Pipeline Damage Research Articles

Quantitative Study of Sensor-Pipe Distance in Noncontact Magnetic Detection of Ferromagnetic Pipelines

Non-contact magnetic detection technology has great application potential in buried oil and gas pipelines due to the non-contact online detection of pipeline damage. However, the non-contact magnetic detecting signal is affected by the distance between the magnetometer and the ferromagnetic pipeline, leading to missed detection of pipe defects. It is essential to calibrate the detection height for the non-contact magnetic detecting signal. In this paper, the spatial analytical model of non-contact magnetic signals above ferromagnetic pipelines is established. Then, a quantitative variation law of non-contact magnetic detecting signals is obtained under different pipe diameters and internal pressures. Next, a signal calculation model based on the detection height h is established. A detection height correction method is proposed to eliminate the influence of detection heights on the magnetic detecting signal. An experimental investigation is carried out to validate the accuracy of the numerical calculation results and correction method. The results indicate that the magnetic detecting signal has a second-order exponential relationship with the detection height h within 0.1 m - 2 m. The average error of the signal correction model is 6.37%. The modified magnetic detecting signal via the signal correction method can reflect the damage status of ferromagnetic pipelines.

Predicting Natural Gas Pipeline Failures Caused by Natural Forces: An Artificial Intelligence Classification Approach

Pipeline networks are a crucial component of energy infrastructure, and natural force damage is an inevitable and unpredictable cause of pipeline failures. Such incidents can result in catastrophic losses, including harm to operators, communities, and the environment. Understanding the causes and impact of these failures is critical to preventing future incidents. This study investigates artificial intelligence (AI) algorithms to predict natural gas pipeline failures caused by natural forces, using climate change data that are incorporated into pipeline incident data. The AI algorithms were applied to the publicly available Pipeline and Hazardous Material Safety Administration (PHMSA) dataset from 2010 to 2022 for predicting future patterns. After data pre-processing and feature selection, the proposed model achieved a high prediction accuracy of 92.3% for natural gas pipeline damage caused by natural forces. The AI models can help identify high-risk pipelines and prioritize inspection and maintenance activities, leading to cost savings and improved safety. The predictive capabilities of the models can be leveraged by transportation agencies responsible for pipeline management to prevent pipeline damage, reduce environmental damage, and effectively allocate resources. This study highlights the potential of machine learning techniques in predicting pipeline damage caused by natural forces and underscores the need for further research to enhance our understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance.

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. This approach was verified by experiments and simulations. First, a BP neural network for structural damage location was constructed, and a method to optimize the BP neural network parameters based on the GA was established. Then, a finite element model was established based on the measured data from modal tests of a physical pipeline model, and a large number of vibration neural network training samples were obtained using the finite element model. Finally, to show that the improved BP neural network based on the GA had a better damage location accuracy, the location results of the original BP and GA BP methods were compared for cases with no noise and with 5% noise. The results show that the average error of the BP neural network based on the GA was less than 3%, which was 11.6% lower than that of the original BP neural network.

Research on vibration fatigue damage identification of oil and gas pipeline under the condition of measured noise injection

The acoustic emission signals collected by oil and gas pipelines on offshore platforms are disturbed by noise, which makes it difficult to locate the pipeline damage. To solve this problem, in this study, the measured noise signal of the offshore platform pipeline was first obtained through vibration noise acoustic emission tests. The measured field noise of the offshore platform was injected into a lead break signal collected in the laboratory, and then the injected noise signal was identified by the damage identification method based on empirical mode decomposition (EMD) combined with a probabilistic neural network (PNN). Finally, the method of time difference localization was used for preliminary localization, and then a backpropagation (BP) neural network was used to correct the localization results. The results showed that the recognition method based on EMD combined with the PNN network achieved a 97% recognition accuracy in cases with large amounts of measured noise interference. The fatigue crack localization method based on the BP neural network time difference correction method was used to locate the acoustic emission fatigue crack in the test data, and the average localization error after correction was less than 2%.

