Oil Pipeline Accidents Research Articles

Co-occurrence of polycyclic aromatic hydrocarbons and heavy metals in various environmental matrices of a chronic petroleum polluted region in Iran; Pollution characterization, and assessment of ecological and human health risks

Oil spills from pipeline accidents can result in long-lasting health effect in the people living in a ‎polluted region‎. In this study, the level of the 16 US EPA priority polycyclic aromatic hydrocarbons (PAHs‎) and heavy metals (HMs) have been ‎analyzed in environmental matrices of a region with frequent oil ‎pipeline accidents in Iran‎.‎ The results showed that the mean ‎concentration of ΣPAHs and ΣHMs decreased from the upstream to the downstream and also the levels were ‎higher in the wet season than those in the dry season. The average concentration of HMs in sediments was higher than that in other ‎environments. The 3-ring and 4-ring PAHs were dominant in all of the studied matrices with the average values of 32.61 % and 45.85 %, respectively. The ecological risks of PAHs and HMs were medium and high in all matrices, respectively. In wet season, the total cancer risk (TCR) related to PAHs in agricultural soil was greater than 10−4, whereas it’s very close to the threshold for HMs in water. This study offers a reference for assessing the long-term impact of oil spills in contaminated environmental matrices. The results are crucial for developing effective strategies to mitigate oil pollution ‎impacts and protect environmental and public health.

Interpretable machine learning models for failure cause prediction in imbalanced oil pipeline data

Pipelines are critical arteries in the oil and gas industry and require massive capital investment to safely construct networks that transport hydrocarbons across diverse environments. However, these pipeline systems are prone to integrity failure, which results in significant economic losses and environmental damage. Accurate prediction of pipeline failure events using historical oil pipeline accident data enables asset managers to plan sufficient maintenance, rehabilitation, and repair activities to prevent catastrophic failures. However, learning the complex interdependencies between pipeline attributes and rare failure events presents several analytical challenges. This study proposes a novel machine learning (ML) framework to accurately predict pipeline failure causes on highly class-imbalanced data compiled by the United States Pipeline and Hazardous Materials Safety Administration. Natural language processing techniques were leveraged to extract informative features from unstructured text data. Furthermore, class imbalance in the dataset was addressed via oversampling and intrinsic cost-sensitive learning (CSL) strategies adapted for the multi-class case. Nine machine and deep learning architectures were benchmarked, with LightGBM demonstrating superior performance. The integration of CSL yielded an 86% F1 score and a 0.82 Cohen kappa score, significantly advancing prior research. This study leveraged a comprehensive Shapley Additive explanation analysis to interpret the predictions from the LightGBM algorithm, revealing the key factors driving failure probabilities. Leveraging sentiment analysis allowed the models to capture a richer, more multifaceted representation of the textual data. This study developed a novel CSL approach that integrates domain knowledge regarding the varying cost impacts of misclassifying different failure types into ML models. This research demonstrated an effective fusion of text insights from inspection reports with structured pipeline data that enhances model interpretability. The resulting AI modeling framework generated data-driven predictions of the causes of failure that could enable transportation agencies with actionable insights. These insights enable tailored preventative maintenance decisions to proactively mitigate emerging pipeline failures.

The key role of risk perception in preparedness for oil pipeline accidents in urban areas: A sequential mediation analysis

Effective disaster risk reduction requires conducting research within communities that gave recognized natural or technological risks, to foster the development of more resilient societies. However, limited information is available regarding risk perception and preparedness for technological hazards in disaster-prone urban areas of South America. The aim of this study is to investigate the factors that influence public risk perception regarding the Trans-Ecuadorian Oil Pipeline System and its relationship with household preparedness for a potential accident in the Metropolitan District of Quito (Ecuador). We assess the link among knowledge, trust in authorities, risk perception, intention to prepare, and preparedness. Results from a sequential mediation analysis reveal that risk perception partially and positively mediates the relationship between knowledge and preparedness and acts as a negative full mediator between the latter and trust. These findings provide valuable information for future risk governance and communication strategies, aimed at enhancing risk perception and improving individual preparedness of individuals, as well as risk mitigation procedures.

