Detection of Small Leaks in Liquid Pipelines Utilizing Distributed Temperature Sensing

Fibre-optic based Distributed Acoustic Sensing (DAS), is now commonly used around the world for providing pipeline operators with real-time and early warning of intrusions on their pipeline Right of Ways. Also open to analysis within the DAS signal return are multiple signatures derived from the egress of product leaking from pipelines. Over the last three years, focussing attention on these alternative signals, OptaSense have developed a novel 4-mode External Leak Detection capability, fusing outputs from both DAS, Negative Pressure Pulse (NPP) and Distributed Temperature Gradient Sensing (DTGS) signals (the latter not to be confused with Distributed Temperature Sensing or DTS). The first commercially deployed 4-mode Leak Detection products are now being seen on the market, for both gas and liquids pipelines. In this paper, we report how DAS can be used to provide these four modes of leak detection — including (listed in order of typical detection latency, fastest to slowest) i) negative pressure waves created in the pipeline product from the leak event ii) acoustic noise from turbulent flow through the leak orifice iii) temperature gradients in the soil due to the presence of the leaked product (positive and negative), and iv) local strain/ground heave due to soil displacement by the leaked product. These acoustic, temperature and strain measurements using a fibre-optic cable buried next to a pipeline can be fused together to provide highly sensitive and reliable alerts for pipeline leaks. The pipeline industry has always sought to detect smaller leaks faster, with better locational accuracy. This paper, which draws upon industry sponsored test results and commercial deployment data, provides an update to the industry on leak detection possibilities using DAS.

The relentless pursuit of well integrity is a critical mission in the oil and gas industry, especially in mature fields where the risks of casing leaks loom large. This study from Kuwait highlights the necessity of constant vigilance to proactively tackle these leaks, which threaten both production efficiency and environmental safety. Emphasizing early leak detection, this research aims to demonstrate how advanced methodologies can significantly reduce the costs and risks associated with extensive workover operations and the potential need for well suspension or abandonment. The focus lies in accurately identifying and quantifying casing leaks, including their depth and directional flow, crucial for effective well diagnostics and integrity management. This study introduces a cutting-edge approach for pinpointing casing leaks in wells, especially those with simpler completion designs. It showcases the deployment of an optical fiber-enabled coiled tubing system to implement distributed temperature sensing (DTS). This innovative combination enables real-time downhole measurements, including pressure and casing collar locator (CCL) readings for precise depth determination. The key advancement here is the ability to capture temperature anomalies in real-time, offering a comprehensive and dynamic profile of both the borehole environment and the surrounding formation. This approach represents a significant leap forward in well diagnostics technology. In a North Kuwait oil-producing well with a deviated trajectory, conventional production logging tools (PLT) were inadequate in locating a casing leak. Utilizing well hydraulics analysis, the DTS technology, facilitated by fiber optics-equipped coiled tubing, was employed. Different operational stages were correlated through a series of DTS surveys, ultimately leading to the successful identification of the leak. Notably, this operation was performed without a rig, incorporating a novel nitrogen injection workflow to stimulate leak production. This approach, a first of its kind, marked a shift in industry practices. The precision of this Coiled Tubing and fiber optic system in detecting temperature variations at the influx point was unprecedented, showcasing the method’s effectiveness. This case study offers invaluable insights into leveraging DTS in conjunction with Coiled Tubing for diagnosing completion leaks. The innovative strategy of nitrogen injection via the annulus of the kill string, enhancing fluid column visibility and inducing wellbore production, while simultaneously monitoring temperature shifts, represents a significant breakthrough. This approach not only confirms the leak location with enhanced accuracy but also adds a new layer to the standard DTS protocol. It sets a new benchmark for industry practices in leak detection and well integrity assurance.

Distributed Fiber optic sensing system is a unique tool for the evaluation of distributed temperature over several kilometers. It is a powerful diagnostic instrument for the identification and localization of potential problems, such as leakages in pipelines and dykes, hot-spots in high-voltage cables and other events that create temperature anomalies. Such distributed temperature sensing (DTS) systems have the advantage of being relatively easy to deploy over long pipeline sections and have been shown to detect leakages events with good accuracy and reliability. However, when distributed fiber optic sensing systems are deployed in security-critical applications, where availability and reliability are crucial, it is important to continuously verify and assess the correct functioning and reliability of the whole system, including the sensing cables, the measurement system, the data analysis software and the alert transmission. In the past, such testing have been performed periodically by the pipeline personnel, but testing frequencies are typically low, e.g. once per year. The DTS Automated Trip Testing System is a fully independent device that is able to produce a controlled and localized thermal anomaly (hot spot or cold spot) and verify its correct detection. This allows a continuous verification of the DTS system reliability and functionality and a periodic statistical evaluation of the confidence level (proven by experience SIL rating). This paper will present more specifically the development, the functioning and deployment, and its applications of an automated system and method for testing the efficacy and reliability of distributed temperature sensing (DTS) systems, in particular those DTS systems used for pipeline leakage detection.

