Pipeline Leakage Research Articles

A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection

In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs.

Considering the changes in unsaturated soil saturation after pipeline leakage: A modified Terzaghi model

Pipeline leakage leads to the saturation and weakening of the surrounding soil, increasing the pressure exerted by the overlying soil on underground engineering structures and posing a serious threat. Therefore, it is necessary to thoroughly understand the influence of pipeline leakage on the overlying soil pressure. Based on the saturation distribution within different diffusion ranges, an analysis model for pressure in loose soil under the influence of pipeline leakage was presented. Considering the variation in the matric suction of the unsaturated soil, the soil pressure can be modeled based on the incomplete arch effect. The influence of the relevant parameters on the overlying soil pressure was analyzed, and the accuracy and rationality of the modified model were verified using a trapdoor test. These findings indicate that the modified model can accurately estimate the soil pressure caused by a pipeline leakage. The overlying soil pressure is directly proportional to the diffusion distance, tunnel width, and slip surface inclination angle and inversely proportional to the relative deformation. The rate of change was mainly influenced by the parameters affecting the soil arching effect. The modified model presented in this study can be used to directly calculate the overlying soil pressure affected by pipeline leakage. This calculation is based on the known physical and mechanical parameters of unsaturated soil, pipeline leakage range, and relative deformation of the tunnel, thus providing a theoretical basis for the design of structures under urban pipelines.

Identification of the number of leaks in water supply pipes based on wavelet scattering network and Bi-LSTM model with Bayesian optimization

Water supply pipeline leaks have significant impacts on urban areas and daily life. Acoustic techniques are widely applied in its leak detection, but there is a lack of research on quantifying multiple leaks in pipelines. This study presents a novel approach using a Wavelet Scattering Network (WSN) and Bi-directional Long and Short-Term Memory (Bi-LSTM) model with Bayesian optimization to identify leak quantities accurately. The WSN autonomously extracts signal features, which are subsequently processed by the Bi-LSTM for classification. Through Bayesian optimization, the model’s hyperparameters are refined to enhance performance. The results exhibit an impressive 90.8 % classification accuracy, showcasing the model’s effectiveness in accurately identifying leak quantities. In comparison with existing models, the proposed method stands out for its superior recognition accuracy, shorter training time, and enhanced noise immunity. Ablation experiments further confirm the distinct roles and contributions of the WSN, Bayesian optimization, and Bi-LSTM components in achieving these exceptional results.

Method for identifying the leakage of buried natural gas pipeline by soil vibration signals

Method for identifying the leakage of buried natural gas pipeline by soil vibration signals

Numerical Simulation Study on Reverse Source Tracing for Heating Pipeline Network Leaks Based on Adjoint Equations

In order to identify the leak source in complex heating pipeline networks, a timely and effective simulation of the leakage process was conducted. The open-source computational fluid dynamics software OpenFOAM 5.0 was combined with the PISO algorithm to simulate the pressure during the leakage in water supply networks, transforming the reverse source tracing problem into the solution of an adjoint equation. The validation of the transient adjoint equation for single-phase flow was completed through simulation, and the pressure wave change graph at the moment of the network leakage was solved, which was consistent with the experimental results. Using the open-source finite element analysis software OpenFOAM 5.0, the positioning accuracy of pipeline leak points can be controlled within the range from 92% to 96%. Based on the pressure wave change graph, the position of the leak source in the complex network was determined using the reverse source tracing method combined with the second correlation theory. The results show that the calculation speed of the PISO algorithm combined with the adjoint equation is significantly better than that of the individual SIMPLE and PISO algorithms, thereby proving the superiority of the adjoint method.

Technology Focus: Production and Facilities (December 2024)

