Gas Pipeline Failure Research Articles

The Fault Diagnosis Method for Photovoltaic Modules Based on Machine Learning

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

Failure of a high-pressure flash gas pipeline from coal coke gasification to the cleaning section due to hydrogen-induced cracking

ABSTRACT The high-pressure flash pipeline of coal coke gasification leaked within one week after the operation. Further macroscopic observation showed that cracks appeared in the base material area near the weld. The medium transported by the pipe was low-concentration acid gas. Visual inspection, tensile testing, impact testing, chemical composition analysis, metallographic analysis, energy dispersive spectroscopy, X-ray diffraction analysis, and electron backscattering diffraction analysis were used to reveal the sources of cracks in the pipeline. It was found that the cracks were intergranular and filled with corrosion products. The chemical composition of the corrosion products is rich in Cr, S, and O. After the crack was opened, fracture features of ‘rock candy’ were observed, exhibiting brittle fracture characteristics. The cracks were caused by hydrogen-induced cracking along the grain boundary. On this basis, suggestions and countermeasures were put forward for the failure of the high-pressure flash gas pipeline.

Risk Evaluation and Software Development of Urban Natural Gas Pipelines Based on Bayesian Networks

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

Research Progress of Failure Mode Diagnosis Methods for Oil and Gas Field Pipelines

In recent years, the service safety evaluation of oil and gas pipelines in the whole life cycle has attracted the attention of many researchers. This paper summarizes the failure modes of oil and gas pipelines and focuses on four machine learning-based oil and gas pipeline failure mode diagnosis methods: fault tree analysis method, failure expert system evaluation method, the fuzzy comprehensive evaluation method of pipeline failure, failure mode, and impact analysis method, and prospects the development of oil and gas pipeline failure mode diagnosis technology, in order to provide a reference for subsequent research.

Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline

Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline

Studying Corrosion Failure Prediction Models and Methods for Submarine Oil and Gas Transport Pipelines

To predict the corrosion failure of carbon steel oil and gas pipelines more accurately, a new corrosion failure prediction model for submarine oil and gas transport pipelines was constructed. A corrosion failure prediction management system was also developed based on the constructed model. To construct the model, corrosion experiments were carried out to analyze the influences of temperature, partial pressure of CO2, pH value, and flow rate acting on the corrosion rate. Based on the analysis results and the corrosion experiment data, a new corrosion failure prediction model containing the time and flow rate for oil and gas pipelines was constructed. The model is based on the existing corrosion prediction model and has a determination coefficient, R2, of 0.9573, which indicates good prediction accuracy. A machine learning prediction model was also used to predict, and the prediction results are compared with that of the proposed model, which further verifies the accuracy and feasibility of the proposed model. A corrosion failure prediction management system for carbon steel oil and gas pipelines was developed based on the constructed model, which makes corrosion failure prediction more convenient and faster and provides a reference for the accurate prediction and efficient control of oil and gas pipeline corrosion failure.

Dynamic predictive analysis of the consequences of gas pipeline failures using a Bayesian network

Modern natural gas pipeline failures constitute devastating disasters, as they can result in cascading secondary crises. Therefore, reduction of buried gas pipeline's reliability, has become a major concern among stakeholders and researchers in recent years. This study employs a dynamic Bayesian network to investigate the consequences of natural gas pipeline failures. We consider seven parent nodes—age, diameter, length, depth, population, time of occurrence, and land use—and twelve consequence factors to analyze the overall losses stemming from pipeline failure. The proposed model can handle both static and dynamic systems using quantitative and/or qualitative data. To demonstrate the applicability and effectiveness of our developed model, we analyze the gas pipeline network of Regina in Saskatchewan, Canada. The results show that age and diameter are the two most important and sensitive parameters. The developed Bayesian network model will aid decision-makers in effectively managing and improving the reliability of their assets.

Comprehensive analysis of corrosion failure of blast furnace gas pipeline in a steel plant

The corrosion failure of blast furnace gas pipeline made of Q235 carbon steel in a steel plant was investigated. Macroandmicromorphology, thickness loss rate and phase composition of rust layer have been comprehensively studied. The macroandmicromorphology of failed tube in service for 3.5 years were observed by laser scanning confocal microscope (LSCM), scanning electron microscope (SEM) and energy disperse spectroscopy (EDS), and chemical composition of on-site condensed water and materials was examined. Results indicated that Cl is enriched on the surface of loose corrosion products, which is composed of FeOOH and Fe2O3. Large thickness loss rate and corrosion gully or holes suggest that mechanism of pitting corrosion are acid chemical corrosion and electrochemical corrosion.

