Gas Pipeline Leak Detection Research Articles

Research on oil and gas pipeline leak detection method based on 1DCNN-DBO-LSTM

Abstract Detecting leaks in oil and gas pipelines is crucial for the safe operation of these systems. Current techniques for identifying oil and gas pipeline breaches are insufficient and fail to utilize the temporal attributes of the leak signal. This research proposes a fusion recognition model that integrates a long short-term memory network (LSTM) optimized using a one-dimensional convolutional neural network (1DCNN) with the dung beetle optimization algorithm (DBO). The model initially tackles the issue of excessive depth in the 1DCNN network, which paradoxically results in decreased accuracy and increased computational load. It develops a lightweight 1DCNN network via structural experiments that adaptively extracts data features. Subsequently, to mitigate the effects of the LSTM model's high stochasticity and the challenges associated with manual hyperparameter selection on detection accuracy, an enhanced LSTM model is proposed. This model is optimized through the integration of the Dung Beetle Optimization Algorithm (DBO) to refine the hyperparameters in LSTM. A 1DCNN-DBO-LSTM recognition model is developed, whereby the data features retrieved adaptively from the 1DCNN are fed into the enhanced DBO-LSTM for classification, and the T-SNE dimensionality reduction technique is utilized to visualize the network at each output layer. The model is assessed utilizing the precision and duration measures. The suggested model enhances recognition accuracy and reduces detection time compared to existing advanced models. The approach presented in this work can extract pipeline data features more swiftly and accurately, thus enhancing classification precision.

Gas Pipeline Leak Detection by Integrating Dynamic Modeling and Machine Learning Under the Transient State

This study focused on developing machine learning models to detect leak size and location in transient state conditions. The model was designed for an onshore methane–hydrogen blending gas pipeline in Canada. Base case simulations revealed significant effects on mass flow and pressure due to leaks, with the system taking approximately 6 h to reach a steady state from transient conditions. This made it essential to analyze the flow characteristics during the transient state. Trend data from the pipeline’s inlet and outlet were examined, considering the leak size and location. To better represent the data over time, a method was used to create two-dimensional images, which were then fed into a CNN (convolutional neural network) for training. The model’s accuracy was assessed using classification accuracy and a confusion matrix. By refining the data acquisition process and implementing targeted screening procedures, the model’s classification accuracy increased to over 80%. In conclusion, this study demonstrates that machine learning can enable rapid and accurate leak detection in transient state conditions. The findings are expected to complement existing leak detection methods and support operators in making faster, more informed decisions.

Artificial Intelligence based Leak Detection in Blended Hydrogen and Natural Gas Pipelines

In the transition towards a hydrogen-based energy system, the strategic use of pipelines is crucial for efficient hydrogen distribution. Leveraging existing natural gas pipelines to carry a mix of hydrogen and natural gas offers a cost-effective alternative to building new infrastructure. This study explores the development of a leak detection system compatible with existing pipelines, specifically tailored for the blended hydrogen and natural gas mix. Given the scarcity of leak data for blended hydrogen-natural gas pipelines, the study introduces a Real-Time Transient Model (RTTM) for blended gases, simulating leak dynamics to generate necessary data. Additionally, a leak detection system (LDS) is developed using a fusion of Convolutional Neural Network (CNN) and Explainable Artificial Intelligence (XAI) through Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This LDS framework overcomes the “black box” issue common in AI-driven systems, enabling reliable detection. The integration of Explainable and traditional AI techniques holds promising implications for blended hydrogen pipelines by enhancing the safety and efficiency of hydrogen transportation, thereby mitigating economic and environmental impacts, and addressing public concerns.

ECNet: Network Based on Encoder With Local Convolutional Attention and Cross Layer Information Fusion for Natural Gas Pipeline Leakage Detection

ECNet: Network Based on Encoder With Local Convolutional Attention and Cross Layer Information Fusion for Natural Gas Pipeline Leakage Detection

Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm

Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm

EMDet: An entropy blending and multi-link parallel feature enhancement detection model for gas pipeline weak leakage detection

