Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model

A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia (BC), Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases, damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment approach for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on the corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline's mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF have been obtained and visualized with the aid of the Geographic Information System (GIS). The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.     

基于前景-后悔理论的油气管道风险模态分析

针对现有油气管道的风险评估方法未考虑人的有限理性和后悔规避的心理行为特征,提出了一种基于前景-后悔理论的油气管道风险模态分析方法.首先,通过期望值法明确决策事物参考点,并限定决策者对各风险模态的期望取值区间,构建可能性分布函数.其次,利用决策准则值与参考点的差值确定收益或损失,进而计算价值函数矩阵.再次,基于概率-可能性相容性原理,采用比例转化法实现可能性与概率的转化,以构造权重函数矩阵,从而计算前景值矩阵.最后,以前景值中的正负理想点为参考点计算后悔理论中各风险模态的欣喜-后悔值,确定油气管道的风险评估结果.研究表明,该方法合理且有效,能够准确地对油气管道风险进行预测,为油气管道的风险评估提供了新方法.     

Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry

In this research, a framework combining lean manufacturing principles and fuzzy bow-tie analyses is used to assess process risks in chemical industry. Lean manufacturing tools and techniques are widely used for eliminating wastes in manufacturing environments. The five principles of lean (identify value, map the value stream, create flow, establish pull, and seek perfection) are utilized in the risk assessment process. Lean tools such as Fishbone Diagram, and Failure Mode and Effect Analysis (FMEA) are used for risk analysis and mitigation. Lean principles and tools are combined with bow-tie analysis for effective risk assessment process. The uncertainty inherent with the risks is handled using fuzzy logic principles. A case study from a chemical process industry is provided. Main risks and risk factors are identified and analyzed by the risk management team. Fuzzy estimates are obtained for the risk factors and bow-tie analysis is used to calculate the aggregated risk probability and impact. The risks are prioritized using risk priority matrix and mitigation strategies are selected based on FMEA. Results showed that the proposed framework can effectively improve the risk management process in the chemical industry.     

Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis

Failure of oil and gas transmission pipelines was analyzed by fault tree analysis in this paper. According to failure modes of pipeline: leakage and rupture, a fault tree of the pipeline was constructed. Fifty-five minimal cut sets of the fault tree had been achieved by qualitative analysis, while the failure probability of top event and the important analyses of basic events were evaluated by quantitative analysis. In conventional fault tree analysis, probabilities of the basic events were treated as precise values, which could not reflect real situation of system because of ambiguity and imprecision of some basic events. In order to overcome this disadvantage, a new method was proposed which combined expert elicitation with fuzzy set theories to evaluate probability of the events. As an example, failure probability of pipeline installation was assessed by using the proposed method, achieving its fuzzy failure probability of 6.4603×10⁻³. The method given in this article is effective to treat fuzzy events of FTA.     

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R²) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.     

Safety assessment of natural gas storage tank using similarity aggregation method based fuzzy fault tree analysis (SAM-FFTA) approach

Fault tree analysis (FTA) is an important method to analyze the failure causes of engineering systems and evaluate their safety and reliability. In practical application, the probabilities of bottom events in FTA are usually estimated according to the opinions of experts or engineers because it is difficult to obtain sufficient probability data of bottom events in fault tree. However, in many cases, there are many experts with different opinions or different forms of opinions. How to reasonably aggregate expert opinions is a challenge for the engineering application of fault tree method. In this study, a fuzzy fault tree analysis approach based on similarity aggregation method (SAM-FFTA) has been proposed. This method combines SAM with fuzzy set theory and can handled comprehensively diverse forms of opinions of different experts to obtain the probabilities of bottom events in fault tree. Finally, for verifying the applicability and flexibility of the proposed method, a natural gas spherical storage tank with a volume of 10,000 m³ was analyzed, and the importance of each bottom event was determined. The results show that flame, lightning spark, electrostatic spark, impact spark, mechanical breakdown and deformation/breakage have the most significant influence on the explosion of the natural gas spherical storage tank.     

