Gas Pipeline Leakage Research Articles

Multi-Factor Coupling Analysis of Porous Leakage in Underwater Gas Pipelines

Natural gas pipeline leaks under the sea will have a significant negative effect on the marine ecosystem, result in significant financial losses, and possibly even harm marine floating objects. The VOF (Volume of Fluid) multi-phase flow model is used to numerically simulate and study the diffusion process of porous leakage in submarine gas pipelines. Experiments confirmed the model’s correctness and dependability. Based on this, the coupling effect and the porous effect of the leakage velocity, the size of the leaked pore, and water velocity of the natural gas pipelines on the diffusion of porous leakage in the submarine gas pipelines are analyzed with the test scheme designed by the orthogonal test method. The similarity principle is used to connect the leakage model with the actual application. The results show that three factors, namely, the leakage velocity, the size of leaked pore, and the water velocity, influence the shape of the air mass, the time when the gas reaches the sea surface, and the diffusion range. The size of the leakage hole and the leakage velocity have a substantial impact on the form of the air mass and the amount of time it takes for the gas to reach the sea surface, while the water velocity has no effect. Additionally, while there is essentially little impact from the leakage velocity, the size of the leaky pore and the water velocity have a significant impact on the diffusion range. Furthermore, the porous effect between leaky pores has a significant impact on the gas diffusion range. This study can serve as a guide for risk assessment and emergency decision-making regarding a submarine gas pipeline leak.

Integrated dynamic risk assessment of buried gas pipeline leakages in urban areas

In urban areas, buried gas pipeline leakages could potentially cause numerous casualties and massive damage. Traditional static analysis and dynamic probability-based quantitative risk assessment (QRA) methods have been widely used in various industries. However, dynamic QRA methods combined with probability and consequence are rarely used to evaluate gas pipelines buried in urban areas. Therefore, an integrated dynamic risk assessment approach was proposed. First, a failure rate calculation of buried gas pipelines was performed, where the corrosion failure rate dependent on time was calculated by integrating the subset simulation method. The relationship between failure probability and failure rate was considered, and a mechanical analysis model considering the corrosion growth model and multiple loads was used. The time-independent failure rates were calculated by the modification factor methods. Next, the overall evolution process from pipeline failures to accidents was proposed, with the accident rates subsequently updated. Then, the consequences of buried gas pipeline accidents corresponding to the accident types in the evolution process were modeled and analyzed. Finally, based on the above research, dynamic calculation and assessment methods for evaluating individual and social risks were established, and an overall application example was provided to demonstrate the capacity of the proposed approach. A reliable and practical theoretical basis and supporting information are provided for the integrity and emergency management of buried gas pipelines in urban areas, considering actual operational conditions.

BI-IEnKF coupling model for effective source term estimation of natural gas leakage in urban utility tunnels

As an effective way to facilitate the increasing demand for reliable infrastructure, energy supply and sustainable urban development, underground utility tunnels have been developed rapidly in recent years. Due to the widespread distribution of utility tunnels, the safe operation of natural gas pipelines accommodated in utility tunnels has caused great concern considering fire, explosion, and other coupling consequences induced by the gas pipeline leakage. However, the limited information on leakage source terms in accidental leakage scenarios could preclude timely consequence assessment and effective emergency response. In this study, a BI-IEnKF coupling source term estimation (STE) model is developed, with the combination of gas dispersion model, Bayesian inference (BI) and iterative ensemble Kalman filter (IEnKF) method, to achieve the effective source term estimation (including leakage location and leakage rate) and gas concentration distribution prediction. The newly developed model is first evaluated by the twin experiment with good reliability and accuracy. Furthermore, three contributing factors affecting the performance of the developed BI-IEnKF coupling STE model were investigated to assist parameter selection for practical use. Additionally, the novel application of mobile sensors serving as an alternative for fixed sensors is explored, and an application framework is sequentially given to guide the deployment of the developed coupling model in utility tunnels. The results show that the developed model has great performance in accuracy, efficiency and robustness, as well as the potential to be applied in actual utility tunnel scenarios. This study can provide technical supports for safety control and emergency response in the case of natural gas pipeline leakage accidents in utility tunnels. Also, it could be helpful to reasonable references for gas lekage monitoring system design.