To improve the detection accuracy of gas pipeline leakage detection (PLD) especially when the leakage is weak, we propose a hybrid deep learning model, named EMDet in this paper, which incorporates two proposed algorithms called entropy blending (EB) and multi-link parallel feature enhancement (MPFE). Firstly, the EB algorithm is used to determine the optimal number of decomposition layer Lo and the optimal intrinsic mode function Io for variational mode decomposition (VMD), elevating the leakage-related information in Io. Then, three coarse-grained scales have been applied to extract effective and robust features, where the coarse-grained scales are calculated based on the sizes required by the MPFE algorithm. Moreover, the MPFE algorithm is proposed to enhance the representation of features, thereby improving the detection accuracy of PLD. After that, the performance of the proposed EMDet has been evaluated on the negative pressure wave (NPW) data collected from realistic urban environments. Compared with the state-of-the-art PLD models, the performance of EMDet not only reaches the total accuracy of 98.67%, the F1-score of 98.65%, and the fault detection rate of 98.68%, but also reduces the false alarm rate to 0.66% and the missing alarm rate to 1.32%. Finally, the robustness results demonstrate the superior performance and broad application prospects of EMDet for PLD.

Gas pipeline leakage detection and location by using w-FBG array based micro-strain sensing technology

In addressing the significant security concerns associated with urban natural gas transportation, particularly the potential catastrophic consequences of leaks, this paper introduces a novel approach for detecting and locating leaks in natural gas pipelines using weak fiber Bragg grating (w-FBG) array micro-strain sensing technology. The field pipeline model is established, w-FBG array is applied to pipeline leakage experiment, and the pipeline leakage is detected by analyzing the change trend of micro-strain. The pipeline leakage experiments under different pressures were carried out. The experimental results show that there is a good linear relationship between the pipeline leakage pressure and the wavelength change of w-FBG, and the linearity is greater than 0.99, which is consistent with the theoretical analysis and verifies the applicability of w-FBG. According to the characteristics of micro-strain transfer in pipeline leakage process, a ‘abrupt change point’ capturing method based on peak to peak slope and threshold judgment is proposed. Results indicate that the proposed algorithm effectively detects pipeline leaks and accurately locates the point of leakage, with a positioning error of approximately 0.5 m. Compared with the traditional method, the proposed method has higher precision.

Optimized variational mode decomposition denoising method based on weighted mean of vectors algorithm

Aiming at the noise interference in oil and gas pipeline leakage detection, a signal denoising method based on improved variational mode decomposition (VMD) algorithm is proposed. First, the weighted mean of vectors algorithm (INFO) is used to select the parameters of the VMD algorithm adaptively to obtain the best decomposition level K value and penalty factor α value, which effectively solves the problem that the parameters are difficult to select in VMD. Then, according to the Hausdorff distance between the original signal and the probability density of each IMF, the effective modal components are selected for reconstruction and denoising signal is obtained. In oil and gas pipeline leakage signal processing, compared with whale optimization algorithm, grey wolf optimization algorithm, and particle swarm optimization algorithm, the reconstructed signal obtained by the proposed INFO algorithm optimized VMD parameters has higher signal-to-noise ratio and lower mean square error and mean absolute error. It shows that the proposed algorithm has better denoising effect.

Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection

In this article, a novel ensemble framework of improved siamese network (SN) is proposed to address the small sample issue that deep learning approaches encounter, as well as to enhance the precision of pipeline leakage detection (PLD) under small sample conditions. Firstly, training samples are input in pairs to the feature extraction network, and a combination of one-dimensional convolution neural network (1DCNN) and long short-term memory (LSTM) network is introduced to extract features of the time-series data, thus enhancing the effectiveness and robustness of feature extraction. Then, an improved relational metric network is designed to measure the similarity of features, to further strengthen the discriminative nature of the whole framework. In addition, the framework has been augmented with a classification network that can be used directly for PLD. The proposed SN-1DCNN-LSTM framework not only increases the number of training samples from the side, but also fully exploits the similarity information and data features between samples, overcoming the problem of instability and overfitting of deep learning models under small sample conditions. Finally, the experimental results verify the validity and superiority of the method under small sample conditions.