Greenhouse gas emissions from U.S. crude oil pipeline accidents: 1968 to 2020

Crude oil pipelines are considered as the lifelines of energy industry. However, accidents of the pipelines can lead to severe public health and environmental concerns, in which greenhouse gas (GHG) emissions, primarily methane, are frequently overlooked. While previous studies examined fugitive emissions in normal operation of crude oil pipelines, emissions resulting from accidents were typically managed separately and were therefore not included in the emission account of oil systems. To bridge this knowledge gap, we employed a bottom-up approach to conducted the first-ever inventory of GHG emissions resulting from crude oil pipeline accidents in the United States at the state level from 1968 to 2020, and leveraged Monte Carlo simulation to estimate the associated uncertainties. Our results reveal that GHG emissions from accidents in gathering pipelines (~720,000 tCO2e) exceed those from transmission pipelines (~290,000 tCO2e), although significantly more accidents have occurred in transmission pipelines (6883 cases) than gathering pipelines (773 cases). Texas accounted for over 40% of total accident-related GHG emissions nationwide. Our study contributes to enhanced accuracy of the GHG account associated with crude oil transport and implementing the data-driven climate mitigation strategies.

Environmental risk of oil pipeline accidents

Oil spills from pipeline accidents, caused by either material degradation or improper operation, can result in long-lasting environmental damage to soil and water. Assessing the potential environmental risks of these accidents is crucial for effective pipeline integrity management. This study calculates the accident rate using Pipeline and Hazardous Materials Safety Administration (PHMSA) data and estimates the environmental risk of pipeline accidents by factoring in the cost of environmental remediation. Results show that crude oil pipelines in Michigan pose the highest environmental risk, while Texas has the highest environmental risk for product oil pipelines. On average, crude oil pipelines have a higher environmental risk (56,533.6 US dollars·times·mile−1·year−1) compared to product oil pipelines (13,395.6 US dollars·times·mile−1·year−1). Factors affecting pipeline integrity management are also analyzed, including diameter, diameter-thickness ratio, and design pressure. The study finds that larger pipelines with higher pressures receive more attention during maintenance and thus pose a lower environmental risk. Furthermore, underground pipelines pose a much higher environmental risk than pipelines in other environments, and pipelines are more vulnerable in the early and mid-stages of operation. The leading causes of environmental risk in pipeline accidents are material failure, corrosion, and equipment failure. By comparing environmental risks, managers can better understand the strengths and weaknesses of their integrity management efforts.

Chemometric techniques in oil spill identification: A case study in Dalian 7.16 oil spill accident of China

Chemometric methods have unique advantages regarding comprehensive consideration of multiple parameters and the classification of samples or variables. Classification of oil spill sources was carried out by using chemometric techniques, such as Repeatability Limit, hierarchical cluster analysis (HCA), Student's t-test and Principal component analysis (PCA) Biplot. In addition, this paper takes the fingerprint identification of a Dalian “7.16″ oil spill accident as an example to illustrate the effectiveness of chemometric techniques in oil identification. PCA scores plot (explaining 82.77% of variance accounted for three PCs) showed that samples belong to four clusters and result of HCA method further confirmed that. The residual oil in Jinshatan Beach and Haibei Square may be caused by the explosion of Dalian “7–16” oil pipeline accident. The use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution. The results will be of great significance to improve the accuracy and efficiency of oil spill identification based on oil fingerprint.

A Risk Treatment Strategy Model for Oil Pipeline Accidents Based on a Bayesian Decision Network Model.

Risk treatment is an effective way to reduce the risk of oil pipeline accidents. Many risk analysis and treatment strategies and models have been established based on the event tree method, bow-tie method, Bayesian network method, and other methods. Considering the characteristics of the current models, a risk treatment strategy model for oil pipeline accidents based on Bayesian decision network (BDNs) is proposed in this paper. First, the quantitative analysis method used in the Event-Evolution-Bayesian model (EEB model) is used for risk analysis. Second, the consequence weights and initial event likelihoods are added to the risk analysis model, and the integrated risk is obtained. Third, the risk treatment strategy model is established to achieve acceptable risk with optimal resources. The risk treatment options are added to the Bayesian network (BN) risk analysis model as the decision nodes and utility nodes. In this approach, the BN risk analysis model can be transformed into a risk treatment model based on BDNs. Compared to other models, this model can not only identify the risk factors comprehensively and illustrate the incident evolution process clearly, but also can support diverse risk treatment strategies for specific cases, such as to reduce the integrated risk to meet acceptable criterion or to balance the benefit and cost of an initiative. Furthermore, the risk treatment strategy can be updated as the risk context changes.

