Pipeline Risk Research Articles

A safety management system for natural gas pipeline in subsidence area of coal mine

ABSTRACT As one of the main ways to transport natural gas, pipeline safety is of great significance to maintaining social stability and ensuring the national energy supply. Aiming at the safe operation of natural gas pipelines in subsidence areas of coal mine, this work proposes a safety management system for condition monitoring and control. This system contains three technical modules: the surface and pipeline displacement monitoring, the risk assessment index system, and early warning model. Firstly, the collected data of site topography and pipeline conditions are transmitted to the database remotely. Second, multi-source data are collected to form data reports and conduct in-depth analysis and mining. Then, according to the analysis results, the pattern of surface and pipeline displacement is explored, the risk assessment system of gas pipelines is constructed, and the pipeline strain is monitored. Real-time computing and other technologies are developed to form a safety management system for gas pipelines in subsidence areas. Taking Shanxi Province in China as an example, based on the system developed by the technical modules, the application of surface and pipeline displacement monitoring, risk assessment, and pipeline stress-strain monitoring technology in subsidence areas is achieved. The results show that the risk score of the area where the pipeline is located is 82, which is a high-risk level. In addition, in the process of strain monitoring, the measured stress at the most unfavorable position is 140.207 MPa, which meets the allowable stress requirements. This safety management system can provide important support for decision-making of risk prevention, assessment, and management of natural gas pipelines in subsidence areas of coal mine.

A novel fuzzy approach to gas pipeline risk assessment under influence of ground movement

The gas transport infrastructure is frequently localized in areas subjected to anthropogenic movements and strains. The potential impact of the ground movements on the gas pipeline in the aspect of its damage can be properly assessed e.g. by predicting strains, taking into account the causes of terrain movement. On the other hand, the hazard is also related to technological factors like design of the pipeline. The presented method is based on artificial intelligence methods allowing for evaluation of probability of failure risk in gas supply pipeline sections. The Mamdani fuzzy inference was used in this study. Uncertainty of variables characterizing the resistance of the gas pipeline and predicted continuous deformations of ground surface were accounted for in the model by using triangular-shaped membership functions. Based on the surface deformations and gas pipeline resistance and the inference model one can make prediction when the gas pipeline is hazarded. There were estimated two the most hazarded parts for two pipelines. We proved that the proposed model can contribute to the protection, costoptimization of the designed pipelines and to the repairs of the existing gas pipelines.

Two-level quantitative risk analysis of submarine pipelines from dropped objects considering pipe–soil interaction

Pipeline damage from platform dropped objects is a significant risk for offshore installations. Pipelines are usually buried to reduce the risk. Previous methodologies and standards haven't addressed the nonlinearity and pipe–soil interaction in risk analysis. Moreover, the appropriate burial depth remains undetermined, causing unnecessary investment or potential risk. To provide a comprehensive evaluation, this study presents a two-level quantitative risk analysis for pipeline failure. Finite element analysis and probabilistic approaches are coupled to quantify the effects of nonlinearity and pipe–soil interactions and the underlying uncertainties of related factors. At the macro-level, a frequency-consequence procedure is developed to assess the spatial distributions of pipeline failure probabilities, which are then utilised in the preselection of pipeline routes and post-design risk assessment. At the micro-level, based on a secondary development technology, a reliability-based framework is proposed and verified, providing insights into the failure mechanisms of each collision event and investigate the variabilities of related parameters. These methodologies can be used as guidelines for pipeline risk analysis and protection, avoiding excessive conservatism by considering pipe–soil interactions (seabed flexibility and burial depth). The relationships between protection measures and failure probabilities are established to provide clues for the selection of optimal burial depth.

