Failure Probability Of Pipeline Research Articles

Failure probability estimation of natural gas pipelines due to hydrogen embrittlement using an improved fuzzy fault tree approach

The estimation of failure probability is challenging in hydrogen embrittlement in steel pipelines due to the complexity of the synergistic effect of multiple factors. The present study proposed a hybrid methodology to estimate the failure probability of steel pipelines due to hydrogen embrittlement. The methodology integrates the fault tree analysis with a fuzzy comprehensive evaluation. Fault tree analysis captures the logical relationships between influencing indicators to develop a new assessment model of hydrogen embrittlement in steel pipelines. An improved fuzzy fault tree analysis method was proposed to process aleatoric and epistemic uncertainties to estimate the probability of each basic event due to the difficulty in obtaining the actual probabilities. The failure probability of blended hydrogen natural gas pipelines was estimated by considering the correlation of events. A case study demonstrated the applicability of the proposed method. Maintenance measures can be implemented according to the evaluation results to ensure pipeline safety.

Failure Probability of Corrosion Defective Pipelines Considering Failure Correlation between Pipe Sections

Failure Probability of Corrosion Defective Pipelines Considering Failure Correlation between Pipe Sections

Risk evolution of crude oil pipeline under periodic maintenance based on dynamic bayesian network

Pipeline failure can result in a chain reaction of accidents, including oil and gas leaks. Traditional risk analysis methods fail to fully capture the degradation and imperfect maintenance processes of multi-state systems. The model utilizes a hybrid failure rate recursive rule to accurately depict the changes in system reliability under varying maintenance intervals. The robustness of the model was validated through a case study of crude oil pipelines and the probability of pipeline failure under different maintenance strategies was predicted. The results suggest that semi-annual maintenance can significantly reduce the probability of pipeline failure compared to annual maintenance. If the equipment has experienced multiple instances of imperfect maintenance, it will accumulate excessive fatigue damage, leading to a gradual increase in the growth rate of the hazard rate function. Therefore, after 36 months, the advantage of semi-annual incomplete maintenance gradually diminishes, indicating that there is an upper limit to the number of equipment maintenance actions. To maximize failure risk reduction and minimize losses, a combined maintenance strategy (semi-annual and annual maintenance) should be implemented. Finally, through sensitivity and diagnostic analysis, key factors leading to pipeline failure were identified and appropriate measures to prevent pipeline failure were proposed.

Thermo-mechanical stability analysis of thermosyphons for pipeline soil and their optimal layout in permafrost regions

In permafrost regions, warm-oil buried pipelines release vast amounts of heat energy to surrounding soils and lead to rapid permafrost degradation. Settlement disasters induced by permafrost degradation significantly increase the probability of pipeline failure. The two-phase closed thermosyphon (TPCT) can cool soil, inhibiting permafrost degradation, but its deployment is still at the experimental stages along the China–Russia Crude Oil Pipeline (CRCOP). Therefore, it is important to determine an optimal layout for TPCTs that can achieve both effective cooling and cost control. In this study, a strategy for a nonlinear thermo-mechanical coupled model is proposed through Python–Abaqus secondary development. Based on it, the thermal and mechanical effects of TPCTs under different spacings, as well as their optimal layout, are numerically investigated. Closer TPCT spacing leads to a lower degradation rate of frozen soil and a lower peak TPCT heat flux. In addition, the peak pipeline von Mises stress in the transition zone increases for greater TPCT axial distances over time. When the TPCT axial distance is ≤8 m, the minimum pipeline principal strains remain below the critical compression strain over 50 years. Finally, a backpropagation neural network is constructed to predict the maximum pipeline compressive strain, and most prediction results fall within the 95% confidence interval.