Screening of critical dangerous parts of Pi-shaped pipe in the corrosive environment

In order to accurately predict the key dangerous parts of the polygonal pressure pipeline in a corrosive environment, based on the stress fatigue theory, a stainless steel pipeline damage calculation model coupled with the coupling of fatigue and corrosion is established, and the cold and operating conditions are carried out for the polygonal pressure pipeline. The analysis of stress and damage fields shows that the fatigue life of the square-shaped pipeline is reduced from 9225 to 42 years after the influence of the stress concentration factor of pitting corrosion. Based on the results of the damage analysis, by defining the dangerous concentration parameters, it is predicted that the key dangerous parts of the zigzag pressure pipeline are mainly the elbow and the long side abdomen. The greater the dangerous concentration parameter is, the more concentrated the dangerous parts are. When the dangerous concentration parameter is greater than 0.5, the elbow of the pipe shoulder and the long side joint are not critical dangerous parts anymore.

Study on damaged micro-mechanism of pipe wall during braking and anchoring process of pipeline intelligent plugging robot

Pipeline intelligent plugging robot (PIPR) realizes braking and anchoring through hard contact of slip and pipeline. This process will cause bite marks on the inner wall of the pipeline, which will aggravate corrosion and shorten the service life of the pipeline. Therefore, it is necessary to comprehensively consider slip anchoring performance and degree of pipeline damage, and optimize slip structure parameters of PIPR, to reduce the damage caused by hard contact between slip and pipeline. Based on force analysis and the hard contact mechanism of PIPR in the course of the anchoring process, the damage factors of pipe walls are analyzed, and a finite element analysis model of slip and pipeline is established. The plastic strain of the inner surface of the pipeline, the plastic strain of slip, and the von Mises stress under three parameters of slip tooth top angle, tooth inclination angle, and tooth number are comprehensively evaluated. In addition, visible indoor tests are established to verify the simulation model. The results show that the optimal tooth structure of slip is tooth top angle [Formula: see text], tooth inclination angle [Formula: see text], and tooth number [Formula: see text]. This study will provide a reference for the optimal design of the slip of PIPR. It has important engineering significance for the successful implementation of its anchoring operations.

Quantitative Estimation of Pipeline Slope Disaster Risk in China

China’s economic development is closely related to oil and gas resources, and the country is investing heavily in pipeline construction. Slope geological hazards seriously affect the long-term safe operation of buried pipelines, usually causing pipeline leakage, property and environmental losses, and adverse social impacts. To ensure the safety of pipelines and reduce the probability of pipeline disasters, it is necessary to predict and quantitatively evaluate slope hazards. While there has been much research focus in recent years on the evaluation of pipeline slope disasters and the stress calculation of pipelines under hazards, existing methods only provide information on the occurrence probability of slope events, not whether a slope disaster will lead to pipeline damage. Taking the 2015 Xinzhan landslide in Guizhou Province, China, as an example, this study used discrete elements to simulate landslide events and determine the risk level and scope for pipeline damage, and then established a pipe-soil coupling model to quantitatively evaluate the impact of landslide hazards for pipelines in medium- and high-risk areas. The results provide a reference for future pipeline disaster prevention and control.

Interaction Behaviors of Longitudinal and Transverse Seismic Waves with Underground Geoengineering Objects

Abstract Main pipelines as long linear objects are vulnerable to dangerous natural and man-made influences. One of the technogenic sources is large-scale explosions, which cause a sharp fluctuations of soil and cause serious damage to underground pipelines. When calculating the strength of pipeline systems, it is assumed that the damage occurs mainly due to additional axial stretching. However, the destruction and damage of pipelines can occur during seismic impacts directed perpendicular to the longitudinal axis of the pipeline. To assess the impact of longitudinal and transverse waves on the underground pipeline during seismic action a software-calculation module is developed. It implements a model of dynamic strength analysis, which allows estimating the magnitude of longitudinal and trans-verse seismic loads on the underground pipeline to establish safe parameters of seismic loads and geometric dimensions of the protected object. The final system of equations of motion of N nodes of a discrete system for a single length of the pipeline is presented as a system of 4N equations of the first order. The efforts on the internal nodes are determined by the strain stresses at the displacement of the nodes of the adjacent rods. The efforts on the nodes of the pipe contour are the diffraction interaction of seismic waves in the soil with the pipe. The system of equations is supplemented by the corresponding initial and boundary conditions. The dependences of the inflow of longitudinal and transverse pressure of explosive seismic pressure on the pipeline are established. The dependences of stresses in the pipeline of the diameter, thickness and type of soil are researched.