Designs and preliminary results from a multi-year project for simultaneous Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) executed across multiple wells on a Steam-Assisted Gravity Drainage (SAGD) field in Canada is presented. Primary objectives of the project were to demonstrate prototypical execution of DAS and DTS data collection on a standard SAGD pad setup and to characterize inflow across the production zone. Challenges and learnings from this technically demanding project are explored alongside a discussion on the improving viability of continuous acoustic monitoring to augment temperature sensing commonly utilized in SAGD instrumentation design. The DTS technique employs a combination of an optoelectronics topside instrument and a downhole sensing fiber to produce a distributed temperature profile of the fiber string. The topside instrument comprises a laser light source, a fast optical detector and a combination of electrical and optical circuits to control light pulsing, and the detection and measurements of Raman backscattered light containing encoded temperature information about the temperature profile. The sensing fiber type is selected based on suitability with the topside instrument and the expected maximum operating temperature of its intended sensing environment. While DTS is compatible with both single-mode (SM) and multi-mode (MM) fiber type, MM graded-index fibers are often selected for SAGD environment due to its larger optical core size. Using a MM fiber allows for increased capture of the backscatter Raman light, a much weaker light signal than the laser light launched into the optical fiber. Temperature dependent Raman signals are extracted from the returned light and by applying the Optical Time Domain Reflectometry (OTDR) technique, which produces a temperature trace consisting of temperature magnitude matched to each unit distance along the sensing fiber. The temperature trace can vary in its spatial resolution subject to the topside instrument configuration with typical resolving resolution between 0.5 – 1.5 meters. Other general DTS sensing techniques are described in the literature (for example, Hartog (2017)) but are not described here. Some of the earliest uses of DTS in thermal enhanced oil recovery (EOR) was to monitor steam breakthrough and pump vapor locking in California's steam flood as reported by Carnahan (1999). Since then, advancement in specialty fiber optics suitable for thermal environment upwards to 300°C has enabled mainstream adoption within SAGD. Uses of DTS data has expanded to include, but not limited to, monitoring of well and steam chamber conformance, tubing and casing integrity, gas ingression and water coning. DAS is an optical interferometry technique employing an optoelectronics topside instrument and a downhole sensing fiber to detect minute changes in fiber length at each location along the fiber string. The instrument is sensitive at the pico-strain (ρε) level with a spatial resolution equal to a configurable distance referred to as the gauge length. The term "acoustic" in Distributed Acoustic Sensing broadly designates any mechanical perturbation able to alter the local state of strain anywhere along the optical fiber, be it from dynamic pressure changes, or dynamic strain (e.g. vibration). While DAS is compatible with both SM and MM fiber type, deployment alongside SM fiber types have been traditionally preferred as the laser light coherence is better preserved throughout its round-trip propagation compared to MM fiber. However, DAS with MM fiber can still be used for typical fiber lengths in SAGD wells without significant penalty in signal quality as shown in literatures (MacPhail (2016), Burke (2022). This cross-compatibility with MM fiber allows collection of DAS on existing DTS fibers deployments without further intervention. Prior to DAS adoption in oil and gas, it was predominantly deployed in defense and security application to perform parameter monitoring and ground disturbance detection. As DAS technology evolved, it was first trialed in Shell's unconventional tight gas wells in February 2009 to monitor plug setting, perforation shots and active clusters during fracking operation (Molenaar et al. 2012). In EOR application like SAGD, DAS provides an augmenting dataset correlating to production inflow phases (MacPhail et al. 2016) which would not be readily detectable in the temperature domain alone. Efforts like the one presented in this paper are underway to progress horizontal inflow profiling and inferred relative volumes which aids in understanding well productivity and efficacy of added inflow control devices (ICD). Unlike DTS, acoustic data is largely unaffected by temperature masking due to well completions. For events where insufficient temperature deviation are produced, DAS may be used to surveille for common industry challenges such as steam coning, gas ingression, loss of tubing/casing integrity and more. The emerging market segment for Carbon Capture and Underground Storage (CCUS) is poised to utilize DAS as one of the many leak detection toolkit for the injection monitoring wells.