Sensitivity enhancement for microbial fuel cell type oil sensor by regulation of anode area, external resistance and substrate concentration

To promote application of the microbial fuel cell (MFC)-based oil sensor in monitoring oil leak risks of oil pipelines and to overcome the influence of signal fluctuation, the sensor sensitivity needs to be enhanced. Simple and viable start-up measures such as regulating anode area, external resistance and substrate concentration were implemented to investigate their influence on the sensitivity. Results show that enlarging anode area and lessening external resistance will significantly promote the sensitivity (change of current signal per oil amount). Increasing anode area from 3.1 to 9.6 cm2 resulted in a 711 % increase in the initial sensitivity and a 358 % increase in the average sensitivity. Decreasing external resistance from 1300 to 400 Ω led to a 797 % increase in the initial sensitivity and a 378 % increase in the average sensitivity. While controlling substrate concentration from 155.7 to 500.7 mg-COD/L had a much smaller impact on the sensitivity. Manipulating substrate concentration over an augmented range (41.2–500.7 mg-COD/L) showed that insufficient substrate (< 80 mg-COD/L) induced a drastic current drop. A theoretical sensitivity formula was derived from a data fitting model based on charge conservation. The formula reveals that a sensitivity factor relating to the ratio of ORR and oxygen transfer is key to enhancing sensitivity, which was verified to have a strong positive correlation with current by data analysis and EIS. In general, this study shows that measures to elevate current will significantly enhance sensitivity besides keeping substrate concentration at a high level is a prerequisite for maintaining it.

A CRITIC-VIKOR based robust approach to support risk management of subsea pipelines

The risk factors related to subsea pipeline failure are diversified and dependent. Efficient risk management is critical for preventing subsea pipeline failure accidents. This paper presents a novel approach for effective risk management of subsea pipelines, which can extract the importance of pipeline risk factors and prioritize the risk management of subsea pipelines. This approach integrates CRITIC with VIKOR to capture the internal correlation and inconsistency among pipeline risk factors and accurately quantify the risk of different pipe sections. The pipeline safety factors are identified to build index-based risk evaluation system of subsea pipelines. The failure risk of subsea pipeline varies with the pipe sections, which is divided considering the structural conditions of pipeline and environmental conditions. The CRITIC method is utilized to assign the weight of risk indices, and the VIKOR method is applied to assess the importance of pipe sections in a Multiple criteria decision-making (MCDM) manner, which can help to find the weaknesses of the pipeline and decide the risk of which pipe section should be mitigated prior. A case study is performed to illustrate the application of the proposed methodology, and it is observed that the approach can be a valuable tool to support more effective risk management and decision-making of subsea pipelines.

Multidimensional risk assessment and categorization of hydrogen pipelines

The use of hydrogen has been shown to be an efficient form of producing energy and meeting society's demands for energy. In this perspective, pipelines are known as the safest and most economical transportation mode for hydrogen. However, accidents in this type of structure lead to multiple consequences of losses such as those related to people, the environment and properties. From this perspective, decision-makers are required to tackle the likelihood of these and to make suitable decisions that can prevent accidents from happening. Multicriteria methods have played an important role in addressing multiple risk problems when there is a need to assign priorities to risk-based decisions. This paper puts forward a multidimensional risk model to categorize hydrogen pipeline sections according to levels of risks based on Utility Theory and the ELECTRE TRI method. A probabilistic modeling is proposed so as to incorporate elements of risk analysis into the evaluation of situations such as accidental scenarios, their probabilities and consequences. An application of the model is presented for a hydrogen pipeline. The results demonstrate that measures to prevent and mitigate risks can be sharpened by taking into account the human, environmental and financial risks dimensions of impacts. Thus, the sections of the pipeline studied are sorted into categories of risks, where 4 out of 10 sections are assigned to the high-risk category, 1 to the medium-risk category, and 5 sections to the low-risk category. A local sensitivity analysis was conducted which varied the weights of the dimensions. Varying the weights by 20% showed that the risk of 3 sections increases to the medium-risk category and 1 section changes to the high-risk one. Moreover, this paper indicates visualization tools that are efficient at communicating information on the levels of risk and the categories of the sections, thus contributing to assessing models that categorize risk in hydrogen pipelines. By doing so this paper supports the process of prioritizing resources for pipeline sections, the aim being to minimize multiple losses.