Ceiling radiation heat flux and downward received radiation heat flux of methane jet fire with hydrogen addition

Radiation and flame scale characteristics of ceiling jet are important key elements, which are related to the safety of hydrogen energy transport. In this study, the flame radiation characteristics caused by hydrogen-blended natural gas (mixed gas) pipeline leakage fire were investigated experimentally. The effects of heat release rate, hydrogen addition ratio, nozzle diameter and source-ceiling height are considered. The flame shape of the ceiling jet fire is modeled by a cylinder cone and a cylindrical model. The WSGGM model is applied to calculate the flame emissivity. The flame emissivity is quantized by the equivalent diameter and gas type. The flame emissivity, downward received radiative heat flux and radiative heat flux under ceiling increase with the increase of hydrogen addition ratio. The view factor is calculated by the solid flame model. Moreover, the calculated flame radiative heat flux is in a favorable agreement with experimental results. The research results may provide reference for risk assessment of hydrogen mixing.

Research on gas pipeline leakage model identification driven by digital twin

When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction.

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

The leakage of natural gas pipelines may cause significant safety accidents and cause severe economic losses to society. Therefore, timely detection and location of pipeline leak is an effective measure to reduce losses. Traditional methods need to extract the leak features from signals and then input them into convolutional neural networks to realize leakage diagnosis, which is a tedious process. This paper proposes a pipeline leak detection method based on residual connections and one-dimensional separable convolutional neural network (1D-RC-SCNN) and optimizes the structure and parameters of the model. The model uses one-dimensional separable convolution to replace the traditional convolutional neural network, which effectively reduces the number of model parameters. The improved residual connection can prevent the training data from overfitting and speed up the model convergence. The leakage signal after segmentation and preprocessing is directly sent to the 1D-RC-SCNN model, the leakage features are adaptively extracted, and then, the results are output through the Softmax classifier. The method directly performs end-to-end processing on the leaked signal, which improves the data processing speed. Finally, the cross-correlation method is used to locate the pipeline leak. The effectiveness of the proposed method is verified by building an experimental platform and compared with related models. The experimental results show that the method has high accuracy for the detection and localization of pipeline leak.

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.

Effects of Pipeline Pressure on Diffusion Characteristics of Leaked Natural Gas in Tunnel Space.

Pipeline transportation has become the main mode of natural gas transportation. Due to inevitable aging, corrosion, and third-party damage, natural gas pipeline leakage accidents occur frequently. Leakage in the tunnel will lead to the leakage and accumulation of natural gas, and the potential explosion risk will threaten the tunnel's safety. It is significant to elaborate on the diffusion behavior of leaked natural gas in tunnel space for the traceability of leakage points and the formulation of safety technical measures. In this paper, a scale-down experimental platform for natural gas pipeline leakage in the tunnel is built, and the influence of pipeline pressure on natural gas diffusion characteristics is described. The results show that the diffusion process of leaked natural gas in the tunnel space shows obvious segmentation characteristics, and the concentration of natural gas reaches the maximum at the end of the continuous leakage stage. The increased pipeline pressure promotes natural gas diffusion, and the concentration of natural gas under 1.0 and 1.2 MPa rises sharply. First dangerous time (FDT) and maximum accumulated concentration (MAC) have a negative correlation with the leakage distance, while FDT and MAC have a good exponential and linear relationship with the pipeline pressure (0.2-1.2 MPa), respectively.

Risk analysis of gas dispersion, fire and explosion due to gas pipeline leak at Onshore Receiving Facility of PT XYZ in Muara Karang using Aloha Software 5.4

This research identifies hazards, assesses risks, and recommends mitigations for the risk of gas pipeline leaks at Onshore Receiving Facility (ORF). The objectives of this study are to determine the probability of gas pipeline leaks at ORF and the level of risk of gas pipeline leaks at ORF using a risk matrix, to model gas dispersion, fire, and explosion using ALOHA software to assess the consequences of gas pipeline leaks at ORF, and to provide risk mitigation recommendations to the company if the risk level is unacceptable and the existing risk mitigation measures are not effective. This study is qualitative research in which the author develops a model from theoretical framework to conceptual framework, and then describes the results of the analysis descriptively. The hazard identification approach used historical data from the Hazard Operability’s Study (HAZOP) data, which found that the highest risk level was gas pipeline leaks in pipelines with a pressure of 350 psi. The probability of pipeline leaks was obtained from the Generic Failure Frequency (GFF) table from the existing Risk-Based Inspection (RBI) study. Consequence analysis was performed using modeling software ALOHA and MARPLOT with gas dispersion, fire, and explosion scenarios. The analysis showed that the risk level of gas dispersion, fire, and explosion scenarios were all still acceptable, referring to the criteria of NFPA 59A. Therefore, it can be concluded that the risk level of gas pipeline leaks at ORF is still acceptable, and the existing risk mitigation measures are sufficient.