A study on the microbial biocorrosion behavior of API 5 L X65 carbon steel exposed to seawater

AbstractDespite much progress achieved, corrosion in oil and gas pipelines remains a concern due to industrial failures, and environmental and economic damages. Oil and gas pipeline failures are very common as a result of the natural degradation of carbon steel exposed to the marine environment. It has been reported that the presence of microorganisms in seawater contributes to the intensification of corrosion and subsequent biofilm formation on metal surfaces. Few scientific publications have investigated the effects of seawater containing natural microorganisms on the internal corrosion of pipelines, which is the motivation for this study. The present study aims to report the corrosion behavior of carbon steel American Petroleum Institute (API) 5 L X65 exposed to seawater collected from the Persian Gulf containing microorganisms such as sulfate‐reducing bacteria (SRB), iron‐reducing bacteria (IRB), and acid‐producing bacteria (APB). Microorganisms were detected, segregated, colonized, and finally injected into autoclaved seawater to investigate their effect on the corrosion behavior of carbon steel. Nondestructive electrochemical techniques were performed to study the corrosion behavior and field emission electron microscope images and energy dispersive x‐ray analysis (EDS) were also utilized to characterize the corrosion products. The results confirmed the amounts of oxygen and iron presented of FeO as the main corrosion product in abiotic conditions. Corrosion products and electrochemical properties of steel have been influenced by microbial activity. The variation of the open circuit potentials (OCP) and Electrochemical Impedance Spectroscopy (EIS) demonstrated that SRB and APB could not only significantly harm an alloy, but also could alter its electrochemical behavior from uniform to local damage. The outcomes proved that biofilm formation and bacterial activity can cause serious degradation.

Failure Mechanism Analysis of L245M Oil and Gas Pipeline in an Oil Field

Corrosion perforation was one of the main reasons for the failure of long-distance oil and gas pipelines in oil and gas fields, which would lead to great hidden dangers in production safety. Based on the design standards and service conditions of the failed pipeline, the reasons of pipeline failure is explored through the analysis of chemical composition, mechanical properties and corrosion property, so as to provide a theoretical basis for the safety control of the oil field chemical equipment. The results showed that the chemical composition, mechanical properties and microstructure of the pipeline all met the design specifications. The corrosion perforation was caused by the condensate water deposition within the low-lying area at the elevation of 52 ∼ 58 km from the station, and the carbon dioxide corrosion and oxygen corrosion under the combined action of CO2, Cl-, O2 and so on.

Modeling and analysis of internal corrosion induced failure of oil and gas pipelines

Modeling and analysis of internal corrosion induced failure of oil and gas 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.

Risk Assessment Method for the Safe Operation of Long-Distance Pipeline Stations in High-Consequence Areas Based on Fault Tree Construction: Case Study of China–Myanmar Natural Gas Pipeline Branch Station

The safe operation of natural gas long-distance pipeline stations is the critical link for its sustainable transmission. Therefore, station operation risk assessment and space-time expression in high consequence areas have become a critical problem that must be urgently addressed. The conventional way to assess the probability of gas pipeline failure cannot meet the requirements of the natural gas operating companies for the safety assessment of natural gas stations. Taking the example of the China–Myanmar natural gas pipeline branch station, based on the fuzzy fault tree model (FFT), a comprehensive pipeline safety evaluation method (CPSE-FFT) has been introduced, which evaluates the risk factor of pipeline operation in the station area and the social security risk brought by it. In this case study, the method is applied to a branch station of the China–Myanmar long-distance pipeline. In the results, the probability of pipeline accidents in the area of the station is 5.79×10−4 times/year, and six buildings with higher risk levels among the 37 buildings are also exposed on the risk distribution map. The overall social risk level is also expressed. CPSE-FFT is a practical, reasonable, and accurate assessment method that can quantitatively reflect the risks posed by the safe operation of natural gas stations. This method is suitable for most natural gas station risk assessments. The results of the risk assessment provide a reference for managers and decision-makers to reduce the risk of natural gas stations.

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.

Under pressure: A qualitative comparative analysis on the factors contributing to the success and failure of cross-border gas pipeline construction in Europe and Turkey

Around 60% of internationally traded gas is shipped through pipelines. In Europe alone, there are around twenty cross-border gas transmission pipelines and new pipelines continue to be proposed. Yet, proposed pipelines often do not make it past the planning stage. Existing research has been unable to find a framework for determining success and failure in cross-border gas pipeline projects’ construction. In this study, a six condition explanatory framework is developed based on an extensive literature review to explain the success or failure of gas pipelines in Europe. These conditions include: support from the involved states, powerful states, the Commission and the US, as well as the number of stakeholders and the technical difficulties (length and onshore/offshore). The study then conducts a Qualitative Comparative Analysis on 21 European gas pipeline projects and finds that support from those involved, powerful member states and the Commission is imperative for the successful construction of gas pipelines in Europe. The absence of these conditions has the opposite effect.