基于多维云模型的埋地并行油气管道风险评价研究*

为提高风险评价准确性以有效支撑管道完整性维护,保证油气管道在多管并行敷设下安全运行,提出1种基于博弈论-多维云模型的风险评价方法。首先,从并行间距、土壤压实度、土壤导热系数、介质流速、管径、埋深、应急响应时间、上下游截断阀间距等方面建立并行管道风险评价指标体系,为降低传统赋权方法的主观性,通过结合熵权法改进层次分析法（AHP）,从而确定主客观权重,再经博弈论组合赋权法综合计算指标权重;其次,运用多维云模型理论确定风险等级,通过修正各风险评分项的等级区间结合指标权重,计算出各级的综合确定度,并进行等级评判;最后,将该方法应用于工程实例。研究结果表明:所评价管段的指标风险处于中等风险水平,风险可接受,管段的整体风险评价结果良好,有利于并行管道安全管理;实例证明该评价方法具有较好的适用性。     

A new leak location method based on leakage acoustic waves for oil and gas pipelines

In order to study a new leak detection and location method for oil and natural gas pipelines based on acoustic waves, the propagation model is established and modified. Firstly, the propagation law in theory is obtained by analyzing the damping impact factors which cause the attenuation. Then, the dominant-energy frequency bands of leakage acoustic waves are obtained through experiments by wavelet transform analysis. Thirdly, the actual propagation model is modified by the correction factor based on the dominant-energy frequency bands. Then a new leak detection and location method is proposed based on the propagation law which is validated by the experiments for oil pipelines. Finally, the conclusions and the method are applied to the gas pipelines in experiments. The results indicate: the modified propagation model can be established by the experimental method; the new leak location method is effective and can be applied to both oil and gas pipelines and it has advantages over the traditional location method based on the velocity and the time difference. Conclusions can be drawn that the new leak detection and location method can effectively and accurately detect and locate the leakages in oil and natural gas pipelines.     

Mapping safety culture attributes with integrity management program to achieve assessment goals: A framework for oil and gas pipelines industry

Introduction: The safety of oil and gas pipelines is an increasing concern for the public, government regulators, and the industry. A safety management system cannot be efficient without having an effective integrity management program (IMP) and a strong safety culture. IMP is a formal document (policies, planning, scheduling, and technical processes) while safety culture is a measure of views, beliefs, and traditions about safety. For regulatory authorities and O&G companies, assessing the effectiveness of both the IMP and safety culture through regulatory audits is a daunting task with indistinct findings. Method: An integrated framework based on regulatory audits is developed to assess the maturity of safety culture based on IMP efficacy through risk-based approach by using failure mode and effect analysis (FMEA). The framework focuses on three distinct aspects, the probability of failure occurrence in case of the non-compliance of regulatory and program requirements, severity of non-compliance, and effectiveness of the corrective actions. Results: Program requirements and performance indicators are translated into assessment questions which are grouped into 18 IMP components. Subsequently, these components are linked with four safety culture attributes. Sensitivity analysis revealed that four IMP components, i.e., organizational roles and responsibilities, policy and commitment, risk assessment, and training and competency, significantly affect the safety culture maturity level. Conclusions: Individual assessment of IMP and safety culture in O&G sector consumes extensive time and efforts in the auditing process. The framework facilitates the process by pursuing common criteria between IMP and safety culture. The O&G companies and regulator can prioritize the improvement plans and guidelines using the framework's findings. Practicalapplications:The integrated framework developed in this research will improve the existing assessment mechanism in O&G companies. The framework has been effectively implemented on a case of 17 upstream O&G pipeline-operating companies in the province of British Columbia, Canada.     

A matrix-based risk assessment approach for addressing linear hazards such as pipelines

Pipelines represent a linear risk source that can create unique challenges when assessing risks. In the past, risk has been managed by identifying construction requirements and setbacks based on population densities and types of land use. In the current risk assessment a matrix-based approach has been developed so as to determine the risks associated with high-vapor pressure liquids pipelines. The approach involved the development of a matrix representing each 100 m section of the reviewed pipeline along with approximately 30 risk factors that describe that section of the pipeline. Further, a receptor matrix was constructed to account for each hectare of land within 1 km of the reviewed pipeline system. This approach has allowed for the determination of risk as a function of location and separation from the pipeline and in turn has allowed for the determination of those areas where peak risks exist. In addition, this approach has ensured that the linear geometry related to pipeline risks has been accurately modeled. The resulting estimated risks have been evaluated against MIACC risk thresholds (geographic risk-based measures) and against proprietary internal corporate standards (societal risk-based measures). In this way the acceptability of the risk from the perspective of both the potentially impacted community and that of the pipeline operator can be measured. The net result is that the company has a clear picture of the risks associated with its pipeline and is better able to optimize its risk management and pipeline integrity programs.     

