantly at those with dominant fracture fluid inflow. By analyzing the evolution of strain profile during fracturing, the effectiveness of multi-cluster fracture initiation and fracture temporary plugging could be evaluated. Full article

Production profiling is essential for optimizing production strategies in oil and gas wells. Conventional production logging tools provide only discrete, time-limited measurements and face operational challenges in long or complex horizontal wells. Distributed fiber-optic sensing (DTS/DAS) enables continuous, full-wellbore monitoring but each sensing modality has limitations when used alone: DTS interpretation is influenced by wellbore disturbances and thermal hysteresis, while DAS acoustic energy does not always correspond to actual inflow zones. This study proposes a joint interpretation method integrating DTS-based temperature inversion with DAS frequency-band energy and apparent velocity analysis. DTS data are processed using a coupled wellbore–formation heat-transfer model to obtain segmental flow rates, while DAS data are analyzed using short-time Fourier transform, cross-correlation, and Hough transform to extract positive and negative apparent velocities indicating fluid migration directions. Field results show that high-production intervals at 4126–4486 m correlate with positive apparent velocities, whereas medium-/low-production and shut-in stages exhibit persistent negative velocities linked to backflow and reinjection. The combined interpretation effectively distinguishes reservoir inflow from wellbore flow by jointly constraining thermal response and flow direction, thereby reducing uncertainties associated with single-method analysis. Full article

Structural health monitoring (SHM) has evolved into an indispensable component for ensuring the safety, durability, and life-cycle efficiency of civil infrastructure. Over the past five years, significant technological advancements have been made in innovative sensing systems, facilitating real-time assessment of structural performance and the early detection of deterioration. This comprehensive review presents recent developments in smart sensor-based SHM, with particular emphasis on the convergence of the Internet of Things (IoT), artificial intelligence (AI), and digital twin (DT) frameworks. Our review critically examines advances in fiber-optic, piezoelectric, MEMS-based, vision-based, acoustic, and environmental sensors, as well as emerging multi-sensor fusion architectures. In addition, bibliometric insights highlight the significant rise in global research activity and influential thematic clusters in SHM between 2020 and 2025. The discussion underscores how AI-integrated data analytics, IoT-enabled wireless networks, and DT-driven virtual replicas enable intelligent, autonomous, and predictive monitoring of bridges, buildings, tunnels, and other large-scale civil infrastructure. Field deployments and case studies are analyzed to bridge the gap between laboratory-scale demonstrations and real-world implementation. Finally, key scientific and practical challenges—including the durability of embedded sensors, the interoperability of heterogeneous data, cybersecurity in connected systems, and the explainability of AI models—are outlined to guide future research. Overall, this review positions contemporary SHM as a transition from traditional damage detection to comprehensive life-cycle management of infrastructure through self-diagnosing, data-centric, and sustainability-driven monitoring ecosystems. Full article

Rapid economic growth has directly driven up energy demand, and the gradual depletion of traditional fossil fuels has severely hindered sustainable development. Developing green and efficient geothermal exploitation technologies constitutes a crucial measure for tackling this sustainable development issue. This paper presents a field test associated with a clean energy system conducted in the Guanzhong Basin, China, with the core component of a coaxial casing deep geothermal well. A distributed temperature sensing system (DTS system) with over 3000 m-depth optical fiber installed and adopted to monitor near-wellbore formation temperature changes. Combining information on the inlet/outlet water temperature and flow rate monitored by an integrated temperature–pressure monitoring system, the heat transfer patterns during the operation of the deep geothermal well are deeply investigated. The research results demonstrate that a higher operation parameter of flow rates has a significant increasing effect on the heat transfer capacity of heat exchangers for coaxial casing deep geothermal wells. Although the increase in inlet temperature has minimal effect on the outlet temperature, it leads to a continuous decline in heat transfer capacity. In addition, as heat exchange duration extends, the geothermal gradient of the near-wellbore formation progressively declines. Full article

Leaks in ponds are a problem due to the loss of water resources, although the problem is greater when the ponds store livestock or agricultural waste (slurry or wastewater), in which case there is a risk of hydrogeological contamination of the environment. The proposed leak detection system is based on distributed temperature sensing (DTS) with hybrid fiber optics using the Raman effect. Using active detection techniques, i.e., applying a specific amount of electrical power to the copper wires that form part of the hybrid cable, it is possible to increase the temperature along the fiber and measure the thermal increments along it, detecting and locating the point of leakage. To validate the system, a full-scale prototype reservoir (25 m × 10 m × 3.5 m) was built, equipped with mechanisms to simulate leaks under the impermeable sheet that retains the reservoir’s contents. For environmental reasons, the tests were carried out with clean water. The results of the leak simulation showed significant differences in temperature increases due to the electrical pulse in the areas affected by the simulated leak (1 °C increase) and the areas not affected (5 °C increase). This technology, which uses hybrid fiber optics and a low-cost sensor, can be applied not only to ponds, but also to other types of infrastructure that store or retain liquids, such as dams, where it has already been tested, to measure groundwater flow, etc. Full article

The increasing deployment of photovoltaic (PV) installations presents critical challenges related to module safety and efficiency. Early detection of hotspots on PV modules is crucial to prevent degradation and mitigate fire risk. This study investigates the effectiveness of fiber optic sensors, specifically fiber Bragg gratings (FBGs) and distributed temperature sensing (DTS) based on Brillouin backscattering, to detect and localize hotspots on PV modules. Both sensor types successfully identified hotspot occurrences, with validation conducted through simultaneous thermocouple measurements and infrared thermographic imaging. The tests provide a comprehensive analysis of measurement methodologies, highlighting the advantages and limitations of fiber optic sensing techniques. While FBG sensors offer the most precise temperature measurements at the PV module surface, DTS systems demonstrate superior capability in hotspot detection. Full article

Groundwater contaminated with organic chemicals can be treated by raising the subsurface temperature, thereby enhancing the rate of microbial degradation. This process requires careful monitoring through space and time to ensure that heat is being delivered to the most contaminated regions of the groundwater system. Here, we demonstrate the effectiveness of a fiber-optic distributed temperature sensing (DTS) system as a high spatial and temporal resolution monitoring strategy. The DTS sensing system required the installation of fiber optic cable in the subsurface. Boreholes were drilled with hollow casings, a fiber optic cable was inserted into the casings, and then, the casings were withdrawn to allow the formation to collapse around the fiber. The fiber was then fusion-spliced into a single continuous fiber that could be interrogated by a Raman-based DTS unit. Temperature measurements were collected at 30 min intervals over a 575 m span with 0.25 m spatial sampling, resulting in over 110,000 temperature data points per day. With this high resolution monitoring the development of thermal plumes emanating from solar-heated borehole heat exchangers could be closely monitored. The pseudo-3D monitoring network showed the lateral and upward migration of the induced thermal plumes over time. This information was valuable for assuring the heated groundwater was contacting the intended treatment zone. Full article