Pipeline Risk Research Articles

Test Analysis and Pipeline Risk Assessment of Oil and Gas Pipeline Laying in Typical Loess Area

In recent years, with the development of oil and gas storage and transportation technology, pipelines have been successfully laid and safely operated in areas with various geological and geographical characteristics. However, for pipelines in particular sections such as collapsible loess areas and landslide ones, oil and gas pipelines will be restricted by conditions. Different problems may even occur during laying and operation. On the basis of mechanical analysis and calculation, this paper establishes a mechanical model. Combined with similar simulation experiments, the influence of water flow erosion on oil and gas pipelines under different slope laying conditions is analyzed. At the same time, the risk assessment methods of special pipe sections in loess areas are established, which have certain guiding significance and reference value for pipeline laying in loess areas.

Deeppipe: A semi-supervised learning for operating condition recognition of multi-product pipelines

Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure pipeline safe, reducing potential risk. However, the operating conditions of the multi-product pipeline change frequently, and the recognition and monitoring by on-site personnel are easy to cause misjudgment, so the operating conditions of the pipeline cannot be accurately recognized. Noticeably, operating condition recognition is an important part of pipeline safety and risk management. Although ample operating data are stored in SCADA system, these data are lack of corresponding condition labels, making it hard to be mined. In this work, a semi-supervised learning for operating condition recognition is proposed to overcome aforementioned issues. Firstly, the operating parameters of each station are preprocessed and collected to construct into data matrices to overcome transient disturbance considering the pipeline space characteristics and time series of the operating data. Then stacked autoencoder (SAE) is used to pre-train the network parameters of multi-layer neural network (MLNN) based on a large amount of unlabeled operating data. After that, MLNN is fine-tuned based on a small amount of labeled data annotated by referring to the operation log. To verify the effectiveness of the semi-supervised learning, a real multi-product pipeline is taken as an example for operating condition recognition. The accuracy, precision, recall and F1 score is 95 %, 95 %, 80 % and 80 %, respectively. Results show that the condition recognition accuracy of the proposed model is better than other machine learning models. Finally, the sensitivity analysis is conducted to illustrate the importance of SAE in this classification model.

CO2 Pipeline Risk Assessment for a Regional-Scale Pipeline in the Midcontinental United States

A comprehensive quantitative risk assessment for the construction and operation of a CO2 transportation network was considered for the Midcontinent of the United States. The assessment evaluated the risk associated with construction and operation using data from the U.S. Occupational Safety and Health Administration to determine the risk of injury or death for pipeline workers and data from the U.S. 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 a 344-mile-long pipeline was found. The average construction and operational risk for the CO2 pipeline was $6,116,948. The average risks for natural gas distribution, natural gas transmission/gathering, and non-CO2 hazardous liquid pipelines with the same configuration were $6,264,868, $11,971,828, and $28,212,756, respectively. The results showed that the overall and operational risks for CO2 pipelines was less than that of other types of pipelines.

Quantitative evaluation of explosion consequences in urban underground drainage

The leaked gas from urban pipelines can easily spread to adjacent underground spaces and cause dangerous explosion accidents, therefore it is crucial to accurately identify underground spaces with high explosion consequences quantitatively, which is the key link to carry out the underground space explosion risk assessment. This paper proposes a method to quantitatively estimate the consequences of the urban underground drainage explosions. The method is established based on a large-scale underground explosion experiment and consists of several damage indicators as well as correction factors. The effects of fragments after the gas explosion, overpressure, and ground vibration post-explosion on nearby residents, infrastructures, and buried pipelines are quantitatively investigated through experimental results and theoretical analysis. The social impact and rescue force distribution affected by the explosion are assessed. A case study was generated in urban area which showed that this method is practicable. The results presented here may provide sound theoretical guidance for urban pipeline risk assessment and explosion hazard control.

Prediction and management of hydrate reformation risk in pipelines during offshore gas hydrate development by depressurization

The hydrate reformation in marine gas hydrate production system can significantly affect the accuracy control of the production pressure differential and even cause pipeline blockage problems. In this paper, a transient temperature and pressure calculating model was established to predict the risk of hydrate reformation in production pipelines during offshore gas hydrate development, which considering the coupling interactions between the real-time drainage of electric submersible pump (ESP), the downhole preheating of electric induction heater (EIH), the wellbore multiphase flow and heat transfer. Using the model, the dynamic evolution laws of gas–liquid two-phase flow behaviors in different pipelines of mixed transportation, drainage and gas production were studied. Furthermore, the region of hydrate reformation in production pipelines was predicted quantitatively. The results indicate the risk of hydrate reformation in the drainage pipe is the highest relative to other pipes, and the main flow pattern in this pipe is bubbly flow. Besides, the conventional hydrate management method through inhibitor injection was applied to prevent the hydrate reformation. It can be found that the required Ethylene glycol (MEG) minimum concentration reaches 22% in order to completely eliminate the risk of hydrate reformation. To this end, a new hydrate management strategy by adding additional pumps and heaters was proposed, and the prevention effect of hydrate reformation in different scenarios was discussed in detail. The simulated results demonstrate that this method can effectively address the potential hydrate reformation risks without the use of inhibitors. This work will provide theoretical references for the hydrate flow assurance operations in marine gas hydrate production testing.