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface. Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure. Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper. The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India. In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment. The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16.147–10.638 kg/cm², 14.922–12.1674 kg/cm², 2.7645–1.2063 kg/cm² correspondingly. Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner. The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 91.9% when applied to the historical pressure datasets. The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation.     

基于模糊Bow-tie模型的油轮靠港装卸作业溢油风险分析

为了全面分析油轮靠港装卸作业溢油事故风险,在风险定量分析中引入了模糊Bow-tie模型,基于事故树方法分析油轮靠港装卸作业发生溢油事故的原因,采用事件树方法分析溢油事故可能导致的后果,利用模糊集理论与专家评价相结合的方法分析油轮靠港装卸作业溢油的模糊可能值,采用层次分析法确定作业溢油后果因素的权重值,采用矩阵乘法计算溢油后果风险值。分析结果表明:油轮靠港装卸作业过程中一旦发生溢油,发生火灾+污染、爆炸+污染的概率较高。基于以上风险分析提出了油轮靠港装卸作业风险的防控措施,可为油轮靠港装卸作业安全风险管理提供参考。     

海底管道泄漏油气扩散规律数值仿真与对比分析

为研究海底原油与天然气单相泄漏扩散规律的差异性,合理制定应急响应策略,减小事故损失,针对海底管道失效所致的原油与天然气泄漏问题,基于计算流体动力学CFD方法,建立海底油气管道泄漏事故后果预测与评估模型,对特定事故场景下的海底原油与天然气泄漏扩散过程进行模拟与分析,从泄漏扩散过程、工况因素影响、泄漏后果及应对策略4个方面对比原油与天然气的泄漏扩散特性。结果表明:相同工况下,海底原油与天然气在泄漏速率、扩散时间、扩散形态及水平最大扩散距离方面存在显著差别;与天然气相比,原油泄漏扩散行为对工况因素具有更高的敏感性;原油泄漏会引发严重的环境灾害,天然气泄漏则会影响海上结构物的稳定性及引发火灾爆炸事故,据此需合理制定具有针对性的应对策略。     

Risk assessment of an oil depot using the improved multi-sensor fusion approach based on the cloud model and the belief Jensen-Shannon divergence

At present, enterprises have introduced the Internet of Things (IoT) technology to monitor and evaluate the safety status of oil depots, allowing for the collection of a substantial amount of multi-source monitoring data from factories. However, sensor monitoring data is often inaccurate and fuzzy. To improve the reliability of risk prevention and control based on multi-source sensor data, this study proposed a CM-BJS-DS model based on the cloud model (CM), the Belief Jensen-Shannon (BJS) divergence and Dempster-Shafer(D-S) evidence theory. First, the relevant evaluation factors of the accident and their threshold intervals of different risk levels were determined, and the fuzzy cloud membership functions (FCMFs) corresponding to different risk levels were constructed. Then, the sensor monitoring data were processed using the correlation measurement of the FCMF, and basic probability assignments (BPAs) were generated under the risk assessment frame of discernment. Finally, the BPAs were pre-processed by the improved evidence fusion model and the accident risk level was evaluated. Based on the monitoring data, a case study was performed to assess the risk level of vapor cloud explosion (VCE) accidents due to liquid petroleum gas (LPG) tank leaks. The results show that the proposed method presents the following characteristics: (i) The BPAs were constructed based on the monitoring data, which reduced the subjectivity of the construction process; (ii) Compared with single sensors, the multiple sensor fusion evaluation yielded more specific results; (iii) When dealing with highly conflicting evidence, the evaluation results of the proposed method exhibited a higher belief degree. This method can be used as a decision-making tool to detect potential risks and identify critical risk spots to improve the specificity and efficiency of emergency response.     