The Signal Characteristics of Oil and Gas Pipeline Leakage Detection Based on Magneto-Mechanical Effects.

In order to solve the problem of the quantification of detection signals in the magnetic flux leakage (MFL) of defective in-service oil and gas pipelines, a non-uniform magnetic charge model was established based on magnetic effects. The distribution patterns of magnetic charges under different stresses were analyzed. The influences of the elastic load and plastic deformation on the characteristic values of MFL signals were quantitatively assessed. The experimental results showed that the magnetic charge density was large at the edges of the defect and small at the center, and approximately decreased linearly with increasing stress. The eigenvalues of the axial and radial components of the MFL signals were compared, and it was found that the eigenvalues of the radial component exhibited a larger decline rate and were more sensitive to stress. With the increase in the plastic deformation, the characteristic values of the MFL signals initially decreased and then increased, and there was an inflection point. The location of the inflection point was associated with the magnetostriction coefficient. Compared with the uniform magnetic charge model, the accuracy of the axial and radial components of the MFL signals in the elastic stage of the improved magnetic charge model rose by 17% and 16%, respectively. The accuracy of the axial and radial components of the MFL signals were elevated by 9.15% and 9%, respectively, in the plastic stage.

Resonant photoacoustic cells for laser-based methane detection

Abstract. Against the background of the steady increase in greenhouse gases in the atmosphere, a fast and inexpensive method for detecting methane is required. This applies to the direct measurement of the background concentration of methane in the atmosphere and also to the detection of leaks in natural gas pipelines. Photoacoustic (PA) sensors offer the possibility of highly sensitive gas detection and cost-effective design at the same time. In this work, we investigated a photoacoustic sensor for methane in low concentrations, focusing on a special cell design, the so-called T-cell. Different cylinder geometries of six T-cells and the influence on the sensor performance were examined. An interband cascade laser (ICL) with a central wavelength of 3270 nm was used for excitation and a micro-electromechanical systems (MEMS) microphone as detector. The detection limits achieved were below the methane background concentration in air of 1.8 ppm.

Gas Pipeline Leakage Detection Method Based on IUPLCD and GS-TBSVM

To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was decomposed into several intrinsic scale components (ISC) by the UPLCD algorithm. Then, the signal reconstruction process of UPLCD was optimized and improved according to the energy and standard deviation of the amplitude of each ISC, the ISC components dominated by the signal were selected for signal reconstruction, and the denoised signal was obtained. Finally, the TBSVM was optimized using a grid search algorithm, and a GS-TBSVM model for pipeline leakage identification was constructed. The input of the GS-TBSVM model was the data processed by the IUPLCD algorithm, and the output was the real-time working conditions of the gas pipeline. The experimental results show that IUPLCD can effectively filter the noise in the signal and GS-TBSVM can accurately judge the working conditions of the gas pipeline, with a maximum identification accuracy of 98.4%.

A novel method for defects marking and classifying in MFL inspection of pipeline

Magnetic Flux Leakage (MFL) testing is the most widely used non-destructive technique for the inner inspection of oil and gas pipelines. Accurate quantification of defects is a long-standing difficulty in the field of pipeline leak detection. Scientific marking and classification of defects is an important prerequisite for accurate quantification. A novel method of marking and classifying defects with MFL signals is proposed, which is aiming at the problem that it is difficult to mark and classify defects accurately due to the tremendous amount of oil and gas pipeline leakage magnetic detection data. An improved CLIQUE algorithm is used to mark the defect areas of segmented pipelines to estimate the number and location of defects. Then the 3D MFL characteristic signals of the marked areas are studied and extracted. The SSA_BP neural network is trained to classify the defects. The effectiveness and accuracy of the proposed method are tested and verified by using finite element simulation defects and actual defects. The results show that the method is more efficient in marking defects and more detailed in marking areas.

