Pipeline Risk Assessment Research Articles

Urban Natural Gas Pipeline Operational Vulnerability Under the Influence of a Social Spatial Distribution Structure: A Case Study of the Safety Risk Patterns in Kunming, China.

Frequent urban natural gas pipeline accidents pose a serious threat to the safety of people and property in surrounding areas. However, current research on natural gas pipeline risks primarily focuses on evaluating the pipelines themselves, with no established method for assessing the impact of pipeline disasters on surrounding areas. This paper proposes an urban natural gas pipeline risk assessment method that integrates the physical attributes of the pipelines with an analysis of social vulnerability based on urban social spatial distribution. Using urban Point of Interest (POI) data, a social spatial distribution model for potential natural gas pipeline accidents is constructed. The risk of pipeline failure is assessed based on physical vulnerability, while the consequences of failure are evaluated through social vulnerability. This method combines the analysis of physical and social vulnerabilities to achieve a comprehensive urban natural gas pipeline risk assessment. The results identified 68 out of 6148 pipelines in the study area as "double high" pipelines, characterized by high physical vulnerability (relatively high risk pipelines) and high social vulnerability (involving level IV areas). The high risk communities identified in the study area are the Cuihu West Road Community and the Daguan Commercial City Community, highlighting the characteristics of risk distribution. The findings suggest that this study contributes to improving urban resilience to natural gas pipeline incidents, reducing potential economic losses and public impacts, and enhancing urban public safety. It also provides new insights into natural gas pipeline risk assessment and urban public safety research.

Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry.

AI-Powered Solutions for Missing Data in Pipeline Risk Assessments

AI-Powered Solutions for Missing Data in Pipeline Risk Assessments

Risk assessment of gas pipeline using an integrated Bayesian belief network and GIS: Using Bayesian neural networks for external pitting corrosion modelling

AbstractCorrosion poses a great risk to the integrity of oil and gas pipelines, leading to substantial investments in corrosion control and management. Several studies have been conducted on accurately estimating the maximum pitting depth in oil and gas pipelines using available field data. Some of the frequently employed machine learning techniques include artificial neural networks, random forests, fuzzy logic, Bayesian belief networks, and support vector machines. Despite the ability of machine learning methods to address a variety of problems, traditional machine learning methods have evident limitations, such as overfitting, which can diminish the model's generalization capabilities. Additionally, traditional machine learning models that provide point estimations are incapable of addressing uncertainties. In the current study, a Bayesian neural network is proposed to include uncertainty in estimating the corrosion defect of a pipeline exposed to external pitting corrosion. The results are then incorporated into a Bayesian belief network for evaluating the probability of failure and its corresponding consequences in terms of social impact, thus forming a comprehensive risk assessment framework. The results of the Bayesian neural network are validated using field data and achieved a testing accuracy of 90%. The framework of the study offers a powerful decision‐making tool for the integrity management of pipelines against external corrosion.

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data.

Pipeline risk assessment is crucial for pipeline safety management and operation. The aim of this study is to develop a comprehensive assessment model that accurately evaluates pipeline risks and ensures the safe and reliable operation of the pipeline system. The model is based on multisource spatial data and is primarily applicable to long-distance oil and gas pipelines that traverse complex geological conditions in mountainous areas. The research is conducted using the example of the Jinliwen natural gas pipeline in Zhejiang Province, China. By analyzing the geological data of the study area and the potential risks that the pipeline may encounter, a comprehensive risk assessment indicator system for the pipeline was developed using slope units to divide pipeline sections. The pipeline risk levels are classified using the K-means clustering-entropy weighted-random forest algorithm. The model is evaluated using accuracy (Acc), precision (Pre), recall (R), F1-score, and the ROC curve. The results show that the model has an accuracy of 0.917, a precision of 0.92, a recall of 0.916, an F1-score of 0.914, and an AUC (Area Under Curve) of 0.93, indicating its strong predictive capability. The risk assessment results demonstrate a strong consistency when compared with actual incident events. This indicates that the constructed model effectively reflects the influencing factors of pipeline risk, providing a basis for pipeline risk assessment and disaster prevention and mitigation efforts in similar regions.