Advanced thermal fluid leakage detection system with machine learning algorithm for pipe-in-pipe structure

Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) measurements are technologies which are adding some benefits in the aim to replace or complete traditional logging measurements like noise logging tools (NLT) or production logging tools (PLT). The aim of well integrity interventions using distributed fiber optic sensing (DFOS) is to significantly reduce the duration and the cost of these operations, and to provide additional information in comparison to traditional logging tool. The combination of DAS and DTS can offer both qualitative and quantitative information regarding fluid dynamics in the context of well integrity investigation, as the flow characteristics (intensity of turbulences). In addition to acoustic (high frequency DAS) and temperature (DTS) monitoring for leak detection, we propose to use DTGS (Distributed Temperature Gradient Sensing) with low frequency DAS (< 1 Hz) to monitor leaks on the completion tubing of a well. The main advantage of using DTGS with DAS is its sensitivity to small leaks, including the direction of propagation of the leak.

Fiber Optic Liquid Leakage Detection (FOLLD) project has permitted of assessing on the DTS (Distributed Temperature Sensing), the DSTS (Distributed Strain and temperature Sensing) and the DAS (Distributed Acoustic Sensing) capabilities, combined with a Standard Telecom Fiber Optic Cable (FOC), for detecting small pressurized (20 barg) and pre-heated (50 °C) liquid (water) leakages on a buried dummy pipeline. DTS/DSTS real time results demonstrated that the soil disruption, the soil and external environmental temperature variation had an influence on leakage detection results robustness. Consequently, only the first three (3) trials out of the ten (10) trials provide some comprehensive results. Real time results shown that two (2) out of three (3) DSTS devices were able to detect a leakage up to twenty (20) km with a detection success rate of 67% in the position P1 or P2 or P3. One (1) out of two (2) DTS device detects up to twenty (20) km with a detection success rate that depends on the fiber optic position (67% for P2 or P3 and 33% for P1). Only one (1) DSTS shows a detection success rate up to fourty (40) km with a detection success rate of 67% in the position P1 or P2 or P3. DTS/DSTS real time results highlight that 100% successful detection rate can be reached up to twenty (20) km with several fiber optic cables in the position: P1/P2 or P1/P3 or P1/P2/P3. For DAS systems, the real time results shown that it was not possible to distinguish a leakage "noise" from other surrounding noise. This was especially due to the water pump noise, which was always occurring simultaneously with the leakage. Only a post processing treatment provide the leakage detection using a DAS technology. Post treatment DTS/DSTS and DAS detection success rate are better than the real time results. This highlight the potential of both technologies. All DTS/DSTS and DAS testing results demonstrated that the detection success rate are Supplier's dependent.

New mathematical formulations for accurate estimate of nitrogen leakage rate using distributed temperature sensing in Mechanical Integrity Tests

In detection and localization of leakage from pipeline, as the signal of pressure is usually mixed with noise, the singularity of the negative pressure wave generated by a small leakage can not be accurately extracted by using conventional methods and thus the leakage can not be accurately located. For the above reasons, a practical method based on improved harmonic wavelet was used. This method is to identify small leakage signal. The fine features, the basic principle and the shortcomings of harmonic wavelet were presented. Concerning its shortcomings of poor positioning in the time domain, an improved harmonic wavelet was proposed. And then in order to accurately locate leakages from pipelines, the inflection points of negative pressure waves were accurately extracted by using the improved harmonic wavelet. The experimental results show that the improved harmonic wavelet has improved the capability of positioning in the time domain in order to locate more accurately.

Detection of small leakage from long transportation pipeline with complex noise

Abstract. We present a novel, fine-resolution temperature profiling method based on Distributed Temperature Sensing (DTS) that is adaptable, reproducible, and fully FAIR. Accurate probing of near-surface temperature gradients requires sub-centimeter resolution, particularly in environments with short vegetation such as grass, where strong insulating properties promote steep gradients. Conventional DTS systems provide spatial resolutions of approximately 25 cm along fiber optic cables that can span several kilometers. By compacting such cables into a helical coil supported by a laser-cut frame, the Fine Resolution Adaptable Distributed Temperature Sensing (FRADTS) method attains vertical resolution and accuracy at the millimeter scale. The frame design is generated by a parametric script that outputs laser-cutting files, enabling users to assemble coil structures from sheet material with identical or easily adapted geometries. We demonstrate the method in both laboratory tests and a field campaign at the CESAR atmospheric observatory in Cabauw, the Netherlands, where a prototype coil captured high-quality vertical temperature profiles within the lowest meter above the soil, including a 10 cm grass layer. A resolution of 1.3 mm was attained and verified, and the influence of environmental factors such as solar radiation and precipitation on measurement biases was mapped and quantified. Despite minor artifacts, the method proved robust and effective, providing high-quality profiles under a wide range of weather conditions. As the method is modular and parametric, it can easily be applied in other research, potentially extending its application to other fields.