Fragility Study of Buried Pipeline Networks Based on Copula Function

The seismic fragility of water supply networks is an important research component for lifeline engineering in earthquake-prone regions. An accurate computational model was established to rapidly and efficiently assess pipeline damage under seismic excitation. In this study, the fragility of the pipeline network is deduced based on Copula function technology and the series-parallel model. This solves the low accuracy problem caused by simplifying elbow, tee, and other nodes and ignores the correlation between connected components in the fragility calculation of the buried pipeline network. Then, the shaking table tests of the elbow pipe, tee pipe, and scale pipeline network are performed, and the prototype pipeline network’s finite element comparative calculation model is also established. Then, the optimal Copula function for the pipeline network, the Gumbel Copula function, is obtained by calculating the correlation between each component’s seismic demand. It was found that the seismic demand of pipe and elbow components in the sand is higher than that in the clay, while the opposite is valid for the tee component. After that, a two-parameter log-normal distribution is employed to obtain the seismic fragility of three types of components in different soil properties. It was also found that the maximum failure probability can be obtained from the elbow leakage. The fragility of the elbow pipe is greater than that of the tee pipe. The fragility of components is then applied to the fragility calculation of the scale pipeline network by a series-parallel model, and the obtained results are compared with numerical simulations. It is demonstrated that the series-parallel combination calculation-based results have a high safety reserve for the pipeline design.

Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines

Failure and leakage of natural gas pipelines can lead to serious ecological losses and casualties. Third-party damage has become an important cause of pipeline failure and leakage, which urgently needs an accurate risk assessment method to assess the risk. Conventional qualitative risk analysis methods can only point out the critical events of failure accidents but fails to predict the failure probability. This paper proposes a dynamic risk probability analysis method based on Dynamic Bayesian network (DBN), which is validated by a third-party damage case under uncertainty. First, human factors are taken as the main analysis object in the risk analysis, by which two subcategories of intentional and unintentional factors are classified. A complete risk factor analysis is performed by combining expert recommendations with the fault tree analysis method and developing a coupled model with the event sequence diagram. Second, in order to deal with the uncertainty of risk factors, the coupled model is mapped to a DBN model. The prior probabilities of the input DBN model are obtained by database, fuzzy set theory, and Dempster-Shafer evidence theory. Weibull distribution is applied to construct the probability transfer process between time segments, which better fits the characteristics of third-party disruptive factors in onshore pipelines. Finally, the practicality and advantages of the proposed method are demonstrated by a real case study, which identifies 6 critical events and predicts the probabilistic information in different time slices. Furthermore, the method predicts the probability of failure events and potential consequences by processing the time series information, and it is found that the probability of structural damage and explosion is higher than other consequences. In this way, some risk management countermeasures are proposed in a targeted manner. The results show that compared with the conventional BN model which only performs probabilistic inference once, the DBN model can perform temporal dynamic inference to achieve the prediction of failure probability, and it can effectively achieve the numerical prediction of risk failure probability.

Study of deformation characteristics and a strain calculation model for pipelines impacted by landslides

It is not uncommon to see pipelines damaged by landslides. If the maximum strain that landslides exert on pipelines can be calculated from available survey data, then pipeline damage can be more accurately predicted and the safety of the pipeline can be improved. Furthermore, this information can assist targeted prevention and control measures, which should greatly reduce the risk that landslides pose to pipelines. However, although researchers have conducted considerable research on landslide-induced pipeline deformation, there is no model that can calculate the change in pipeline strain under the displacement of landslide material. Based on geological data collected from a buried crude oil pipeline damaged by a landslide near a city in southeast China, this work uses numerical simulations to obtain the deformation parameters of pipelines affected by landslides at different orientations and for different landslide and pipeline parameters. This work also summarizes the deformation characteristics of pipelines affected by landslides under these different variables. A model is then constructed and verified that simulates the change in pipeline strain with soil displacement while considering the characteristics of the landslide mass and the pipeline itself. The results show that the model can sufficiently reflect the influence of various factors that act on pipeline deformation and can accurately calculate the maximum strain that landslides exert on pipelines.