Multisource Multimodal Feature Fusion for Small Leak Detection in Gas Pipelines

Multisource Multimodal Feature Fusion for Small Leak Detection in Gas Pipelines

An exploration of novel natural gas leak detection based on the efficient CH4/N2 separation of polymer blend membrane

The CH4/N2 high-efficiency membrane separation technology is capable of effectively enriching CH4 from natural gas, and holds great potential for application in the fields of biogas purification and natural gas pipeline leak detection. However, preparing high-performance separation membranes is challenging due to the similarity between CH4 and N2 molecules. To enhance the CH4/N2 separation performance, block polyetheramide copolymer (Pebax)/EVA blended membranes were fabricated in this study. Additionally, a novel gas leakage detection tube was prepared. The EVA14 (14 wt% VA) membrane exhibits an optimal transport-facilitated channel among a series of pure EVA membranes, resulting in a CH4 permeability of 21.41 Barrer and CH4/N2 selectivity reaches 1.80 under mixed-gas feed conditions at 35 °C. The CH4 permeability of Pebax/EVA (30 wt%) reaches 120.5 Barrer by physical blending, and the CH4 permeability is increased by 191 % in comparison with pure EVA14 membrane. The Pebax/EVA (10 wt%) membrane exhibits the optimum CH4/N2 selectivity, and compared with pure EVA membrane increased by 35.5 %. EVA and Pebax polymers are blended for the first time for CH4/N2 separation, and the blending of block polyetheramide copolymer intensifies the membrane's separation performance comprehensively. The natural gas leak detection tube was fabricated with a membrane, offering a new and efficient CH4/N2 separation strategy.

Simulation and experimental study of buried natural gas pipeline leak detection based on sound source characteristics

Buried pipe-line leakage will affect the thermal characteristics of the soil environment, leading to a poor soil environment. In addition, leakage of natural gas possibly produces an explosion and subsequent fire, which has fatal harm. Sustainable detection of underground gas pipe-line leaks is a significant part of current research. In this study, a method for leak detection of buried natural gas polyethylene pipe-lines based on sound source characteristics is investigated. The simulation software was applied in analyzing the variation of leakage rate and sound source in buried pipes under different leakage conditions including mainly different leakage apertures and pipe pressures. Also, an experiment platform was built to verify the simulation results. These results can provide help for gas pipe-line leakage detection and safety protection.

Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction

The natural gas pipeline leakage detection task based on acoustic signal has some problems such as background noise coverage, lack of effective features, and low fault identification accuracy caused by small sample data. However, only one of these problems was usually studied in previous technologies. Almost no one has attempted to challenge multiple issues at the same time. In this study, a natural gas pipeline leak detection model that integrates acoustic feature processing techniques and feature reconstruction is proposed to resolve the above problems collaboratively. This model consists of two components. The first component is a feature processing technique of the acoustic signal that integrates frequency domain vector denoising and time domain associative function feature enhancement. The second component is a one-dimensional convolutional neural network with an expanded structural feature encoder (FAE) for feature reconstruction (FAE-1D-CNN). In the feature processing stage of the acoustic signal, firstly, the acoustic signal collected by the acoustic sensor is discretized into a digital signal. Secondly, the energy modal function is used to perform high/low energy modal clustering of digital signal features. The feature validity is enhanced by adding association factors to the low-energy modal features matrix. A low-pass filtering method is then used in the high-energy modal features to remove the background noise coverage of the high-frequency components. In the fault feature extraction stage, a feature encoder (FAE) is introduced in the 1D-CNN network to extract effective fault features while performing secondary reconstruction of local spatial features, addressing the problem of small sample leakage signals with few effective fault characteristics. The global average pooling layer is used instead of the fully connected layer, and the Softmax function is adopted as the classifier for fault discrimination. The performance of the proposed method was evaluated on the GPLA-12 dataset, and the fault identification accuracy is up to 95.17%. Compared with other competing methods, the method in this paper exhibits optimal performance and has broad application prospects.

Acoustic injection method based on weak echo signals for leak detection and localization in gas pipelines

Underground gas pipelines are facing health and safety issues due to the material corrosion and equipment aging. Conventional acoustic methods may fail to detect and localize leaks in low and medium pressure gas pipelines because the sound of gas escaping from a leak source is usually too weak to be detected. In this paper, an active acoustic injection method is presented to stimulate specific frequency sound waves into the pipeline for leak detection based on weak echoes from the leak location. Further analysis is conducted on the relationships between the reflection coefficient and the size of the leak hole, the pipe diameter, the wall thickness of the pipe and the sound frequency. A signal reconstruction method is subsequently proposed to effectively detect and localize the echo signals. Finally, some experiments are carried out in a specially constructed gas pipe. Field test results indicate that a leak localization accuracy of 0.1 m can be achieved by the proposed method, which may be beneficial to practical leakage detection in gas pipelines.