Research on the Propagation of Acoustic Signal and Attenuation Change Law of Gas Pipeline Double-Point Leakage

In order to better model the acoustic signal propagation and attenuation of pipeline leaks, a laboratory gas pipeline leak detection platform taking air as the experimental medium is hereby adopted to investigate the law of hole spacing on acoustic signal propagation and attenuation at different pressures for double-point leaks. The results show that in the case of a fixed pressure, the amplitude of the three hole spacing in the propagation along the pipe decreases gradually, while that near the leak point decreases the most, with a maximum difference being up to 90%, after which the difference gap between the amplitude is gradually narrowed; different hole spacing has little effect on the RMS voltage along the downstream of the double-point leak pipe but exercises a greater effect on the RMS voltage propagation between the double-point leak; the attenuation coefficient of the leak signal decreases generally with the distance, which generally becomes smaller with the increasing distance and that along the downstream of the pipeline and near the leakage point decreases the most in the case of the double-point leakage, with a decrease rate reaching close to 50%. Then, the decrease rate becomes smaller gradually and reaches close to 20%, while that of the attenuation coefficient of the two distances between the three sensors is relatively close for the double-point leakage, with the first section close to 50% to 60% and the second section close to 60% to 80%. Under the condition where the pressure is different and the hole spacing remains the same, the acoustic signal increases with the increasing pressure when the attenuation coefficient decreases.

A simulation-based optimization method for emergency evacuation induced by gas pipeline leakage risk

Long-distance gas pipelines with large diameter and high pressure, on which a leakage induces gas diffusion or fire explosion, may result in major influence to nearby buildings and residents. Safety and rapid evacuation of potentially affected people is a top priority. To analyze the affected areas by disasters and improve evacuation efficiency of the affected areas, this study presents a simulation-based optimization method for emergency evacuation induced by gas leakage risk. First, the influence radii of different leakage accidents were calculated based on damage criteria and the evacuation radii around the accidents were determined considering the panic psychology. The number of evacuees and their spatial distribution were calculated. Secondly, an evacuation simulation model for affected communities based on the multi-agent system was established to analyze the evacuation process of residents. Finally, the optimal design method and strategies for community evacuation were proposed. Responsibility areas of organized evacuation service for community exits were determined. The results showed that the evacuation times of the two communities A and C were reduced by 10% and 24%, which indicates organized evacuation is more efficient than unorganized evacuation. The selection of community exits is more balanced. Its rationality of the proposed method was verified by the comparison of evacuation simulation.

Edge Computing Offloading Method Based on Deep Reinforcement Learning for Gas Pipeline Leak Detection

Traditional gas pipeline leak detection methods require task offload decisions in the cloud, which has low real time performance. The emergence of edge computing provides a solution by enabling offload decisions directly at the edge server, improving real-time performance; however, energy is the new bottleneck. Therefore, focusing on the gas transmission pipeline leakage detection scenario in real time, a novel detection algorithm that combines the benefits of both the heuristic algorithm and the advantage actor critic (AAC) algorithm is proposed in this paper. It aims at optimization with the goal of real-time guarantee of pipeline mapping analysis tasks and maximizing the survival time of portable gas leak detectors. Since the computing power of portable detection devices is limited, as they are powered by batteries, the main problem to be solved in this study is how to take into account the node energy overhead while guaranteeing the system performance requirements. By introducing the idea of edge computing and taking the mapping relationship between resource occupation and energy consumption as the starting point, the optimization model is established, with the goal to optimize the total system cost (TSC). This is composed of the node’s transmission energy consumption, local computing energy consumption, and residual electricity weight. In order to minimize TSC, the algorithm uses the AAC network to make task scheduling decisions and judge whether tasks need to be offloaded, and uses heuristic strategies and the Cauchy–Buniakowsky–Schwarz inequality to determine the allocation of communication resources. The experiments show that the proposed algorithm in this paper can meet the real-time requirements of the detector, and achieve lower energy consumption. The proposed algorithm saves approximately 56% of the system energy compared to the Deep Q Network (DQN) algorithm. Compared with the artificial gorilla troops Optimizer (GTO), the black widow optimization algorithm (BWOA), the exploration-enhanced grey wolf optimizer (EEGWO), the African vultures optimization algorithm (AVOA), and the driving training-based optimization (DTBO), it saves 21%, 38%, 30%, 31%, and 44% of energy consumption, respectively. Compared to the fully local computing and fully offloading algorithms, it saves 50% and 30%, respectively. Meanwhile, the task completion rate of this algorithm reaches 96.3%, which is the best real-time performance among these algorithms.