Study on risk prevention and control of third-party construction damage of underground pressure pipelines

The utilization scale of underground pressure pipelines such as gas, heat and water supply pipelines is increasing, and pipeline accidents occur frequently, among which the third-party construction damage has become one of the main factors, so it is crucial to prevent and control the risk of third-party construction damage. This paper probes into the risk prevention and control measures for the third-party construction damage of underground pressure pipelines from four aspects of management, laws and regulations, science and technology and culture. The research results show that only by taking into account the standardized management, restriction of laws and regulations, scientific and technological support, and cultural publicity, can the third-party construction damage risks of underground pressure pipelines be effectively prevented and controlled.

Causes and treatment of adhesive damage in polymer flooding injection pipeline

The viscosity of polymer liquid is an important parameter used in oilfield. Understanding the loss rule and regularity of polymer liquid will help to improve the distribution and water injection quality of polymer liquid and ensure the effective distribution of polymer liquid. According to the oil-water viscosity data of the previous polymer flooding fluid, it is found that the oil-water viscosity is within the range of 5%-20%, with an average of 9.8%. The reasons of single well pipeline viscosity are complex and varied, which are closely related to reducing substances, types and quantities of bacteria, fouling oil suspensions on the inner wall of pipeline, pipeline length and running time. Therefore, from the aspects of Fe2+, pipeline running time and so on, the influence factors and prevention methods of single well pipeline viscosity loss were studied, and the corresponding treatment measures were worked out, and good results were obtained.

Experimental research of electrical characteristics combined solar-electric air heater

Poor water quality in district heating systems leads to failure of boilers and pipelines in heating networks. The main reason for this is corrosion and scale deposits. The presence of these factors shows the low efficiency of water treatment systems. The article presents the results of the introduction of non-traditional water treatment technologies for district heating systems based on the experience of the Zhytomyrteplokommunenergo utility company of the Zhytomyr City Council in 2007-2021. In the boiler houses of the enterprise, water treatment systems were modernized, and the production chemical service was reorganized. In particular, chemical deaeration was launched in combination with traditional sodium cationization, as well as chemical deaeration in combination with complexonate water treatment against the classical water treatment technology - ion exchange. Installed magnetic sludge separators with magnetic inserts. The technology of water softening with anti-corrosion protection using Redox filters has been introduced. Gravimetric corrosion sensors of our own design have been developed and installed. The database of processing and statistical analysis Access "Corrosion damage of pipelines of heat networks" was created and put into operation. The experience of modernizing the water treatment system has shown that work in this direction increases the reliability of operation of district heating systems by reducing the consumption of energy carriers and material resources. In addition, the problem of improving the operational reliability of the heat exchange equipment of thermal stations needs to be solved. The projected start of the use of heat recovery devices on water heating boilers in operation, in condensation or partially condensation modes, will also bring with it a number of problems with the neutralization of condensate and its return to the heat supply system. The end result is an increase in production reliability, environmental friendliness and a reduction in the cost of heat for district heating systems.

Classification of damage types in liquid-filled buried pipes based on deep learning

In long-distance pipelines, this type of local damage can lead to different forms of damage. Ultrasound (UT)-guided wave technology can detect channel damage at a distance and reduce the workforce and material resources. Deep learning has the advantages of high efficiency and accuracy for pipeline damage classification and identification. This study proposes a classification method that combines UT-guided waves with deep residual neural networks. First, the time-series data of the defect echoes are encoded into different types of images using the glare angular field matrix. Then, the features of the generated images are extracted using ResNet. Finally, it is put into Faster-RCNN for training, validation and defect type recognition. Finite element models containing cracked, square and circular defects were built to verify the effectiveness of the method. The network models were trained for classification, testing and validation using pipes with broken defects. Finite element analysis results show that the network model classifies cracked, square and circular defects with different damage levels with accuracy, recall and F1-score indices close to 90%, and the experimental results show that the network model has an identification accuracy of approximately 90%. Furthermore, the results show that the Faster-RCNN-ResNet model is more accurate in identifying complex pipeline defect types than machine learning and other deep learning methods. The model shows good feasibility and effectiveness in classifying the damage types of long-transmission buried pipelines.