Data-driven digital twin method for leak detection in natural gas pipelines

Leak detection in natural gas pipelines is an extremely important and persistent problem in the oil and gas industry. The construction of accurate physical models of pipelines is limited by the high complexity, unavailable closed-form solution, and strict experienced personnel requirements. Moreover, industrial automation has the common problems of large amount of data with little information. This paper proposes a data-driven digital twin (DT) method for leak detection as a new paradigm solution to these challenges. From the perspective of knowledge-based data-driven, a DT pipeline learning and updating scheme based on normal data directly from operational data during the entity pipelines life-cycle is proposed to enhance DT adaptability. A DT-driven leak detection method is proposed, making effective use of data interaction and fusion of DT. The effectiveness and performance of the proposed approach is illustrated by deploying the DT pipeline in a simulated leak scenario of a real running natural gas pipeline. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd

Acoustic microfiber sensor for gas pipeline leakage detection

Gas pipeline leakage can have significant environmental and industrial consequences, making it crucial to safely and efficiently detect such leaks. This study introduces a novel microfiber sensor based on the Mach-Zehnder structure, designed specifically for detecting small gas pipeline leaks. Initially, simulations were conducted to analyze the electric field distribution of the microfiber and the changes in sound pressure caused by pipeline leakage. Subsequently, the sensor was developed by encasing the microfiber in a double layer of polydimethylsiloxane (PDMS) and utilizing a nut with a hole as an external fixation device. To ensure accurate measurement of low-frequency acoustic pressure changes, the sensor's installation structure was integrated into the pipeline branch pipe. Experimental results demonstrate sensor's capability to detect acoustic signals generated by small leakage apertures measuring 0.1 mm in diameter. The received voltage signal exhibited a strong linear relationship with the size of the leakage aperture within the range of 0.1-6 mm, with a correlation coefficient exceeding 0.97. In addition, the sensor has reliable electrical safety and anti-electromagnetic interference capability.

Control of gas pipeline leakage detection based on hybrid inline fiber interferometer

Hybrid inline fiber interferometers gained attention recently especially in the building control and detection fields due to their hybrid multi-path interferences, multi-parameter measurements, high sensitivity, and high precision. This work used etched multimode fiber (MMF) to introduce a novel medical gas pipeline leakage detector based on hybrid Fabry-Perot/Mach-Zehnder inline fiber interferometer (FP-MZI). The hybrid FP-MZI of this work consisted of two cascaded identical fiber Bragg gratings (FBGs) spliced to coreless fiber sandwiching the MMF. Four samples of different MMF lengths and sizes were used as sensing heads in the detection measurements. Self-imaging and phase-equations of the proposed interferometer were solved analytically to select the required length; then COMSOL software was used to ensure the occurrence of the required interference along the proposed interferometer at the designed wavelength. Two medical gas sources; air compressor and high-pressure gas cylinder, attached to two pipelines of size 15×0.7, and 12×0.6 mm was used to provide the required pressure. Gas leak was introduced to the pipes manually through controlled valves and measured using the proposed hybrid FP-MZI. Both constant and variable gas flowrates was investigated with variable gas pressures. The obtained sensitivity was ranged from 0.17-20.9 pm/bar for all of the investigated cases.

Leak Detection in Natural Gas Pipelines Based on Unsupervised Reconstruction of Healthy Flow Data

Summary Timely detection of leak accidents plays an essential role in the safe operation and risk assessment of natural gas pipelines. However, the scarce leak data and complex operating conditions lead to small samples, data imbalance, and problems with confusing operating conditions. The reliance on leak data limits the recognition performance of the artificial intelligence classification method for leakage operating conditions. A leak detection method based on the unsupervised reconstruction of healthy flow data is established to address these problems. First, an unsupervised neural network is established to reconstruct healthy flow data from real natural gas pipelines. And a model update strategy based on active learning is designed to improve the model’s adaptability for time-varying pipelines. Next, a dynamic alarm threshold strategy that accounts for the knowledge of the experience and statistical characteristics of the data segments is suggested to prevent false alarms caused by ambiguous operating conditions. Finally, unlike most recent work that only considers simulated data or laboratory data, this paper conducts a leak case study on an actual natural gas pipeline in service to improve the robustness of the proposed method in the actual operating environment. The findings of this paper can be used as a reference to analyze pipeline behavior analysis based on pipeline flow trend characteristics and early alarm management.

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.

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%.