Improved ResNet50 Deep Learning Algorithm for Dust Explosion Risk Assessment of Dust Removal Pipelines

ABSTRACT In production environments dealing with explosive dust, the associated hazards pose critical risks to employee health and compliance with production environment regulations. To mitigate these risks, prevent equipment cross-contamination, and enhance workplace safety, most production equipment is equipped with dust removal systems. Given the potential explosiveness of dust, dust explosions can initiate high-temperature and high-pressure processes, leading to severe damage. Consequently, preventive measures must be implemented at the source. Incidents of dust explosions caused by dust removal systems are relatively common. To enhance the accuracy of identifying dust explosion risks within dust removal pipelines, this study presents an improved ResNet-50 algorithm for assessing explosion risks in such pipelines. By incorporating the SE attention module and leveraging ImageNet pre-training, we developed the SEResNet50 model, trained on a proprietary dataset using transfer learning. The ablation experiment verified the contribution of the SE attention module and transfer learning to the algorithm. Furthermore, the algorithm is compared with four classic recognition algorithms including VGG16, ResNet18, AlexNet, and GoogleNet. The results show that the SE attention mechanism and transfer learning improve the explosion risk assessment accuracy by 1.81% and 2.33% respectively. More specifically, the algorithm achieves a performance of 97.50%, 95.00%, 100.00%, and 97.43% in terms of Accuracy, Precision, Recall, and F1 Score respectively, which is better than the four algorithms. The algorithm’s ability to detect dust of different colors was evaluated through heatmap visualization of depth features. This algorithm meets the need for accurate risk assessment of dust removal pipelines and improves the safety management level of enterprises.

Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies

Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.

Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning

This paper presents a novel methodology utilizing machine learning (ML) techniques to accurately forecast the failure pressure of corroded pipelines, which are pivotal for oil, gas, and petroleum product transport. In contrast to prior approaches, this study overcomes their limitations by integrating physically significant factors tied to corroded pipeline failure mechanisms and delivering interpretable models. Robust ML models are constructed using dependable experimental data from pertinent literature sources, ensuring research rigor. To bolster model interpretability, the SHAP method is deployed, facilitating a comprehensive grasp of each feature's impact on predictions. The performance and efficacy of the developed ML models are rigorously assessed using real-world pipeline incidents reported by the PHMSA in the US, further validating their practical utility. The findings not only affirm the models' precision and dependability but also yield valuable insights, underscoring their importance. This study makes a substantial contribution to the enhancement of pipeline integrity management by enabling precise failure pressure predictions in corroded pipelines. The insights gleaned from this research hold practical implications, enabling well-informed choices in pipeline design, integrity management, and risk assessment. Thus, it stands as a valuable resource for pipeline operators aiming to optimize resource allocation and bolster overall pipeline safety and efficiency.

Advancing lightning risk assessment of pipeline insulating flanges with isolating spark-gap

Insulating flanges serve as crucial components, strategically positioned between two segments of a pipeline to ensure effective electrical isolation. These elements play a vital role in facilitating the optimal performance of the cathodic protection system of buried pipeline networks. Moreover, an isolating spark gap is commonly integrated between adjacent pipeline sections to mitigate the stress voltage induced on the insulating flange during lightning strike events. . Despite this protective measure, certain scenarios may arise where the voltage drop across the isolating spark gap's connecting cables surpasses the insulating flange's dielectric strength, leading to undesirable failures. The magnitude of this voltage drop is contingent upon various factors, including the parameters of the incoming surge as well as the length of the connecting cables. In this paper, we present both straightforward theoretical calculations and intricate modelling techniques that can be employed to assess the lightning risk associated with the insulating flanges of pipelines that incorporate isolating spark gaps.

A Model for Assessing the Potential Impact Radius of Hydrogen Pipelines Based on Jet Fire Radiation

The accurate determination of the potential impact radius is crucial for the design and risk assessment of hydrogen pipelines. The existing methodologies employ a single point source model to estimate radiation and the potential impact radius. However, these approaches overlook the jet fire shape resulting from high-pressure leaks, leading to discrepancies between the calculated values and real-world incidents. This study proposes models that account for both the mass release rate, while considering the pressure drop during hydrogen pipeline leakage, and the radiation, while incorporating the flame shape. The analysis encompasses 60 cases that are representative of hydrogen pipeline scenarios. A simplified model for the potential impact radius is subsequently correlated, and its validity is confirmed through comparison with actual cases. The proposed model for the potential impact radius of hydrogen pipelines serves as a valuable reference for the enhancement of the precision of hydrogen pipeline design and risk assessment.