Seismic fragility analysis of water supply pipelines retrofitted with corrosion-protection liner buried in non-uniform site

The corrosion-protection liner (CPL) technology consists of installing flexible plastic liners with anchoring studs inside existing pipelines and subsequently filling cement mortar to the gap between the waterproof liner and the pipeline. With the excellent chemical resistance, impermeability and fast construction speed, CPL provides an economical and environmentally friendly alternative for pipeline rehabilitation without large-scale excavation. However, the seismic performance of water supply pipelines after being retrofitted with CPL has not been well studied yet. In this study, a series of full-scale quasi-static experiments were firstly conducted on ductile-iron push-on joints before and after reinforcement with CPL to investigate the nonlinear behavior of the joints under longitudinal load and transverse bending. Simplified numerical models of straight pipelines with joints before and after retrofitting in the non-uniform site were then developed in the OpenSees platform, and incremental dynamic analysis (IDA) were performed with consideration of nonlinear soil-pipeline interaction. Twenty-eight pairs of ground motions with the peak ground velocities (PGV) collectively scaled from 200 mm/s to 3000 mm/s were used as inputs. Seismic fragility curves of the pipelines before and after retrofitting were developed with respect to the optimum intensity measure, PGV. It can be seen from the experimental results that the longitudinal load and bending moment capacity of the push-on joint increased about 400% and 20% after CPL reinforcement, respectively, and the joint opening and rotation decreased about 20% and 18% after retrofitting. Numerical results show that the CPL reinforced pipelines exhibit better seismic performance and CPL effectively reduces the failure probability of segmented pipelines under earthquake ground excitations.

Comparative study of microstructure and toughness of automatic welded joints of X70 pipeline steel with no slope and 25° slope

X70 pipeline steel is the most widely used for oil and gas pipeline transportation. When it is used in mountainous areas, the probability of pipeline failure increases dramatically due to the lack of toughness of welded joints. In order to ensure the welding quality, fully automatic welding has become the trend of pipeline welding. In this paper, X70 pipeline steel is butt welded by fully automatic welding under the condition of both no slope and 25° slope, and the microstructure and the toughness of two welded joints were observed and measured. The results show that the slope affects grain size and impact toughness. The grain size of the coarse-grained heat-affected zone (CGHAZ) in the root weld at the lower groove is larger than that at the upper groove. The impact energy of the heat-affected zone (HAZ) at the upper groove is significantly higher than that at the lower groove. For no slope welded joint, the weld CTOD value in the NQ direction is the lowest. For the 25° slope welded joint, the weld CTOD values are lower than HAZ CTOD values. Meanwhile, the HAZ CTOD values in the NP direction are significantly lower than those in the NQ direction. The fracture morphology of impact and CTOD of two welded joints is similar. The root-weld CGHAZ at the lower groove is the weakest area in the welded joint. For welding under slope conditions, the effect of slope on the performance of welded joints must be taken seriously.

A hybrid algorithm for inspection planning of subsea pipelines subject to corrosion-fatigue degradation

The corrosion-fatigue degradation is a significant concern causing subsea pipeline failure. This paper presents an inspection planning methodology for subsea pipelines subject to corrosion-fatigue degradation using dynamic Bayesian network (DBN) and improved Adaptive Genetic Algorithm (IAGA). The classical decision tree is used to represent the inspection process of pipeline. The corrosion-fatigue degradation is simulated using DBN to estimate the failure probability of subsea pipeline. IAGA is used to automatically present the inspection schemes and determine the optimal solution due to its strong optimization ability. A case study is utilized to illustrate the feasibility of the methodology. The results show that the cost is the lowest when the pipeline is inspected three times (11, 19 and 25 years) in its service, which is 58.0235 million dollars. The methodology can be used to guide the daily inspection planning of subsea pipeline subject to corrosion-fatigue degradation.