This year has been both inspiring and exhilarating in the production and facilities domain, highlighted by a record 269 abstracts. This plethora of ideas fosters industry knowledge and promotes the exchange of innovative solutions driven by engineering advancements and data analytics. It covers the deployment of digital technologies, the journey toward decarbonization and its environmental impact, and the enhancement of energy efficiency and maintenance of asset integrity. The process of reviewing a significant number of technical abstracts based on their relevance, originality, clarity, and potential impact on the oil and gas industry is essential to ensure that JPT’s readers see papers with high-quality contributions to the production and facilities community. Comprehensive digital solutions cover the entire spectrum of production and facilities management, integrating a wide range of technologies such as advanced data analytics, Internet of Things devices, artificial intelligence, machine learning, cloud computing, automation, robotics, and digital twins. To exemplify this, paper OTC 32872 outlines learnings by evaluating the routine of a floating production, storage, and offloading vehicle crew to identify scenarios for the application of ground robots in day-to-day offshore activities, leading to a boost in productivity while reducing human exposure to risk. In the field of minimizing carbon footprint and promoting environmental sustainability, several academic papers have outlined strategies for reducing emissions led by a creative ecosystem of technologies and solutions, encompassing the deployment of methane emissions quantification at production facilities, reducing gas flaring, enhancing energy efficiency, repurposing of existing gas pipeline networks for the transport of hydrogen and hydrogen/methane blend, and using hydrogen fuel in a dual-fuel engine on a drilling rig. A notable illustration is URTeC paper 3866061, which presents continuous-monitoring point sensors for methane emissions, tracking changes in parts per million of methane in the air. Collecting raw data and applying a proprietary data-science algorithm, using a plume-dispersion model with the continuously acquired data, the evidence is reported to the operator, identifying the piece of equipment from which emissions originate and quantifying the volume and rate of the emission. In the asset-integrity-management landscape, the integration of digital technologies is playing a key role, whether through machine learning and artificial intelligence paired with fiber-optic distributed acoustic sensing technologies used to enhance pipeline integrity or rapid and reliable management of pipeline leak detection with a novel method on a multiphase-flow leak-detection model using only input and output parameters. From the standpoint of composite and nonmetallic materials, ongoing product research, development, and qualification efforts are being pursued to meet rising demand to stretch the operational envelope and enhance reliability. With the rapid advancement of carbon capture use and storage wells, paper SPE 217664 provides a guideline for such selections based on the composition of the CO2 stream, including impurities and the composition of the saline formation into which the CO2 will be injected. Recommended additional reading at OnePetro: www.onepetro.org. SPE 216229 Resource Optimization at Remote Facilities by Leveraging Advance Maintenance Practices Total Productive Maintenance, Operator-Driven Reliability, and Industry 4.0 Technologies by Muhammad Shahzad Ahmed, ADNOC, et al. OTC 34661 Unleashing the Sky Potential: Revolutionizing Construction With Drones by Chen Khian Fong, Shell, et al. SPE 217716 World’s First Demonstration of Hydrogen Blending in Dual-Fuel Engines on a Drilling Rig by W. Kernan, Caterpillar, et al.

Experimental study on hydrogen pipeline leakage: Negative pressure wave characteristics and inline detection method

This paper presents an experimental study on a potential safety system for hydrogen fuel cells. It focuses on early leakage detection and localization. This would enable effective system response to avoid the effects of catastrophic loss of containment. Experimental results are presented from a pipeline setup wherein the leak is identified utilizing inline pressure sensors and flowmeters. The investigations involve recorded data at two leak locations at different initial pressures, mass flow rates, and leak diameters. The leak detection technique is based on analysis of the negative pressure wave (NPW) intensity and propagation duration, denoted as peak-to-peak amplitude (Δh) and oscillation period (Δτ). They are extracted from filtered pressure records' first derivative (dp/dt). Δh and Δτ increase as the initial mass flow rate and leak diameter decrease. Therefore, this approach is advantageous for detecting leaks from tiny cross-sections. Δh and Δτ measured at two leak positions at different lengths from the system inlet increase at a higher initial flow rate of 1.0 g/s, but they exhibit opposite behavior at a lower rate (0.42 g/s). Increasing the sampling rate in the dp/dt-time graphs enhances the precision of leak localization. Calculation results show an effective leakage localization with an accuracy of 15 mm (2.5%). The leak flow rate during nitrogen leakage tests is about three times that of hydrogen, and it has higher Δh (about two times at 10 bar).

Fiber optic sensing technology in underground pipeline health monitoring: a comprehensive review

Pipelines play a critical role in transporting water, oil, and gas and are indispensable for urban development. However, monitoring underground pipelines is challenging due to the complex environment in which they are situated. Traditional sensors have limitations in all-round and real-time monitoring, while fiber optic sensors offer several advantages, including large coverage, high sensitivity, long sensing distance, and corrosion resistance. As such, fiber optic sensing technology (FOST) has emerged as a promising tool for underground pipeline monitoring. This review article provides a comprehensive overview of FOST, including its basic principles, classification, and research progress in underground pipeline monitoring. Specifically, this article focuses on the technology’s application in monitoring pipeline leakage, deformation, corrosion, and geological natural disasters. In addition, the article highlights the need for future research and development in FOST. For example, combining FOST with artificial intelligence can enhance the accuracy and efficiency of pipeline monitoring. Overall, this review article offers valuable insights into the potential of FOST for underground pipeline monitoring and provides essential guidance for future research in this field.