A review on hazards and risks to pipeline operation under transporting hydrogen energy and hydrogen-mixed natural gas

As an efficient and clean fuel, hydrogen energy plays an important role in relieving the energy crisis and achieving the orientation of zero carbon emissions. Transportation is the key link in the construction of hydrogen energy infrastructure. For large-scale and long-distance transportation of hydrogen, pipeline transportation has the advantages of high efficiency and cost saving. While using the existing natural gas pipeline to transport hydrogen, it would economize the economic cost, time cost and labor cost. However, the transportation of hydrogen may bring more hazards and risks. Based on the investigation of a large number of literatures, the research advance in hydrogen embrittlement, leakage, combustion and explosion risk of hydrogen and hydrogen-mixed natural gas pipelines was reviewed. The mechanism, research means and evaluation methods of hydrogen embrittlement, as well as the experimental and numerical simulation research results of leakage, combustion and explosion were discussed in detail. The definite and important conclusions include: (1) For buried hydrogen-mixed natural gas transportation pipeline, the leakage rate of hydrogen and methane is the same, the formation of the leakage crater is foreign to the nature of leakage gas. (2) When adding less than 25 volume percentage of hydrogen into the natural gas pipelines, the explosion risk would not be increased. Future research should focus on the risk prediction, quantitative risk assessment, intelligent monitoring, and explosion-suppression technical measures of hydrogen and hydrogen-mixed natural gas transportation pipelines, so as to establish comprehensive and multi-level pipeline safety protection barriers.

Probabilistic-based burst failure mechanism analysis and risk assessment of pipelines with random non-uniform corrosion defects, considering the interacting effects

Corrosion defects were primary causes for pipeline burst failures. Traditional methodologies simplified the realistic irregular-shaped defects as uniform ones, ignoring the effects of random morphologies on failure behaviours, which caused deviations in remaining strength pb estimation and reliability analysis. Addressing this issue, through coupling random field, non-linear finite element analysis and Monte-Carlo Simulation, an integrated methodology was developed to describe failure behaviours of pipelines with random defects. The failure mechanism and reliability analysis were performed. The pbs for random defects exhibited significant variabilities, of which the probability distribution patterns altered with different corrosion depths attributed to multiple failure modes. The main value of pb for random defects was lower than that of uniform defects, especially for deeper corrosion and interacting defects. At maximum, this reduction attained over 28%; The spatial variation gradient of remaining wall thickness dominated failure developments, based on which three failure modes were revealed; The wall thickness, corrosion degree and interacting effects were influential on failure probabilities Pfs. An average failure risk of Pf over 0.6 would be suffered for employing traditional methodologies. Polynomial chaos expansion models were fitted to estimate pb from corrosion morphologies, showing reasonable accuracies. Besides, the performance variations with different cases were discussed.

Experimental study on jet fire characteristics of hydrogen-blended natural gas

Blending hydrogen into natural gas can increase fuel reactivity and the risk of jet fires in case of pipeline leakage though it is an effective delivery method. In this work, horizontal jet fires of hydrogen-blended natural gas at various operating pressures and hydrogen content were investigated experimentally. The flame temperature, lift-off distance, and flame length were measured for the scenarios with 0%–50% hydrogen content (in volume fraction) and 200–800 Pa. Results show that with the increase of hydrogen content, the flame temperature rises while the lift-off distance and flame length decrease. The flame length is slightly affected when the hydrogen content is less than 10% and a maximum reduction in flame length is 13.7% as the blended hydrogen content increases to 50%. Further theoretical analysis suggested the dimensionless correlations of the temperature distribution along the jet axis, lift-off distance, and flame length with different hydrogen content, respectively. The research results may provide reference for the risk assessment of hydrogen-blended natural gas pipelines.

CO2 pipelines release and dispersion: A review

Carbon Capture, Utilization, and Storage (CCUS) is an emerging industry in response to climate change. With this, more pipelines are being built to transport CO2 to sequestration sites. CO2, though non-toxic and non-flammable, is an asphyxiant that can cause death when exposed to high concentrations. Hence, there is a need to evaluate the release and dispersion behavior of CO2 from pipelines. Industrial-scale experiments of CO2 dispersion have been performed to acquire quantitative data. However, the sheer number of scenarios that could possibly occur is difficult to perform one-by-one experimentally. Computational tools, such as Computational Fluid Dynamics (CFD), and risk assessment methodologies, such as Quantitative Risk Assessment (QRA), have then been utilized to predict CO2 dispersion behavior and hazards, respectively. In this review, the different industrial-scale experiments, CFD models for CO2 dispersion, and risk assessment of CO2 transport pipelines were discussed. Moreover, we emphasize the possibility of using machine learning as a tool to increase the accuracy and efficiency in predicting CO2 dispersion behavior. The future direction of predicting CO2 dispersion behavior through the use of machine learning was also explored.