Methodology for assessing pipeline failure probability due to a debris flow in the near field

Aboveground transmission oil pipelines can cross debris flow-prone areas. Currently, there are no available methodologies to assess pipeline failure status with the different pipeline arrangements (location, direction, and segment lengths) and different operating conditions. For solving the research gap, this study proposes a novel methodology to simulate the cascade processes of debris flow propagation, the impact of debris flow on pipelines, and pipeline failure. With consideration of different pipeline arrangement and operating conditions. We introduce the polar coordinate system to set up locations and directions scenarios for the first time. Also, we use the 3-D debris flow simulation model (DebrisInterMixing solver in OpenFOAM) coupled with a modified pipeline mechanical model considering operating conditions for the first time. The proposed methodology shows the different trends of pipeline failure probability with the increase of pipeline segment length for the different pipeline locations and directions. The result shows, for the pipelines of 30° the tensile stress has a more moderate increase rate with the increase of pipeline segment length, and the pipeline failure probability keeps 0 at the 5-m location. At 5 m and 15 m locations, the failure probabilities of the pipelines of 60° and 90° start to increase when the segment length is 13–14 m, while for other pipelines the segment length is 17–19 m. The findings of this study can support the decisions of government authorities, stakeholders, and operators for risk assessment, prioritization of hazard mitigation measures and emergency planning, or concerning decisions regarding pipeline siting during the design, routing, construction, operation, and maintenance stage.

Prediction of corrosion failure probability of buried oil and gas pipeline based on an RBF neural network

Risk assessment is critical to ensure the safe operation of oil and gas pipeline systems. The core content of such risk assessment is to determine the failure probability of the pipelines quantitatively and accurately. Hence, this study combines the MATLAB neural network toolbox and adopts an Radial Basis Functions (RBF) neural network with a strong non-linear mapping relationship to build a corrosion failure probability prediction model for buried oil and gas gathering and transmission pipelines. Based on the hazard identification of pipeline corrosion failure, the model summarizes the causes of corrosion failure and determines the input and output vectors of the neural network based on the fault tree. According to the selected learning samples, through the design and training of network parameters, the RBF neural network that can predict the system failure probability is finally obtained. Taking the failure probability of 30 groups of high-pressure gathering and transmission pipelines of gas storage as an example, the capability of inputting the probability of the bottom event and outputting the probability of the top event is demonstrated through training data. Our results show that the calculated failure probability based on the fault tree analysis model is consistent with the predicted failure probability based on the RBF neural network model. Hence, the RBF neural network model is shown to be reliable in predicting the corrosion failure probability of buried pipelines.

Risk Assessment Method for the Safe Operation of Long-Distance Pipeline Stations in High-Consequence Areas Based on Fault Tree Construction: Case Study of China–Myanmar Natural Gas Pipeline Branch Station

The safe operation of natural gas long-distance pipeline stations is the critical link for its sustainable transmission. Therefore, station operation risk assessment and space-time expression in high consequence areas have become a critical problem that must be urgently addressed. The conventional way to assess the probability of gas pipeline failure cannot meet the requirements of the natural gas operating companies for the safety assessment of natural gas stations. Taking the example of the China–Myanmar natural gas pipeline branch station, based on the fuzzy fault tree model (FFT), a comprehensive pipeline safety evaluation method (CPSE-FFT) has been introduced, which evaluates the risk factor of pipeline operation in the station area and the social security risk brought by it. In this case study, the method is applied to a branch station of the China–Myanmar long-distance pipeline. In the results, the probability of pipeline accidents in the area of the station is 5.79×10−4 times/year, and six buildings with higher risk levels among the 37 buildings are also exposed on the risk distribution map. The overall social risk level is also expressed. CPSE-FFT is a practical, reasonable, and accurate assessment method that can quantitatively reflect the risks posed by the safe operation of natural gas stations. This method is suitable for most natural gas station risk assessments. The results of the risk assessment provide a reference for managers and decision-makers to reduce the risk of natural gas stations.

Risk-based maintenance time for oil and gas steel pipelines under corrosion including uncertainty on the corrosion rate and consequence-based target reliability

A risk-based procedure is proposed to recommend a sustainable maintenance strategy for oil and gas steel pipelines under internal pitting corrosion. The procedure considers the uncertainty and evolution on time of the corrosion depth, which depends on the initial depth and the corrosion rate, and includes the calculation of the time variant failure probability, or reliability, and its acceptable limit (target) which are calculated according to the importance of the pipeline failure consequences. Inspection results from pig runs, on an actual pipeline, are the basis to calculate the pipeline failure probability on segments of 20 km length. These calculations are performed via Monte Carlo simulation and the time variant reliability index is compared to the target reliability index per segment and per importance level. The optimal time is the time when the reliability index meets the target reliability for the considered level of failure consequences. The results allow for the priorization on the attention or pipeline segments, and funds allocation, for pipelines maintenance works according to the risk exposition conditions of the segments.