BaNTERA: A Bayesian Network for Third-Party Excavation Risk Assessment

Third-party damage constitutes a major threat to underground natural gas pipeline safety; in the U.S., between 2016 and 2020, it caused eleven fatalities, twenty-nine injuries, and $124M USD in property damage losses. Several research studies have been carried out to identify the causes and contextual factors leading to third-party damage. However, there is a lack of models that are not only causally-based, but also comprehensive and suitable for modeling the probabilities of a pipe hit and subsequent damage. This paper presents the development process and results of building BaNTERA, a probabilistic Bayesian network model for third-party excavation risk assessment in the U.S. BaNTERA’s capabilities for risk-informed decision support are presented in three ways: verification of the model’s performance, validation of its damage rate predictions with historical industry data, and application in multiple case study scenarios. Preliminary results indicate that BaNTERA offers valuable insight including and beyond a probability estimation of third-party damage. Using the best available industry data and previous models derived from multiple sources, different inference methods can assist in pipeline damage prevention and risk mitigation. As such, BaNTERA represents a promising holistic and rigorous tool for addressing third-party excavation damage in natural gas pipelines.

XGBoost algorithm-based prediction of safety assessment for pipelines

Pipeline safety is closely related to people's lives, environment and economic development. The traditional methods for pipeline inspection are laborious and very expensive, such as non-destructive examination (NDE) and engineering assessment (EA). In recent years, machine learning (ML) methods have been proven useful to solve pipeline assessment problems. These methods have mainly focused on a characteristic analysis of weld defects or pipeline life curves, and have not provided systematic and effective assessment methods for the pipeline industry. In this study, after comparing with other reference ML models, XGBoost algorithm-based predictive model is proposed for safety assessment of pipelines. First, two models based on XGBoost are established and tuned to create relationships between 9 parameters (e.g., internal diameters of pipelines) and NDE results/EA results. Then grid search and various statistical metrics (confusion matrices, AUC-ROC and confidence levels) are applied to optimize and assess these models. In addition, the prediction interpretability of XGBoost is analyzed to show its usefulness and applicability in industry. The results show that both of these two models satisfy the required predictive performance. The XGBoost model based on the EA results is finally selected thanks to its superiority by achieving an accuracy of 98.5%, AUC-ROC of 99% in testing, higher ratio of certain prediction (91.8%) and other metrics (precision of 98.8% and recall of 98.7%). In the analysis of model interpretations, a maximum operating pressure (MOP) is found to be the most important feature to affect the safety of pipelines. The results also show that when the current ML method based on the historical results of NDE and EA is established and proven stable, it can accurately predict the future risk of new pipelines in the absence of NDE and EA. In the studied research region, up to 91.8% of the funds and manpower spent on NDE and EA can be saved based on this ML method. Overall, the proposed XGBoost model provides an alternative to help industry evaluate pipeline welds with very few excavations and manual inspections. As more data from other regions is collected and used to train ML models, this method can be widely applied in industry.

Development of probabilistic failure pressure models for pipelines with single corrosion defect

Many models have been developed in the past to predict the failure pressure of a pipeline with a single corrosion defect. However, most of the existing models are conservative and may lead to unnecessary repairs and maintenance in the pipeline risk management. The aim of this study is to develop probabilistic failure pressure prediction models that are suitable for risk assessment of corroded pipelines. The proposed models are developed at three levels of pipe ultimate strength based on a comprehensive database (consist of both experimental and numerical data) established in this study. When comparing with the existing models, it is found that the proposed models provide unbiased and more accurate predictions. Also, the results of the reliability analysis of a corroded pipeline with various material properties and corrosion configurations reveal that the performance of a failure pressure prediction model plays a critical role in determining the structural performance of corroded pipelines.