Individual risk quantification model of pressure pipeline based on fuzzy evaluation method with catastrophe progression

The safety of pressure pipelines has become an important part of national public safety because of their special professional technology and potentially high risk. To identify the key influencing factors of individual risk of pressure pipeline more effectively and explain the interaction mechanism of each factor clearly, using the factor analysis method and catastrophe progression method, the fuzzy comprehensive evaluation mathematical model of the individual risk of the pressure pipeline was established, Meanwhile, the risk factors of the risk subject and the risk receptor were identified respectively based on the PSR model and the vulnerability model, and the acceptance criterion of the individual risk of the pressure pipeline was studied. In addition, the 185 km Jinjing pipeline of China Aviation Oil Group was selected as a case study to obtain the comprehensive evaluation results of the risk subject, object, and individual risk of Jinjing pipeline. Finally, the acceptance criteria and control measures were given. Results show that the individual risk fuzzy comprehensive evaluation model of the pressure pipeline is reasonable and applicable, and the acceptance criteria of the individual risk of the pressure pipeline are formulated. The conclusion provides a method for the government regulators to implement the risk-based supervision of the pressure pipeline.

Dynamic risk assessment model of buried gas pipelines based on system dynamics

The risks associated with buried gas pipeline are dynamic because of the variable operating environment as well as numerous accident-causing factors. However, most risk analysis studies provide a static overview of the system. This paper proposes a dynamic risk assessment model based on system dynamics (SD) to deal with both the complexity of a given system and changes therein with time, because SD offers unique advantages in revealing the dynamic characteristics of system behavior. For corrosion failure, which is closely related to time, our preliminary results are used to calculate the dynamic failure probability. For the time-independent failure causes, a failure probability calculation model based on the modification factors is proposed. Then, the accident consequences are analyzed according to the evolution process of gas accidents. The SD model for the risk assessment of gas pipelines is constructed by considering failure probability and accident consequences. The failure probability, accident consequences, and individual risk are simulated by considering a natural gas pipeline in Zhuhai, China, as an example. The results show that the dynamic development laws of buried gas pipeline risk are consistent with the actual situation and the proposed model can effectively characterize the temporal and spatial laws of risk evolution.

Study on the natural gas diffusion behavior in sewage pipeline by a new outdoor full-scale water cycling experimental pipeline system

To initiate the experimental study of natural gas diffusion in sewage pipeline and evaluate the sewage pipeline risk, a new outdoor full-scale water cycling experimental pipeline system was built. The concentration distributions and characteristic time were measured in a comprehensive monitoring system. As the rate of the gas leakage flow decreases, the alarm time increases significantly. It is found that the influence of water flow direction on methane gas diffusion is different under varying gas flow rates. Furthermore, the methane diffusion rate in sewage pipeline is also a function of water flow rate and water level height. Based on the experimental results, the corresponding suggestions for prevention and control of gas leakage accidents in sewage pipelines were put forward to improve the urban public security management system and the security capacity of urban public security.

Failure risk analysis of pipelines using data-driven machine learning algorithms

Failure risk analysis of pipeline networks is essential for their effective management. Since pipeline networks are often large and complex, analyzing a large number of pipelines is often intensive and time-consuming. This study utilizes recent advances in machine learning to develop a viable alternative to computationally intensive analytical approaches to determine the failure risk of steel oil and gas pipelines. Burst failure risk of pipelines under active corrosion defects is evaluated considering the remaining strength of pipelines with corrosion pit. In the first part of the study, a comprehensive database of pipelines is generated based on information extracted from literature, and true failure pressure is predicted considering variability between predicted burst failure pressure and experimental burst test results. True burst failure pressure is estimated using various design codes in a probabilistic manner. Among the various burst failure prediction models, the DNV RP-F101 model provides the lowest variability in the failure pressure prediction. Therefore, the model is used to generate the probability of failure of pipelines at a burst limit state. Pipelines are classified based on their probability of failures, from low to severe failure risk groups. Then, eight machine learning algorithms are evaluated based on the generated dataset to identify the best failure prediction model. The performance of machine learning algorithms is evaluated on the basis of a confusion matrix and computational efficiency. It shows that XGBoost is the optimal algorithm in predicting the failure and is recommended for future analysis. The computational efficiency of the machine learning algorithm is also compared to the physics-based model. It is revealed that machine learning algorithms can perform a failure risk analysis of pipelines with greater computational efficiency than the physics-based approach. Even the slowest machine learning algorithm is about 12 times faster than the physics-based model.