Pipeline Failure Assessment Based on Fuzzy Bayesian Network and AHP

A long-distance pipeline is a crucial connection for oil and gas (O&G) producing areas and demand locations. Pipeline failure may be caused by various factors, which can escalate into a disastrous event, causing huge loss of life and property. Reliable failure assessment is urgently needed to avoid the occurrence of pipeline failure and reduce losses effectively. Owing to a lack of historical pipeline data and the impact of various uncertainties, this study presents a model that systematically integrates Bayesian network (BN), fuzzy theory, and analytic hierarchy process (AHP) to analyze the probability of pipeline failure. In the proposed model, factors contributing to O&G pipeline failure are obtained from the literature, database searches, and expert questionnaires. AHP is used to establish a hierarchical network represented by a causality diagram. The hierarchical network is mapped to a BN structure, and the conditional probability of nodes in the BN is obtained by AHP and expert judgment. The prior probabilities of basic factors are analyzed by expert opinion with fuzzy theory in the absence of historical data. The model can be used for the quantitative analysis of pipeline failure. Moreover, diagnostic analysis can also be performed by updating the probabilities in the BN model with new information. The feasibility and rationality of the model are validated in a gas pipeline, which indicates that the proposed model can provide effective decision-making for pipeline managers to prevent and manage pipeline failures.

Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas

Currently, quantitative risk assessment is often used for pipeline safety analysis. However, the low probability of pipeline failure may produce a false safety evaluation result because high-consequence areas with high population densities cannot be effectively identified. Furthermore, nearly half of the gas leakage accidents in China have occurred in densely populated areas. Therefore, an improved quantitative risk assessment method is proposed. First, we establish two models: (1) a failure probability model based on improved historical failure and disaster derivation probabilities and (2) a risk consequence model considering potential direct and indirect losses based on the probability of disaster evolution. Considering the concept of human rights equality and the social model of “life is first” in China, a method to correct the loss value of life according to population density is proposed, which can effectively avoid the hidden phenomenon of high-consequence areas mentioned above. A pipeline in China is evaluated using this improved method. Compared with traditional evaluation results, the new evaluation method can effectively identify a high-consequence area and obtain more reasonable results. Thus, a pipeline maintenance plan can ensure the interests of enterprises and fully respect the lives of individuals threatened by the potential risk of the pipeline.

QUANTITATIVE RISK ASSESSMENT OF THE EFFECT OF SAND ON MULTIPHASE FLOW IN PIPELINE

The presence of sand particles flowing along with reservoir fluids in a pipeline increases the probability of pipeline failure. The risk of pipeline failure is either accentuated or abated by the flow conditions of the fluids in the pipeline. In this study, a quantitative risk analysis of the effect of sand on pipelines during multiphase flow, under the pipeline failure modes; sanding up, erosion, and encountering abnormal pressure gradient was conducted. Three piping components were considered: line pipe (nominal size 1.5 in [3.8 cm]), swing check valve (nominal size 12.007 in [30.5cm]) and 90 deg LR Elbow (nominal size 2.25 in [5.7cm]). Correlations that indicate the critical velocities and the critical sand concentrations above/below which these failures occur were employed and implemented in a Visual Basic program. The analysis was conducted at a temperature of 204 °C and pressure of 604 psi [4.2×106 Pa]. A probability distribution, simulating real-life scenario was developed using Monte Carlo simulation. This determines the probability of deriving critical sand concentration values that fall beyond the set statistical limits which indicates the probability of occurrence of the failure being investigated. For all three failures, the severity of occurrence (represented by CAPEX incurred in solving the failures) was multiplied with the probability of failure which gave rise to the risk indexes. Based on the histogram plot of average risk index and analysis, the study reveals that larger diameter components are prone to turbulence which lead to greater risk of erosion. The risk of abnormal pressure drop and sanding up were considerably lower than that for erosion (abrasion).