SDCSN: a hierarchical parallel localization method for pipeline leakage based on vibration signals

Abstract In applying deep learning methods to detecting and localising pipeline leaks, improving the fitness of deep learning methods to leak signals is an important task. We propose a novel detection model called Stacked dilated convolutional shrinkage network (SDCSN). This model incorporates a Stacked Dilated Convolution (SDC) module specifically designed for vibration signals, enabling the extraction of rich multi-scale local features. Moreover, implementing the Residual Shrinkage Building Unit (RSBU) module for noise reduction in the network architecture. Building upon this foundation, we introduce a new concept centred around hierarchical leakage discrimination and parallel prediction positioning. This approach enables accurate assessment of leakage levels and precise identification of multiple leakage points. Finally, the performance of the proposed method is verified in real experiments and the optimal settings for the dilated rate are determined. The results demonstrate a maximum classification accuracy rate reaching 98.94%.

Optimizing acoustic signal processing for localization of precise pipeline leakage using acoustic signal decomposition and wavelet analysis

Pipelines play a crucial role in today's infrastructure. However, they are easily affected by environmental conditions. Therefore, regular inspections are essential to ensure the integrity of pipelines. This study introduces a new approach that combines variation mode decomposition (VMD) and empirical wavelet transform (EWT) with continuous wavelet transform (CWT) for acoustic data to improve the signal-to-noise ratio (SNR) and accurately detect pipeline leakage location in the time domain.The acoustic data was gathered in three distinct scenarios inside an open environment, where external noise considerably influenced the original signal.VMD and EWT are employed to enhance the signal-to-noise ratio (SNR). VMD efficiently distinguishes high-frequency noise and assures distinct mode separation, whilst EWT delivers precise temporal localization for non-stationary signals. Based on the results of VMD and EWT, certain IMFs were selected for further study and SNR is computed among the respective chosen IMFs and original signals to measure the improvement. Subsequently, the CWT was utilized on the chosen IMF for feature extraction, effectively finding leakage locations in the time domain.This study illustrates the feasibility of successfully mitigating noise and extracting signals in open and noisy environments, thus enhancing the accuracy of leak detection. The results establish a robust basis for subsequent investigations in pipeline leak detection and demonstrate the applicability of advanced signal processing approach to real-time leak detection systems.

Acoustic Monitoring Efficiently Identifies Pipeline Leaks

A Pennsylvania water utility was facing nonrevenue water loss of 20%. By implementing a remote acoustic monitoring system, the utility significantly decreased water loss, reduced costs and employee overtime, and improved customer service.

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.

Pipeline leak detection based on generative adversarial networks under small samples

During the actual industrial process, it is challenging to obtain samples of oil and gas pipeline leaks resulting in the scarcity of training data samples suitable for fault diagnosis. In order to tackle this issue, this paper suggests a method a Recursive Generalization self-attention generative adversarial network (RAGAN) aided by Wasserstein distance with gradient penalty. The initial step involves applying the short time Fourier transform to the acoustic signal of oil and gas pipeline leakage, treating it as real data. Subsequently, both the real data and random noise following a Gaussian distribution are fed into the generator. The output is utilised as a pseudo sample. The Wasserstein distance of the distribution of real data and fake samples is introduced as a loss term in the discriminator, and a gradient penalty is added. Finally, the network optimizes the parameters through back propagation until Nash equilibrium. PSNR and SSIM are used as sample reliability evaluation. The results show that the fake samples have high similarity with the real samples, which can be used to expand small sample data. Moreover, extending pseudo samples to small sample data sets can effectively improve the performance of fault diagnosis.