Critical aspects of natural gas pipelines risk assessments. A case-study application on buried layout

The safety aspects of pipelines conveying hazardous materials are included neither under the umbrella of Seveso Directives aiming at preventing major accidents at industrial facilities, nor in other EU legislations, such as the Pressure Equipment Directive (PED). Starting from evidence that in the last decades the international natural gas market has been growing at a very high rate and continues to exhibit an increasing trend, in this paper we focus on consequences deriving from accidents on high pressure buried Natural Gas Pipelines (NGP) and related probabilities of the various outcomes. A survey on historical accidents occurred on NG pipelines in the USA, Canada and EU allowed the attainment of significant statistics concerning the main factors responsible for the accident evolution, namely failure mode, immediate and root cause, evolving scenario, degree of confinement produced by the surroundings and ignition timing. In this paper, we focus on a refined Event Tree framework, to overcome the limitations of the amply applied over-conservative IP UKOOA approach. In order to evidence the capability of the approach, the use of refined PET is exemplified by means of a real case-study of a high pressure buried NG pipeline, contrasting the actual results with those obtained by conventional methods, in terms of evolving scenario probability and damage. Conclusions are drawn about the effective application of the framework within risk assessment and the uncertainties and sensitivities in the pipeline accident modelling.

Effect of terrain, environment and infrastructure on potential CO2 pipeline corridors: a case study from North-Central USA

In this paper, a study has been undertaken with the objective to delineate the potential CO2 pipeline corridors through the north-central region of the USA including North Dakota, Montana, Wyoming, Utah and Colorado to enable the implementation of carbon capture storage and utilization projects. A combination of GIS along with analytical hierarchy process is used to identify regions with high potential for pipeline development. A total of 19 thematic information layers have been utilized to map the study area which reflect the effect of ecology, environment and existent infrastructure towards laying new CO2 pipeline networks. Weights are assigned to each class in the thematic maps based on their characteristics, capacity of building and maintaining pipelines and the potential environmental risk of CO2 pipelines from the literature. A tract suitability index is developed which identified Western North Dakota, central Wyoming and Western Montana as regions highly suitable for development of CO2 pipelines. The study reveals that 54.5% of the region of interest is suitable for pipeline construction while 15.39% of land is classified as either poor or infeasible candidate for locating CO2 pipelines. It is also revealed that pipeline right-of-way, water bodies and population centres have the most significant impact on future pipeline development. The results of the study are also varied to show the effect different factors could have on the potential CO2 corridors and are then correlated with existent pipeline in study area to check the validity of the resulting analysis.

Soft computing for modeling pipeline risk index under uncertainty

The risk of water pipe failure is deemed one of the significant challenges of the 21st century. The risk analysis and modeling are intricate tasks due to the complexity of the buried piping system, which is nonlinear, dynamic, and includes vast arrays of indicators that cannot be measured meticulously in any conventional metrics. Deterioration indicators extracted from field inspections and/or experts' questionnaires have inherently certain degrees of uncertainty and subjective judgments. Soft computing techniques are capable of coping with the imprecision, uncertainty, and fuzziness of data. The objective of this research is to develop a soft computing approach grounded in a fuzzy inference system (FIS) to enable encoding the deterioration indicators into a risk index while dealing with the ambiguity and imprecision. The approach involves five phases that are based on data from Arequipa City in Peru, simulation and FIS, and supported by 3D schematic representations. Data are channeled to the FIS engine after defining the membership functions and 127 fuzzy rules. Subsequent to successive simulation iterations, aggregation, and defuzzification of the outputs, the indices for risk-of-failure are generated along with 3D visualization models. To ensure the coherency of the proposed model, Monte Carlo simulation is performed in conjunction with sensitivity analysis on the data. The validation results of a sample revealed the efficacy of the model with the Coefficient of Variation and the Mean Standard Error of 0.0296 and 0.03, respectively. The five phases are chained together to enable the model to fulfill its functions.

Pipeline transportation of crude oil in Canada: Environmental risk assessment using modified diffusion models

Pipeline transportation of heavy oil poses significant risk to the environment and the human population. Though remarkable progress has been made in developing both qualitative and quantitative techniques for assessing pipeline risk, the potential threat to the bodies of water has not been well studied in the literature. In this study, we focus on three bodies of water, that is, river, lake, and aquifers, and propose water diffusion model for each, which are then used to evaluate the risk of the Energy East pipeline project in Canada. Each diffusion model facilitates estimating impact radius that enables determining the environmental risk, which in turn could be used as a surrogate of affected population.