Time-Dependent Reliability Analysis of Buried Water Distribution Network: Combined Finite-Element and Probabilistic Approach

Corrosion deterioration is the predominant factor for a large number of cast iron (CI) water main breaks. The reliability of CI pipelines decreases overtime as corrosion growth on pipe walls significantly reduces the strength of pipeline materials. To predict the reliability of water pipelines accurately, this study used a finite-element analysis (FEA) approach to determine the maximum pipeline stress at various stages of the pipeline’s life. A series of FEAs was performed to consider uncertainties in parameters associated with stress analysis. The circumferential stress was found to be the most critical stress for pipelines. Time-variant circumferential stress was obtained by accounting for the time-dependent corrosion pit growth on the pipeline wall. The time-variant failure probability of pipelines was determined by comparing the circumferential stress and the tensile failure strength of the pipeline at the burst limit state. The FEA model developed in this study was validated with the results obtained from previous studies. Monte Carlo simulation was performed to generate the fragility curves in a probabilistic manner. This study also compared the fragility curves obtained utilizing FEA and conventional equations (elastic ring theory and the Spangler formula). The results showed that analytical equations are too conservative and provide a higher failure probability of pipeline. A sensitivity analysis was performed to identify the relative importance of parameters contributing to the pipeline’s performance. Additionally, the reliability of the water distribution network (WDN) was calculated based on the minimum cut set (MCS) and graph decomposition methods. The results showed that the two methods provide similar outcomes, however, MCS provides a greater safety margin. The proposed approach was illustrated for an example WDN.

The risk assessment of a crude oil pipeline using fuzzy and bayesian based bow-tie analysis

Cross country pipelines are one of the major modes of transportation of petroleum products. Due to external and internal factors, the failure probability of pipeline is increasing over the past decades. The failure of pipelines can cause fires, which can result in environmental damage, loss of materials and further resulting into casualties. Effective risk assessment is vital to prevent and mitigate such potential incidents. In this work, an efficient tool that makes use of Bayesian approach along with Bow-tie analysis is used to obtain the failure frequencies of all the identified causes that may lead to failure of the selected pipeline located in Kerala, India. Bayesian networks can perform effective dynamic risk analysis by considering the conditional dependencies between various basic events leading to pipeline failure. The fuzzy logic and expert elicitation method are incorporated to determine the prior failure frequencies of all the identified causes of pipeline failure. The effectiveness of Bayesian network in performing forward analysis to determine the probabilities of pipeline failure consequences is demonstrated. This study also identifies the weak links associated with the occurrence of particular consequences, so that adequate measures can be taken to rectify them.

Seismic resilient three-stage enhancement for gas distribution network using computational optimization algorithms

A computer-simulation-based three-stage optimization strategy is proposed for the resilience enhancement of urban gas distribution networks (GDNs). In stage I (pre-earthquake stage), the Fixed Proportion and Direct Comparison Genetic Algorithm (FPDC-GA) is applied to select key pipelines need to be strenthened or replaced under limited funding in preparation for future potential earthquakes. In stage II (post-earthquake stage), pressure tests must be carried out according to the gas leakage situation reported by users or detected by devices. The Multi-Label K-Nearest-Neighbor (ML-KNN) algorithm is used to predict the corresponding failed pipelines and optimize the pipeline pressure test order. In stage III (repair stage), a strategy based on a greedy algorithm is applied to optimize the pipeline repair sequence. The proposed methods were applied to the GDN of a city in northern China. The following conclusions were drawn from the results: (1) The FPDC-GA enhanced the robustness and resourcefulness of the GDN system to the maximum level within the available funding budget. (2) The pipeline pressure test order calculated using the ML-KNN algorithm was significantly improved compared with a random pressure test order or one based on the empirical failure probability of pipelines. (3) After optimization using a greedy algorithm, the performance recovery curves under different earthquake conditions were shaped as an exponential function, which indicates that the performance of the GDNs recovered in the most efficient manner. The findings of this study could be useful as tools for the seismic resilience enhancement of GDNs in different stages. The proposed optimization algorithms can also be extended to the lifeline of other networks.