CO2 Pipeline risk assessment and comparison for the midcontinent United States

A comprehensive quantitative risk assessment for the construction and operation of CO2 transportation networks considered for the Midcontinent United States was conducted. The results showed risks associated with CO2 pipelines were significantly less than those of other pipeline types. The assessment used four conceptual pipelines of different lengths to discuss risks operators may see. The assessment evaluated the risk associated with construction and operation using data from the US Occupational Safety Health Administration to determine the risk of injury or death for pipeline workers and data from the US Pipeline and Hazardous Materials Safety Administration for CO2, natural gas distribution, natural gas transmission/gathering, and non-CO2 hazardous liquid pipelines to develop quantitative likelihood and severity values leading to risk values. The data for the assessment covered incidents from 2010 to 2017 for CO2 pipelines. The average risk for construction and 30 years of operation for four CO2 pipeline configurations ranging between 79 and 1,546 miles in length was found. The construction and operational risk averaged between $1,400,521 (approximately $0.02/tonne of CO2) for the shorter pipeline (79 miles) and $27,481,939 (approximately $0.10/tonne of CO2) for a longer pipeline (1,546 miles). The largest risks of fatality for CO2 pipelines comes from vehicle transport. The largest operational risk to the pipeline was due to leakage. Public pipeline opposition is also a significant risk; it was not quantified but is addressed.

Earthquake Risk of Gas Pipelines in the Conterminous United States and Its Sources of Uncertainty

Relatively little research has been conducted to systematically quantify the nationwide earthquake risk of gas pipelines in the US; simultaneously, national guidance is limited for operators across the country to consistently evaluate the earthquake risk of their assets. Furthermore, many challenges and uncertainties exist in a comprehensive seismic risk assessment of gas pipelines. As a first stage in a systematic nationwide assessment, we quantify the earthquake risk of gas transmission pipelines in the conterminous US due to strong ground shaking, including the associated uncertainties. Specifically, we integrate the US Geological Survey 2018 National Seismic Hazard Model, a logic tree–based exposure model, three different vulnerability models, and a consequence model. The results enable comparison against other risk assessment efforts, encourage more transparent deliberation regarding alternative approaches, and facilitate decisions on potentially assessing localized risks due to ground failures that require site-specific data. Based on the uncertainties approximated herein, the resulting sensitivity analyses suggest that the vulnerability model is the most influential source of uncertainty. Finally, we highlight research needs such as (1) developing more vulnerability models for regional seismic risk assessment of gas pipelines; (2) identifying, prioritizing, and measuring input pipeline attributes that are important for estimating seismic damage; and (3) better quantifying seismic hazards with their uncertainties at the national scale, for both ground failures and ground shaking.

Risk assessment of buried gas pipelines based on improved cloud-variable weight theory

Various risk factors of urban gas pipelines will lead to leakage, significantly impacting the environment, assets, and life safety, thus making pipeline risk prevention and control crucial. However, existing methods cannot sufficiently model the uncertainty in the pipeline risk evaluation process. In addition, the calculation method of factor weights may lead to unrealistic results. In this study, variable weight theory and cloud theory are introduced. Then, a novel method of cloud-variable weight function is proposed to analyze the pipeline's risk level and critical risk factors by establishing a pipeline risk assessment index system. The proposed method fully considers the uncertainty in the evaluation process, resolves the contradiction of existing methods to model the fuzzy concepts accurately, optimizes the weight distribution, and obtains a more scientific and reasonable assessment result. A case study is conducted on a pipeline in Beijing City. The results illustrate that the proposed method is beneficial for helping pipeline operators determine the pipeline risk status and weak links in the pipeline system, thereby providing a basis for risk control and rehabilitation.