基于粗集-层次分析法的严重度模糊综合评判权重确定方法

针对传统方法确定严重度因素权重主观性较大,精度不够的问题,将粗集理论和层次分析法结合起来,提出基于粗集层次分析法的严重度模糊综合评判权重的获取规则.收集某矿冲击地压事故10例,以发生事故后死亡人数、受伤人数、财产损失、停产损失、对人体的危害程度、对环境的影响程度、对企业信誉的影响程度和其他潜在影响8项指标作为严重度要素,对各要素的原始事故数据进行挖掘.首先利用层次分析法求得各项严重度要素的主观权重,然后运用粗集理论计算得出客观权重并将其权值化处理作为其权重系数,最后由两者之和得到组合权重.     

基于层次-模糊评价法的山区油气管道地质灾害易发性研究

针对山区油气管道地质灾害的基本特征,将层次分析与模糊综合评价相结合,建立起可用于山区管道地质灾害易发性评价的层次-模糊评价模型;并从自然因素、管道敷设状况、灾害活动和设计与误操作四个方面建立山区油气管道地质灾害易发性评价体系.首先依据专家经验构造各层影响因素的判断矩阵,并利用层次分析法求出各影响因素的权重集,然后对山区油气管道地质灾害易发性进行多级模糊综合评价,最终根据评价结果确定山区油气管道地质灾害易发性等级.通过具体实例检验了本文所建立评价方法的应用效果.     

A fuzzy risk assessment approach for occupational hazards in the construction industry

The techniques in the construction industry have been improved due to the rapid development of science and technology. However, the constructional hazards are not decreased as expected. To reduce or prevent occupational hazards in the construction industry, a fuzzy risk assessment method was proposed to provide a prevention and improvement technique against occupational hazards. This method used two-stage quality function deployment (QFD) tables to represent the relationships among construction items, hazard types and hazard causes. A fuzzy analytic network process (ANP) method was developed to identify important hazard types and hazard causes. Failure modes and effect analysis (FMEA) was performed to assess the risk value of hazard causes based on the fuzzy inference approach. The proposed method was applied to a telecom engineering company in southern Taiwan. The performance evaluation result indicated that this method can provide satisfactory risk assessment values of hazard causes and relevant improvement strategies.     

A risk assessment methodology for high pressure CO2 pipelines using integral consequence modelling

This paper presents a risk assessment methodology for high pressure CO₂ pipelines developed at the Health and Safety Laboratory (HSL) as part of the EU FP7 CO2Pipehaz project. Until recently, risk assessment of dense phase and supercritical CO₂ pipelines has been problematic because of the lack of suitable source term and integral consequence models that handle the complex behaviour of CO₂ appropriately. The risk assessment presented uses Phast, a commercially available source term and dispersion model that has been recently updated to handle the effects of solid CO₂. A test case pipeline was input to Phast and dispersion footprints to different levels of harm (dangerous toxic load and probit values) were obtained for a set of pipeline specific scenarios. HSL's risk assessment tool QuickRisk was then used to calculate the individual and societal risk surrounding the pipeline. Knowledge gaps that were encountered such as: harm criteria, failure rates and release scenarios were identified and are discussed.     

基于四阶B-样条最小二乘法的油气管道凹痕缺陷应变计算方法

针对油气管道上的凹痕缺陷,ASME B31.8提出采用基于应变的评价方法,但在内检测数据到应变求解的过程中,缺少噪声消除和轮廓拟合的相关方法。提出了采用四阶B-样条最小二乘拟合算法将检测数据进行噪声消除和轮廓拟合,实现从含有噪声的检测数据到应变求解的应变评价方法。采用文中所提方法和有限元方法,详细计算了几种典型凹痕的轴向弯曲应变、环向弯曲应变及其总应变。对比结果表明,该方法不仅能够较为精确的计算出弯曲应变值,还能得到较准确的弯曲应变分布情况。环向弯曲应变受在线检测工具精度的限制存在一定误差。计算发现,现有标准中忽略了环向薄膜应变分量,这使得总应变的计算结果不够准确。