Study on the Safety Risk Prevention Technology for the Urban Gas Pipeline

Along with the continuous development of our society and economy, the process of urbanization has been pushed forward. As one of the indispensable links in urbanization construction, the gas pipeline has brought great convenience to the life of urban residents, while has some potential safety hazards and brings certain risks to the life, property and safety of urban residents. The management status of the urban gas pipeline and safety risk prevention measures are mainly analyzed in this paper.

Planning, gas pipelines and community safety: What is the role for local planning authorities in managing risk in the neoliberal era?

Urban development of land in historically rural areas which contain high-pressure gas pipelines increases risks to public safety. Pipeline organisations identify local planning authorities as key stakeholders in planning to mitigate risks posed by land use changes. However, neoliberal reforms that have both privatised pipelines and centralised strategic planning decisions have reduced the decision-making power of planners at a local level. This places local planning authorities in an ambiguous position to consider and manage pipeline risk in their professional practice. The paper draws on findings from interviews with stakeholders involved with urban planning and high-pressure gas pipelines, including local planners, in two case study sites in Australia to highlight the impacts of neoliberal planning reforms on the professional practices of planners in managing risks to community safety from gas pipelines. Other researchers have argued that neoliberalism poses problems for the planning profession. Land use planning around high-pressure gas pipelines offers a valuable case study to demonstrate this. This research provides an important contribution to this existing body of research by exploring the effects of neoliberalism through investigation into the lived experiences of planners, showing that these developing trends impact representation of community interests in planning decisions and also public safety.

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.

Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy

Efficiently using multiple energy sources, including renewable energy, is a focus of applied energy research. A regional integrated energy system (RIES) involves coupling use of multiple energy sources, which can improve energy efficiency using multi-energy complementarity. However, increasingly renewable energies and multi-energy sources load access into an energy system increase the multiple uncertainties of RIES, which has considerable impact on both system planning and operation. This study proposes a multi-objective stochastic planning method that is based on the multi-dimensional correlation scenario set generation method for RIES’ expansion planning. The scenario generation method considers the characteristics, time sequence, autocorrelation, and cross-correlation of renewable energies and multi-energy loads. The pipeline risk index for energy network expansion planning is defined considering the energy pipeline’s importance. A multi-objective stochastic planning model based on chance constraints of the energy network is developed to minimize the investment cost and the energy pipeline risk. Finally, to confirm the effectiveness and the applicability of the proposed model and method, certain numerical cases at Yangzhong City, China, are simulated. Two RIES expansion planning scenarios are then compared. Moreover, the Pareto fronts of the optimized expansion planning schemes are demonstrated, providing reference for balancing the energy network planning scheme economy and the energy pipeline risk.

Impact Analysis of Inline Inspection Accuracy on Pipeline Integrity Planning

Abstract Integrity planning methods and inline inspection (ILI) tool performance have a great impact on a pipeline integrity management program. In pipeline integrity planning, risk and integrity assessments are performed to schedule integrity activities like ILI for the purpose of reducing risks and ensuring reliable and safe operations. In this paper, a method is developed for analyzing the impact of ILI tool accuracy on pipeline integrity planning, which is of great importance but has not been systematically studied before. Crack inspection and threat of fatigue cracking are used as the working case for the analysis, although the approach could potentially be used for any pipeline threat type. The Paris' law degradation model is used for the crack growth and subsequent severity and risk assessment. We investigated the impact of ILI tool accuracy on the cost rate, as well as the associated inspection intervals. The impact on long-term cost rate was also investigated considering new defect generation and continuous growth. Sensitivity analyses were performed. The optimal inspection intervals and the corresponding total cost rates with respect to different ILI tool accuracy and different input parameters were obtained and compared. The proposed method can support integrity management program planning by linking risks with integrity plan costs associated with ILI accuracy and optimal re-assessment intervals. The contributions of this paper mainly include the investigation of the problem of how ILI tool accuracy impacts integrity planning, the development of the method for analyzing pipelines with cracks, and the verification and validation with the examples.

Optimization of Building Drainage System Based on Blocking the Transmission Route of Pathogenic Bacteria

Some pathogens exist in the way of fecal oral transmission, while the risk of building drainage pipeline is easy to be ignored and hard to be eliminated. It is worth to reflect on the SARS incident in Taoda garden in Hong Kong, in case of group infection. At the same time, the transmission chain of pathogenic bacteria is analyzed, and according to the transmission chain, the idea of system optimization or solving the transmission of pathogenic bacteria is put forward.

Ranking of the Sections of East Siberia–Pacific Ocean Main Pipeline by Accident Probability Using Bow-Tie Method and Checklist Support Tool

AbstractWhen developing a methodology for assessing the risks of Russian pipelines, it is necessary to take into account the difficult conditions for the passage and functioning of fuel energy faci...