Oil pipeline multiple leakage detection and localization based on sensor fusion

Oil pipeline simultaneous leak localization solely using pressure and flow rate measurements at its boundaries is inherently challenging. Localizing simultaneous leaks in a long-distance oil pipeline solely using pressure and flow rate measurements at its boundaries presents significant challenges. This difficulty is attributed to the presence of noisy measurements and the interdependence of two equations that incorporate variables associated with leaks, leading to inherent uncertainty. Additionally, existing leak detection methodologies predominantly utilize a single sensor, which contributes to a high incidence of false alarms, particularly in complex operational environments. Developing nonlinear equations based on momentum and continuity principles is proposed as a comprehensive solution for estimating multiple leak locations in oil pipelines. In this study, multiple leaks in long-distance oil pipelines are estimated using a two-stage decision-making scheme. Arrays of sensors are proposed for improving the reliability and accuracy of simultaneous leak estimations, replacing the traditional reliance on single sensors along pipelines. The Fisher fusion technique combines inaccurate measurements to improve estimation accuracy. This method integrates sensor measurements even when noise is present, demonstrating its effectiveness. Comprehensive simulationsare conductedusing OLGA multiphase flow simulation software to evaluate the robustness and practicality of the proposed approach under various real-time conditions found in long-distance oil pipelines. The system’s state convergence and precision in simultaneous leak estimations highlight the effectiveness of the proposed strategy in oil pipeline control. Across various scenarios, the leak detector showed a 28 % improvement for a 50 Km oil pipeline.

Improved object detection network for pipeline leakage localization in the pneumatic system based on thermal images

The infrared thermal imaging method can effectively identify and locate leakage sites using the temperature characteristics of pneumatic system leakage. However, it is limited by the tiny dimensions of the objects, color shifts of varying background temperatures, and indistinctness of feature details. To address these issues, we integrate Omni-Dimensional Dynamic Convolution (ODDC), Squeeze-and-Excitation (SE) attention module, and Normalized Gaussian Wasserstein Distance (NWD) with You Only Look Once (YOLO) into a framework, named ODSW-YOLO, for the precise localization of leakage. Specifically, ODDC is employed to adjust the kernel parameters for improving the accuracy of detecting small targets. To reduce the influence of varying background temperatures, SE attention is adopted to extract key features. NWD is used to enhance the feature details by checking the small feature changes within the detection window. Finally, extensive experiments show that ODSW-YOLO improves the detection accuracy from 0.677 to 0.755, which surpasses the baseline model (YOLOv5).

Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety.

Diffusion Characteristics and Influencing Factors for Large-Hole Natural Gas Pipeline Leakage in Utility Tunnels

Diffusion Characteristics and Influencing Factors for Large-Hole Natural Gas Pipeline Leakage in Utility Tunnels

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.

QUALITATIVE ANALYSIS OF TEMPERATURE ERROR OF NEGATIVE PRESSURE WAVE METHOD FOR DETERMINING THE LOCATION OF A PIPELINE LEAK

The article is devoted to the qualitative analysis of the temperature error of the negative pressure wave method for determining the location of a pipeline leak. Recording the arrival time of the negative wave at both ends of the pipeline allows you to determine the location of a pipeline leak. In contrast to known works on this topic, this article proposes a method for qualitative analysis of the error in determining a leak location. Based on the variational optimization method, an optimal ratio of the mode indicators at which the minimum error in determining the location of a leak can be achieved is defined. A method for qualitative analysis of the error in determining the leak location using the negative pressure difference wave method is proposed. The optimal correlation of the mode functions is determined at which the minimum error in determining the leak location is achieved. An expression for estimating the relative error in determining the leak location using the negative pressure difference wave method is obtained.

Automatic detection of water pipeline leakage via unsupervised learning applied on ground penetrating radar data

ABSTRACT Aging urban water supply pipelines are facing significant leakage issues. Ground penetrating radar (GPR) is an effective non-destructive method for leakage localization, but its image analysis mainly relies on interpreter experience, leading to uncertainty and inefficiency. To address this issue, an unsupervised learning method based on GPR data attributes is proposed for automatic leakage detection. First, velocity and energy attributes are extracted from B-Scan profiles to differentiate wet areas caused by leakage from normal areas. Then, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is applied to automatically classify these two types of areas. The proposed method was evaluated on an experimental platform with constant water pressure and pre-drilled leakage points. Experiments show that the velocity and energy attributes decrease by 25.2 and 26.6% after leakage. Using velocity attributes yields better results, and suggestions for DBSCAN hyperparameters are provided. Feature importance analysis indicates that two-way-travel-time with a score of 46.1 and energy attribution index with a score of 32.7 significantly influence leakage identification. This method can also estimate affected leakage areas with an error not exceeding the spacing of three measurement lines, and is available across various underground conditions without needing well-labeled training data.