Real-Time Identification of Natural Gas Pipeline Leakage Apertures Based on Lightweight Residual Convolutional Neural Network

Deep-learning techniques have been widely used in pipeline leakage aperture identification. However, most are designed and implemented for offline data, with problems such as large parameters, high memory consumption, and poor noise immunity. To solve the problem, this article presents a lightweight residual convolutional neural network (L-Resnet) applied to a real-time detection platform to achieve real-time identification of pipeline leakage apertures. First, based on the depth separable technique, two different separable residual modules are constructed to realize the feature extraction of signals; then, a more efficient activation function is applied to the high-dimensional space to enhance the nonlinear capability of the model; after that, a lightweight attention mechanism is used to weight the features to distinguish the importance of different features; finally, the classification results are obtained by a classifier. The real-time detection platform consists of Jetson Nano, the signal acquisition module, and the processing circuit. The results indicated that the method could accurately identify the pipeline leakage apertures in real time. Moreover, the number of parameters is only 14.71 kb, and the model has good computing efficiency and robustness compared to other methods.

Leakage aperture identification of natural gas pipeline based on compressed acquisition and DSAE

Traditional gas pipeline leak detection methods usually face challenges in that there are redundant data collected on account of the time-domain sampling theory and the valuable leakage information is hidden in the complex vibration signals. This paper put forward an intelligent aperture identification method for natural gas pipeline leakage. This method integrated the compression of monitoring data based on compressed sensing (CS) transformation and automatic feature extraction and identification based on deep neural networks, namely, denoising sparse autoencoder (DSAE). The compressed acquisition can greatly reduce the volume to be processed and effectively extract the aperture information. DSAE combined the merits of sparse auto-encoder (SAE), denoising coding and dropout to achieve robust feature extraction and aperture classification. Experimental results validate the effectiveness and good performance, and the proposed method can realize faster and more accurate identification of different leak sizes compared with the traditional methods.

On-site leakage locating of underground natural gas pipeline based on Ne tracer by miniature time-of-flight mass spectrometry

Natural gas pipeline leakage seriously endangers people's lives and properties, and there is an urgent need for on-site, rapid, and accurate locating the leakage point of the underground natural gas pipeline. Here, we added neon gas to natural gas pipelines as a tracer gas, and used a miniature time-of-flight mass spectrometry (mini-TOFMS) to on-site detect neon gas to quickly locate the leak point of underground natural gas pipelines. The mini-TOFMS used capillary tube sampling to directly analyze the leaked neon gas without sample preparation, and the analysis time of a single sample was only 60 s, which was less than one-seventeenth that of traditional off-line gas chromatography (GC) method. The mini-TOFMS exhibited a linear response range from 69 ppmv to 3.0 × 105 ppmv with the limit of detection (LOD, S/N = 3) of 19.0 ppmv. The correlation of GC and mini-TOFMS for Ne quantitative analysis was as high as 0.98. The performance of the newly designed method with the mini-TOFMS was demonstrated by on-site locating the underground natural gas pipeline leakage point in the experimental station. And leakage point of the natural gas pipeline, especially for those pipelines with different gas pressure buried under the same road, can be found more efficiently and accurately.

Leakage detection in natural gas pipeline based on unsupervised learning and stress perception

Natural gas pipeline leakage can cause serious financial losses to natural gas transportation and pose accidents to the environmental safety. Currently-used supervised learning methods heavily rely on sufficient pipeline failure historical data for their training. Therefore, we propose a novel detection approach based on unsupervised learning and stress perception for determining the leakage situation in pipelines. In this study, pipeline stress signals are first acquired based on residual magnetic effect. The relationship between residual magnetic and stress is built using improved sparrow search algorithm (ISSA) and extreme learning machine (ELM). Then, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is deployed to learn suitable features from the stress signals under the pipeline normal condition, generating high-quality stress data features. Finally, the generated stress features are supplied to the Bayesian Gaussian mixture model (BGMM). And the weighted logarithm probability (WLP) is used as the health indicator for examining pipeline status. The results demonstrate that the relative error of residual magnetic stress model is controlled within 3 %, and the WLP value of fault samples is smaller than − 100, so that the proposed method can discriminate the normal and leak conditions as well as the risk and severity of leakage. This study provides a theoretical basis and new perspective for pipeline leakage detection.