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning

Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions. The danger of leaks and accidents increases as a result of these damages. Pipelines must be evaluated on a regular basis to make sure they are fit for transmission. By evaluating the effects of damages and the possibility of catastrophic failures using a variety of techniques, pipeline integrity is controlled. Applying the relative risk scoring (RRS) technique, pipeline failures are predicted. One of the probabilistic techniques used to forecast risk based on an impartial assessment is machine learning. With different parameters like corrosion, leakage, materials, atmosphere, surface, earth-movements, above-ground and underground facilities, etc., the RRS method provides an accuracy of 97.5% in identifying the risk and gives a precise classification of risk, whether the pipeline has a high, medium, or low risk without any delay on the prediction compared with Naive Bayes, decision tree, support vector machine, and graph convolutional network.

A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines

Preparation of pipeline risk zoning is essential for pipeline construction and safe operation. Landslides are one of the main sources of risk to the safe operations of oil and gas pipelines in mountainous areas. This work aims to propose a quantitative assessment model of landslide-induced long-distance pipeline risk by analyzing historical landslide hazard data along oil and gas pipelines. Using the Changshou-Fuling-Wulong-Nanchuan (CN) gas pipeline dataset, two independent assessments were carried out: landslide susceptibility assessment and pipeline vulnerability assessment. Firstly, the study combined the recursive feature elimination and particle swarm optimization-AdaBoost method (RFE-PSO-AdaBoost) to develop a landslide susceptibility mapping model. The RFE method was used to select the conditioning factors, while PSO was used to tune the hyper-parameters. Secondly, considering the angular relationship between the pipelines and landslides, and the segmentation of the pipelines using the fuzzy clustering (FC), the CRITIC method (FC-CRITIC) was combined to develop a pipeline vulnerability assessment model. Accordingly, a pipeline risk map was obtained based on pipeline vulnerability and landslide susceptibility assessment. The study results show that almost 35.3% of the slope units were in extremely high susceptibility zones, 6.68% of the pipelines were in extremely high vulnerability areas, the southern and eastern pipelines segmented in the study area were located in high risk areas and coincided well with the distribution of landslides. The proposed hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines can provide a scientific and reasonable risk classification for new planning or in service pipelines to avoid landslide-oriented risk and ensure their safe operation in mountainous areas.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses, a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents. The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering. Current oil pipeline leakage signals are insufficient for feature extraction, while the training time for traditional leakage prediction models is too long. A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt (GA-LM) classification model for predicting the leakage status of oil pipelines. The signal that has been processed is transformed to the time and frequency domain, allowing full expression of the original signal. The traditional Back Propagation (BP) neural network is optimized by the Genetic Algorithm (GA) and Levenberg Marquardt (LM) algorithms. The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter. The Accuracy, Precision, Recall, and F1score of the GA-LM model is 95%, 93.5%, 96.7%, and 95.1%, respectively, which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples. The proposed GA-LM model can obviously reduce training time and improve recognition efficiency. In addition, considering that a large number of samples are required for model training, a wavelet threshold method is proposed to generate sample data with higher reliability. The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

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

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

Rapid on-site detection of underground petroleum pipeline leaks and risk assessment using portable gas chromatography-mass spectrometry and solid phase microextraction

Locating underground pipeline leaks can be challenging due to their hidden nature and variable terrain conditions. To sample soil gas, solid-phase microextraction (SPME) was employed, and a portable gas chromatography/mass spectrometry (GC/MS) was used to detect the presence and concentrations of petroleum hydrocarbon volatile organic compounds (pH-VOCs), including benzene, toluene, ethylbenzene, and xylene (BTEX). We optimized the extraction method through benchtop studies using SPME. The appropriate fibre materials and exposure time were selected for each BTEX compound. Before applying SPME, we preconditioned the soil vapour samples by keeping the temperature at around 4 °C and using ethanol as a desorbing agent and moisture filters to minimize the impact of moisture. To conduct this optimisation, airbags were applied to condition the soil vapour samples and SPME sampling. By conditioning the samples using this method, we were able to improve analytical efficiency and accuracy while minimizing environmental impacts, resulting in more reliable research data in the field. The study employed portable GC/MS data to assess the concentration distribution of BTEX in soil vapour samples obtained from 1.5 m below the ground surface at 10 subsurface vapour monitoring locations at the leak site. After optimization, the detection limits of BTEX were almost 100 µg/m3, and the measurement repeatabilities were approximately 5% and 15% for BTEX standards in the laboratory and soil vapour samples in the field, respectively. The soil vapour samples showed a hotspot region with high BTEX concentrations, reaching 30 mg/m3, indicating a diesel return pipeline leak caused by a gasket failure in a flange. The prompt detection of the leak source was critical in minimizing environmental impact and worker safety hazards.

A New Model of Oil Pipeline (Oleduct) Risk Assessment

A New Model of Oil Pipeline (Oleduct) Risk Assessment