Application of HMC-SS Method in Pipeline Reliability Analysis and Residual Life Assessment

In this paper, the reliability analysis and residual life assessment model of gas pipelines with multiple corrosion pits are established. Aiming at the simulation evaluation of small failure probability of gas pipelines, a new method for reliability analysis and residual life assessment of gas pipelines with multiple internal corrosion pits is proposed, which is called the Hamiltonian Monte Carlo subset simulation (HMC-SS) method. Compared with the traditional MCS (Monte Carlo simulation) algorithm, the HMC-SS method has the advantages of less sampling, low cost, and high accuracy. And compared with the random walk SS method, the HMC-SS method can analyze the state space more efficiently and achieve faster convergence. In this paper, the HMC-SS method is applied to the reliability analysis and residual life assessment of gas pipeline engineering, and the sensitivity analysis of the random parameters affecting the failure probability of the pipeline is carried out. The results show that the corrosion rate, the depth of corrosion defects, and the wall thickness of the pipeline have great influence on the residual life of the pipeline, while the yield strength, working pressure, and the length of corrosion pits have no obvious influence on the failure probability and residual life of the pipeline. The analysis shows that the proposed HMC-SS method can be used as a reasonable tool for failure assessment of natural gas pipelines affected by corrosion to determine the remaining life of the pipeline system. This method provides a reliable theoretical basis for the integrity management of the gas pipeline.

Strength matching factor of pipeline girth weld designed by reliability method

Crack-like defects frequently form in pipes exposed to aggressive environments or during welding fabrication. Appropriate strength matching factors should be determined for welding process evaluation based on the reliability aspect. In this paper, the current research status of codes and standards related to the strength requirement of weld metals in the pipeline industry were reviewed. The results showed that almost all standards considered the lower limit of pipe tensile strength as a requirement for weld strength evaluation for specimens of cross-weld test breaking at the weld position. This was contradictory to the original intention of even-match or over-match of pipeline girth weld. The Monte Carlo Method was then employed to investigate the evolution of girth weld strength matching factor and pipeline failure probability under different high-low misalignments, fracture toughnesses, and standard deviations of pipe strength distribution. The data revealed significantly reduced probability of pipe failure as the weld metal strength matching factor increased. At the same strength matching factor, the failure probability of pipe decreased as fracture toughness of girth weld. The failure probability of pipe also declined as the misalignment and standard deviation of pipe strength distribution diminished. Based on the requirement of certain target reliability and strain demand, a semi-empirical prediction model of critical strength matching factor was proposed for engineering applications. The analysis of parameter sensitivity showed the relative importance of several parameters on the critical strength matching factor of girth weld, including high-low misalignment, standard deviation of pipe material strength distribution, and fracture toughness.

Structural Integrity of Steel Pipeline with Clusters of Corrosion Defects.

The main goal of this paper is to evaluate the burst pressure and structural integrity of a steel pipeline based on in-line inspection results, in respect to the grouping criteria of closely spaced volumetric surface features. In the study, special attention is paid to evaluation of data provided from the diagnostics using an axial excitation magnetic flux leakage technology in respect to multiple defects grouping. Standardized clustering rules were applied to the corrosion pits taken from an in-line inspection of the gas transmission pipeline. Basic rules of interaction of pipe wall metal losses are expressed in terms of longitudinal and circumferential spacing of the features in the colony. The effect of interactions of the detected anomalies on the tube residual strength evaluated according to the Det Norske Veritas Recommended practice was investigated in the current study. In the presented case, groups of closely-spaced defects behaved similarly as individual flaws with regard to their influence on burst pressure and pipeline failure probability.