An integrated risk analysis model for corroded pipelines subjected to internal pressures: Considering the interacting effects

The interacting effects were demonstrated to reduce the burst capacity of corroded pipelines significantly, which would cause the potential risk if ignored. To address this point, this study presented an integrated risk analysis model of corroded pipelines subjected to internal pressures, considering the interacting effects. A user-defined program was designed to perform the finite element analysis (FEA) for any random input, and verified with the existing experiments. Furtherly, through the machine learning scheme, the FEA was linked with the Monte Carlo Simulation to estimate the failure probabilities. Through a case study, the failure risk of pipelines with single defect and interacting defects were analyzed, and the effect of the interval space between interacting defects with the failure mechanism was discussed. Moreover, the influence of the uncertainties from the considered stochastic variables was investigated. The results show that the interacting effects increased at most 65% of the failure risk. The failure probability was in a non-linear relationship with the interval space owing to the changing sensitivity of the burst capacity to the interval space. The uncertainties of the wall thickness and the growth rate of the corrosion depth had pronounced impacts on the failure risk. The corresponding changing characteristics were explained.

Pipeline risk big data intelligent decision-making system based on machine learning and situation awareness

Pipeline risk big data intelligent decision-making system based on machine learning and situation awareness

Underwater bubble plume image generative model based on noise prior and multi conditional labels

The characteristics of underwater bubble plume dispersion behavior are important to assess and manage potential risks of underwater gas pipelines. In order to generate sufficient image samples for underwater bubble plume research based on artificial intelligence, we try to design an optimized generative adversarial network. In this paper, a novel underwater bubble plume image generative model that combines noise prior and multi conditional labels is proposed and evaluated using different testing dataset. In the proposed framework, the noise prior which contains the image attributes and the category features are trained with the help of VAEs, prior noise and multi conditional labels are utilized to construct the generative and discriminative models. Finally, 3 types of the underwater bubble plume images generated from the experiment is contrasted and evaluated. The experimental results show that，compared with the existing methods text2img and CGANs，the FID values of the generated images are reduced by 97.4% and 22.8% on SUIM dataset, and the FID values are reduced by 97.4% and 22.8% on BUBBLE dataset, our proposed framework achieves satisfactory and promising performance.

A New Risk Assessment Model to Check Safety Threats to Long-Distance Pipelines

Abstract With the number of long-distance pipelines increasing in China, risk management has become important for controlling pipeline leakage. However, all the current assessment technologies are semiquantitative and do not include inspection data. To address this problem, a new quantitative risk assessment model is proposed to guide decision-making on excavation inspection and maintenance. Based on previous failure cases, the model includes data about the surrounding soils as well as about the pipeline's protective layer, cathodic protection (CP), and pipe wall thickness readings. Testing of the proposed model on previous failure cases shows that the new model can correctly assess the real leakage risk of a long-distance pipeline and support the quantitative integrity management of a long-distance pipeline during its whole service life.

Investigating soil conditions around buried water pipelines using very‐low‐frequency band alternating current electrical resistivity survey

ABSTRACTUnderground pipeline infrastructures, such as water supply and industrial water pipes, were rapidly constructed in Japan during the 1970–1980s economic boom and have been ageing quickly. In general, corrosion of buried metal water pipes depends on the physicochemical properties of the soil around them. Conventionally, when conducting such investigations, the soil is excavated and sampled to analyse these properties in laboratories. As this damages the paved road surface, an alternative method is required. Resistivity is a significant physical property measured when investigating the corrosion risk of underground pipelines. Therefore, if geophysical exploration can facilitate the investigation of soil resistivity from the ground surface, it will play a key role in the renewal planning of water pipes. Traditionally, geophysical methods such as electrical and electromagnetic exploration have been used for measuring subsurface resistivity. Electrical exploration is a robust and noise‐tolerant method; however, it requires electrode installation. Most roads over the pipelines are paved, making conventional electrical exploration using metal electrodes a challenging task. Therefore, the Geological Survey of Japan, the National Institute of Advanced Industrial Science and Technology, has developed a very‐low‐frequency band alternating current electrical surveying technique that uses water‐saturated polyvinyl alcohol sponge electrodes. In this technique, electrodes are placed on an asphalt or concrete paved surface and the electrode measures the soil resistivity profile and detects the corrosive soil distribution without damaging the paved surface. Moreover, we improved this equipment to enable vertical electrical survey of numerous points and acquired data for 740 m of the survey line in 1 day at Maborikaigan coast in Yokosuka City. We found that the resistivity values varied with the depths and locations of the buried water pipes. These variations indicated that surveys and evaluations on long survey lines along water pipe burial routes are essential for risk management in water pipelines.