Cask Principle of Multi-Attribute Risk Assessment: Non-Weighted Maximal Approach for Production Accidents

This paper proposes a non-weighted maximal approach of multi-attribute risk assessment for production accidents, which comes from the Chinese practice of risk management rather than the theoretical weighted multi-attribute approach. The existing literature for risk assessment of pipeline accidents, there is an absence of or lack of explicit consideration of some special dimensions, i.e., environmental pollution as the important derivative disaster. The non-weighted maximal approach is described the maximum function among multiple criteria, which include fatalities, serious injuries, direct economic loss, and environment pollutions. The approach comes from the Chinese government official achievement assessment system with the characteristics of “one ticket veto system for production safety”, and has applied to ex ante assessing likelihood of the accident, and ex post holding the responsible for accidents. At last, applying the case of the Chinese Qingdao oil pipeline accident, the maximal approach is compared with the FN curve criterion, the ALARP principle and the ELECTRE TRI method. The results show that the maximal approach of production safety accident criterion pays more attention to the risk density or risk consequences, which follows the “cask principle” and is much more useful controlling the risk when targeting the vulnerable links of engineering systems.

Environmental consequence analysis of oil spills from onshore pipelines with parametric uncertainty

Due to spatial heterogeneities, measurement errors and inherent errors of parameter estimation, hydrogeology parameters are not exactly known. Therefore, the accuracy of environmental consequence prediction for oil spills from onshore pipeline accidents is subject to the uncertainty of soil parameters. However, due to the time-consuming calculation of uncertainty analysis and the difficulty of modeling the cross-correlation among soil parameters, the correlativity is often overlooked in the current researches involving the impacts of uncertain soil parameters on the environmental consequence estimation. To address above concerns, this study introduces a novel methodological framework for investigating the contribution of correlated soil parameters on the environmental consequence caused by spilled oil from onshore oil pipeline accidents. The proposed methodological framework combines a polynomial chaos expansion(PCE) surrogate model and methods from Uncertainty Analysis (UA) and PAWN-based Global Sensitivity Analysis (GSA). In addition, the practical application of this framework is illustrated with a case study for estimating the contaminated range of spilled oil from onshore pipeline accidents.First of all, the correlation of soil parameters is modeled by the copula function, and a PCE surrogate model has been constructed in order to alleviate the computational burden of the following UA and PAWN-based GSA. Then, UA is performed to quantify the impacts of correlativity by means of Monte Carlo (MC) method. Results reveal the correlativity of soil parameters introduces a substantial variation in the estimation of contaminated depth of spilled oil. In addition, the UA results show that selecting different copula functions to describe the correlation of soil parameters may lead to different variations of contaminated depth. Moreover, the uncertainty of the calculation results has been reduced with the consideration of correlated soil parameters, which may make the calculation results more reliable. Finally, the PAWN-based GSA is performed to identify the most influential parameter on the variation of contaminated depth. Results of the GSA show that saturated permeability and scale parameter α are primarily responsible for the variability of the model output, while the porosity exerts a smaller influence. This finding also suggests the possibility of replacing the less influential parameters with its average value, thus appreciably reducing the computation cost of the problem. The results of GSA not only offer a better understanding of correlativity to the contaminated range of spilled oil, but also identify the parameters for which additional effort needs to be invested to reduce their uncertainty and, as a result, the uncertainty associated with environmental consequence estimation of onshore pipeline accidents.

Ecological condition of the small north river after emergency release of oil products

The features of the chemical compositions of the surface water, bottom sediments, and zoobenthos communities of the small forest water way after an oil pipeline accident were investigated herein. Nine months after the oil spillage, an increased content of oil hydrocarbons (2.8 MAC) remained in the surface water downstream the place of the emergency release of oil-containing liquid. The detected high concentrations of iron ions (1.1–4.6 MAC) and copper (1.2–2.6 MAC) and the decomposition-resistant organic compounds (1.2–3.3 MAC) were typical for the background area, which certified their almost natural origin. The oil hydrocarbon content in the bottom sediments below the emergency release of oil in the water way exceeded the backgrounds one in 7–214 times. Zoobenthos is presented by 18 groups in the water ways of the region under consideration, out of which, 14 groups were detected in the contamination area. The modification of the zoobenthos structure and the reduction of its quantitative indicators of development were observed in the direction from the Vozeyshor creek. The dominance of entomostracans and oligochaete was detected in the bottom communities of the oil-contaminated areas.