Influence of different opening conditions on explosion consequence in a LPG transmission pipeline

ABSTRACT Explosion accident in gas pipeline brings disastrous consequences because of its complex pipeline network with branches. A novel risk assessment methodology is established to quantitatively analyze the influence of different opening conditions on explosion consequence, including the calculation of dynamic changing explosion pressure as independent variable and the risk calculation of probability unit of personal injury and pipeline damage risk as dependent variable. Taking LPG pipeline as research object, the influence analysis results show that the determinacy of probability unit of pipeline damage decreases significantly once the closed pipeline branches open. The maximum decrease of pipeline damage is 77.39% in horizontal branch, due to its easy accumulation and quick release of explosion pressure. While the determinacy of probability unit of personal injury in the outlet area of vertical branch decreases faster after adding water spray. The maximum decrease of eardrum rupture is 6.66% and the maximum decrease of pulmonary hemorrhage is 4.08%. The risk assessment methodology and application results of different opening conditions on personal injury and pipeline damage could assist the optimal layout of industrial gas pipeline.

Investigating brittle damage of buried pipelines under dip-slip faulting with peridynamics

Investigating brittle damage of buried pipelines under dip-slip faulting with peridynamics

Study on Interaction Mechanism of Natural Gas Pipe-Landslide System Reinforced by Micropile Groups Based on Model Test

Natural gas pipeline projects in mountainous areas are inevitably affected by geological disasters such as landslides, which pose a serious threat to the safe operation of pipelines along the routes crossing landslide areas. In this paper, based on a pipe-landslide project in a mountainous area in southwest China, the interaction mechanism and failure evolution process of the landslide-pipeline system reinforced by two kinds of micropiles are studied through indoor large-scale physical model tests, and some suggestions on the support work of the pipe-landslide project are put forward according to the test results. It was found that the deformation process of the engineering system composed of landslide, micropile, and pipeline presents a high degree of synergy under the external force and mainly experiences four stages: initial deformation period, uniform deformation period, accelerated deformation period, and residual deformation period. The bending deformation of the perforated pipe micropile is large at the 1/4 position of the pile top from the pile bottom, and the deformation of the screw micropile near the sliding surface is serious. The pipeline welding port is the weak position of the pipeline; after the failure of the pile, the pipeline interface is first cracked, along the interface position along the two ends of the tear, and finally completely broken. The screw micropile cannot effectively resist the landslide thrust at a large load level, so the risk of pipeline damage is greater. The yield strength and ultimate strength of the perforated pipe micropile are greater than those of the screw micropile, and the perforated pipe micropile can still exert a certain residual resistance after reaching the ultimate bearing capacity, which has a beneficial effect on the reinforcement of the pipeline crossing the landslide system. The research results provide important reference value for landslide-pipeline treatment engineering.

Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm

Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm

Numerical study of scour below vibrating pipelines under waves and currents

Local scour of the seabed near a submarine pipeline is a major factor in pipeline damage and failure. Previous studies on local scour of submarine pipelines mainly focused on fluid flow and sediment transport, ignoring the influence of vibration on the local scour process. In this study, a two-dimensional scour model based on the time-averaged N–S equation was established to analyze the local scour and vortex-induced vibration of the pipeline. The clear-water and live-bed scours beneath submerged horizontal pipelines are presented under current-wave coupling conditions. The study shows that when the submarine pipeline is partially suspended, the vibration of the submarine pipeline is gradually intensified under the action of waves and currents and then affects the scour process near the pipeline, especially for KC numbers larger than 0.8. In addition, thresholds of embedment depth e/D = 0.4 and relative water depth h/D = 6.7 were found, that is, the influence of bed and free surface on the scouring process are greatly reduced once these thresholds are met.