Rank-based risk target data analysis using digital twin on oil pipeline network based on manifold learning

Oil and Gas Pipeline (OGP) projects face a wide scope of wellbeing and security Risk Factors (RFs) all around the world, especially in the oil and gas delivering nations having influencing climate and unsampled data. Lacking data about the reasons for pipeline risk predictor and unstructured data about the security of the OGP prevent endeavors of moderating such dangers. This paper, subsequently, means to foster a risk analyzing framework in view of a comprehensive methodology of recognizing, dissecting and positioning the related RFs, and assessing the conceivable pipeline characteristics. Hazard Mitigation Methods (HMMs), which are the initial steps of this approach. A new methodology has been created to direct disappointment investigation of pinhole erosion in pipelines utilizing the typical pipeline risk strategy and erosion climate reenactments during a full life pattern of the pipeline. Hence in the proposed work, manifold learning with rank based clustering algorithm is incorporated with the cloud server for improved data analysis. The probability risk rate is identified from the burst pressure by clustering the normal and leak category to improve the accuracy of the prediction system experimented on the lab-scale oil pipeline system. The numerical results like auto-correlation, periodogram, Laplace transformed P-P Plot are utilized to estimate the datasets restructured by the manifold learning approach. The obtained experimental results shows that the cloud server datasets are clustered with rank prioritization to make proactive decision in faster manner by distinguishing labelled and unlabeled pressure attributes.

Analysis on the Potential Safety Risks of Long-distance Oil and Gas Pipelines and Research on Management Measures

Analysis on the Potential Safety Risks of Long-distance Oil and Gas Pipelines and Research on Management Measures

Failure Mode and the Prevention and Control Technology of Buried PE Pipeline in Service: State of the Art and Perspectives

Material defects and external environmental factors increase the engineering safety risks of buried PE pipelines in service. This paper mainly reviews the failure mode and the prevention and control technology of buried PE pipelines in service, as well as comparative failure characteristics between the buried PE and steel pipelines. The main failure factors of buried PE pipelines can be divided into four levels: the first level is third‐party damage; the second level includes three failure factors, i.e., surface subsidence, joint failure, and aging failure; the third level consists of pipe piercing, crack development, and pipeline defect; the fourth level is a corrosion failure. Besides, steel and PE pipelines have various physical and mechanical properties, which lead to a significant difference in the service performance and degradation mechanism in practice. For instance, corrosion and welded joint leakage are the two common problems in steel pipelines during the service period, while PE pipelines have excellent corrosion resistance. Additionally, in order to ensure and maintain the long‐term operations of PE pipelines, it is of great significance to develop and promote nonexcavation technologies of construction, renewal, and repair for natural gas‐buried PE pipelines. Furthermore, based on the above studies, some further research studies on buried PE pipelines in service are suggested and discussed, e.g., (a) the service performance and degradation mechanism of buried PE pipelines in complicated environmental conditions, (b) the interaction mechanism among the engineering structure, PE pipelines, and geological environment, as well as PE pipeline geological soils coupled in multiple physical fields, and (c) the combinations of the traditional engineering risk assessment method and the numerical analysis method considering the interaction between the PE pipeline and geological environment. The results could be helpful for a better understanding of the operation conditions of buried PE pipelines, and it is also hoped that this study could provide guidance for the safe operation, maintenance, and integrity management of buried PE pipelines.