A probabilistic analysis model of oil pipeline accidents based on an integrated Event-Evolution-Bayesian (EEB) model

Abstract Over the past several decades, the high frequency of oil pipeline accidents has drawn substantial attention around the world. Many oil pipeline accident analysis models have been established based on the event tree method, the Bayesian network method and Computational Fluid Dynamics (CFD) simulation models. Considering the disadvantages of current models for comprehensively representing the incident evolution process and quantitative analysis for consequences, this paper proposes a probabilistic analysis model for oil pipeline accidents that integrates three methods the event tree (E), the incident evolution diagram (E) and the Bayesian network (B). Therefore, the model is called the “EEB model”. The EEB model can identify the initial event and secondary events, illustrate the accident evolution path, identify the key influencing factors, analyze their effects, and calculate the probabilities of different consequences of oil pipeline network accidents. Compared with other models, the EEB model considers more factors, such as key environmental conditions and the emergency response. Probabilistic analysis of different consequences, including casualties, economic losses, environmental pollution and the influence on social order, can be obtained. For a general scenario of an oil pipeline network accident, the probabilities for different consequences are 71.3% for “less than 5 persons affected”, 68.2% for “less than 10 million RMB lost”, 50.4% for “less than 1 km2 of water pollution” and 59.5% for “influence on social order of less than 100 persons”. The risk for the accident can be estimated by assuming the probability of the initial event as P. The model also denotes the emergency targets to be achieved and the response missions to be executed. Based on this information, a response plan can be developed for decision making. Since the incident evolution process is complex, the effects of the influencing factors should be analyzed. The EEB model highlights the significant influences of the water area (e.g., the probability of “10–50 km2 of water pollution” decreases from 38.7% for “near and large” water bodies to 17.4% for “far and small” water bodies) and the emergency response (e.g., the probability of “50–100 million RMB economic loss” increases from 11.5% for an “effective” response to 29.3% for a “poor” response). The probabilistic analysis obtained by the EEB is more comprehensive than those of other models, and the results can be used for risk analysis, decision making and effect analysis of oil pipeline networks.

Quantitative analysis of factors that affect oil pipeline network accident based on Bayesian networks: A case study in China

Some factors can affect the consequences of oil pipeline accident and their effects should be analyzed to improve emergency preparation and emergency response. Although there are some qualitative analysis models of risk factors’ effects, the quantitative analysis model still should be researched. In this study, we introduce a Bayesian network (BN) model of risk factors’ effects analysis in an oil pipeline accident case that happened in China. The incident evolution diagram is built to identify the risk factors. And the BN model is built based on the deployment rule for factor nodes in BN and the expert knowledge by Dempster-Shafer evidence theory. Then the probabilities of incident consequences and risk factors’ effects can be calculated. The most likely consequences given by this model are consilient with the case. Meanwhile, the quantitative estimations of risk factors’ effects may provide a theoretical basis to take optimal risk treatment measures for oil pipeline management, which can be used in emergency preparation and emergency response.

Risk Assessment of Oil Pipeline Accidents in Special Climatic Conditions

The present study identifies the main accidents’ factors and causes for oil pipeline located in Siberia and operated in special climatic conditions. Various types of pipeline accident scenarios were modeled. It is showed that the most dangerous scenarios are oil spills fire and oil vapor explosion due to the loss of piping integrity (rupture) of the pipeline’s section, laying on marshlands and oil spill on the water surface due to the loss of piping integrity (puncture). The most probable scenario is oil spills fire due to the loss of piping integrity (puncture) of the pipeline’s section, laying on dry lands and marshlands. To estimate the scenarios «event tree analysis» is used. Also such risk indexes as individual, societal, public and potential risks were determined.

Complacency and Automation Bias in the Enbridge Pipeline Disaster

Automation bias was likely the main contributing factor in the Enbridge pipeline disaster that occurred on July 26, 2010, when large amounts of crude oil were released into the Kalamazoo River and Talmadge Creek. An examination of the Enbridge oil pipeline accident suggests that complacency and automation bias played leading roles but were overlooked by regulators. Moreover, we believe that because the National Transportation Safety Board overlooked existing research on automation bias, its recommendations are flawed and could exacerbate, rather than alleviate, the problem. Industry, policy makers, and regulators need to consider automation bias when developing systems to reduce the likelihood of complacency errors.

Experimental study on the probe dynamic behaviour of feeler pigs in detecting internal corrosion in oil and gas pipelines

Leakages are the major cause of in-service natural gas and oil pipeline accidents, and many factors, such as corrosion, can lead to leakages. The feeler pig is one of the most typical contact-testing tools in oil and gas pipelines. In this paper, the probe dynamic behaviour of the feeler pig in detecting internal corrosion has been investigated using a handmade inspecting system. The dynamic characteristics of the probe are different in the uphill section and the downhill section, and the trajectory of the probe is asymmetric. A bouncing phenomenon was found in the outlet region of the corrosion. The experimental results indicated that both the speed and the spring pre-tightening elastic force are closely related to the inspection precision. The research in this paper provides guidance for studying the inspection precision of the feeler pig.