Failure of a Squeeze-Clamped Polyethylene Natural Gas Pipeline

Failure of a Squeeze-Clamped Polyethylene Natural Gas Pipeline

Reliability evaluation of corroded pipeline under combined loadings based on back propagation neural network method

Corrosion is a common type of defect that causes failure of oil and gas pipelines. Pipelines often bear the combined loadings of axial compression force, bending moment and internal pressure in operation; therefore, it is of great significance to study the reliability of pipeline under different combined loadings. First, this work established a nonlinear finite element model of corroded pipeline for the typical internal corrosion defects of groove shape, and the reliability of the model and the accuracy of the finite element analysis method are verified. Then the burst pressure of pipeline under combined loadings is investigated. Second, this work established the BP network analysis and prediction model. The results of numerical simulation are used as samples to train the neural network. The prediction limit function value is output through the forward simulation function, which is compared with the objective function to judge whether the pipeline is reliable. Finally, back propagation neural network model calculated the reliability of pipeline under different working conditions combined with Monte Carlo method. The effects of load, corrosion depth, axial corrosion length, circumferential corrosion width and variation coefficient of parameters on pipeline reliability are analyzed. It is found that axial force and bending moment affect the reliability of corroded pipeline. Generally, the higher the combined loadings are, the lower the reliability is, but this law will be different due to the influence of corrosion parameters. In addition, the corrosion depth has the greatest influence on the reliability of corroded pipeline, followed by the corrosion width and the corrosion length. The coefficient of variation of corrosion depth has a great influence on the reliability of pipeline with high depth corrosion defect. These conclusions can provide references for the reliability evaluation of pipelines.

Semi-supervised learning framework for oil and gas pipeline failure detection

Quantifying failure events of oil and gas pipelines in real- or near-real-time facilitates a faster and more appropriate response plan. Developing a data-driven pipeline failure assessment model, however, faces a major challenge; failure history, in the form of incident reports, suffers from limited and missing information, making it difficult to incorporate a persistent input configuration to a supervised machine learning model. The literature falls short on the development of appropriate solutions to utilize incomplete databases and incident reports in the pipeline failure problem. This work proposes a semi-supervised machine learning framework which mines existing oil and gas pipeline failure databases. The proposed cluster-impute-classify (CIC) approach maps a relevant subset of the failure databases through which missing information in the incident report is reconstructed. A classifier is then trained on the fly to learn the functional relationship between the descriptors from a diverse feature set. The proposed approach, presented within an ensemble learning architecture, is easily scalable to various pipeline failure databases. The results show up to 91% detection accuracy and stable generalization ability against increased rate of missing information.

Risk Classification of Shale Gas Gathering and Transportation Pipelines Running through High Consequence Areas

Shale gas gathering and transportation pipeline poses significant risk due to special geographical conditions and different climatic conditions in high consequence areas such as Sichuan and Chongqing. The risks become critical as gas pipelines run through high consequence areas such as hospital, market, and scenic areas. This study presents a risk classification method for the pipelines running through high consequence areas. The proposed method considers different failure scenarios including third-party damage, corrosion, design and construction defects, mis-operation, and natural disasters. The method uses subjective and objective data from different sources. To minimize the subjectivity and data uncertainty, an improved fuzzy analytic hierarchy process was used to process data. The estimated risk is used to classify different risk zones. After the failure of shale gas pipelines in HCAs, in order to reduce the adverse impact of emergencies, personnel should immediately organize an evacuation to a safe area, focusing on the diagnosis and analysis of risk factors that are more likely to lead to pipeline leakage. The developed classes are verified using field data. The study observes that risk levels classified using the proposed method provide realistic assessments of hazard zoning. Risk zoning will help develop effective risk management strategies.

A risk assessment framework considering uncertainty for corrosion-induced natural gas pipeline accidents

Corrosion degradation is one of the main causations of natural gas pipeline failure, which poses a severe threat to human life, assets, and the environment. This paper develops a methodology to assess the risk of corrosion-induced natural gas pipeline accidents considering the uncertainties in incident escalations. This approach can capture both the uncertainties in corrosion failure likelihood and the impact of pipeline leak accidents. A pair of limit state functions are established to estimate the corrosion failure probability of the pipeline. The pipeline accident from gas release to vapor cloud explosion (VCE) is modelled using empirical models. Subsequently, the uncertainties in gas pipeline corrosion failure accidents, reflected by some uncertain parameters, e.g., basic pipeline parameters, corrosion defect and environmental conditions et al., are identified. These identified parameters are discretized and described using a set of probability density functions. Eventually, Monte Carlo (MC) method is utilized to solve the established models. Besides, sensitivity analysis is conducted to study the effect of uncertain parameters on the likelihood and impact of a pipeline accident. A practical case is used to test the methodology, proving a valuable tool for risk assessment of corrosion-induced natural gas pipeline accidents.