Arctic permafrost is rapidly degrading in response to global warming. Its thermodynamic evolution governs carbon emissions, hydrological shifts, and terrain stability, with critical consequences for both natural systems and built infrastructure. Accurate prediction of the thermodynamic behavior of permafrost remains elusive, hindered by limited observations and underdeveloped methodologies. Here, we introduce a digital twin framework that integrates differentiable modeling (DM) with high spatial resolution distributed temperature sensing (DTS) data to predict and infer key permafrost characteristics—ground temperature, unfrozen water content, thermal conductivity, and heat capacity. By leveraging a neural‐network‐based parameterization, our framework fuses observational data with physical heat transfer equations, enabling real‐time calibration and updating of the spatiotemporally varying soil thermodynamic characteristics. Applied to permafrost beneath a road embankment in Utqiaġvik, Alaska, the digital twin accurately reconstructs the spatiotemporal evolution of soil temperature fields and captures spatial variability in permafrost thermodynamic properties. The prediction results were further validated against shear‐wave velocity distributions inferred from distributed acoustic sensing (DAS), temperature data obtained from borehole thermistors, and thermodynamic properties measured by laboratory testing, demonstrating the framework's robustness. This work advances the predictive understanding of permafrost dynamics under climate change and establishes a generalizable pathway for digital twin applications in Arctic science.

Pipeline small leak detection based on virtual sample generation and unified feature extraction

The small leak in the propulsion system pipeline of the sounding rocket is prone to occur in the connections because of the screw thread loosening. Due to economic and technical bottleneck, the traditional soap bubble method is widely used in practice to evaluate whether existing a leak or not by visually observing the bubble’s size and numbers. Thus doing so will result in the low inspection efficiency and high cost. Using acoustic emission (AE) techniques, this paper presents an experimental study on small leak detection on the screw thread connection in the propulsion system pipeline of sounding rocket. The time and frequency characteristics of the corresponding small leak AE signals are investigated. After characteristic indices extraction and selection, the multi-class support vector machines (MCSVM)-based leak rates recognition algorithm in One–vs–All (OVA) is proposed. It has been validated that, for the propulsion system pipeline of the sounding rocket, the dominant characteristic frequency band of the small leak AE signals induced by screw thread loosening concentrates on 35–45 kHz. The proposed optimal OVA SVM models can achieve good classification accuracy of >98% by using the characteristic index set {Envelope area, standard deviation (STD), root-mean-square (RMS), Energy, Average frequency} and Gaussian Radial Basis Function (RBF) kernel function. The drastic drops in the false alarm attribute to use the combination of time- and frequency-domain characteristic indices. Especially, once adding the “Envelope area” into the characteristic index set, the classification accuracies of the OVA SVM models are further improved significantly regardless of the effect of kernel functions.

Hazardous gas leakage in the petrochemical industry frequently results in major incidents. A significant challenge arises due to the limitations of the current gas plume target feature extraction and identification techniques, which reduce the automated detection capabilities of remote monitoring systems. To address this, we propose BBGFA-YOLO, a real-time detection method leveraging background information and an improved YOLO network. This approach is designed specifically for the infrared imaging of gas plume targets, fulfilling the requirements of visual remote monitoring for hazardous gas leaks. We introduce a synthetic image colorization method based on background estimation, which leverages background estimation techniques to integrate motion features from gas plumes within the synthesized images. The resulting dataset can be directly employed by existing target detection networks. Furthermore, we introduce the MSDC-AEM, an attention enhancement module based on multi-scale deformable convolution, designed to enhance the network’s perception of gas plume features. Additionally, we incorporate an improved C2f-WTConv module, utilizing wavelet convolution, within the neck stage of the YOLO network. This modification strengthens the network’s capacity to learn deep gas plume features. Finally, to further optimize the network performance, we pre-train the network using a large-scale smoke detection dataset that includes reference background information. The experimental results, based on our self-acquired gas plume dataset, demonstrate a significant improvement in detection accuracy with the BBGFA-YOLO method, specifically achieving an increase in the average precision (AP50) from 74.2% to 96.2%. This research makes a substantial contribution to industrial hazardous gas leak detection technology, automated alarm systems, and the development of advanced monitoring equipment.