In-service Pipeline Research Articles

Failure Analysis of Girth Weld Cracking in Gas Transmission Pipelines Subjected to Ground Subsidence and Traffic Loads

Girth welds are weak points in pipelines, and failures occur frequently. In a gas transmission pipeline, a girth weld experienced cracking, prompting a failure analysis using experimental methods and finite element analysis (FEA). Experimental results showed that X-ray non-destructive testing (NDT) revealed cracks, porosity, and lack of fusion in the girth weld. However, the hardness and microstructure of the material showed no abnormalities. During operation, the pipeline experienced an increase in soil cover and was subjected to ground subsidence and vehicle loads. Finite element analysis was conducted on the defective girth weld under different conditions, including varying soil cover depths, different levels of subsidence, and varying vehicle loads, to examine the pipeline’s stress response. The results indicated that the combination of soil cover, subsidence, and vehicle loads led to pipeline failure, whereas none of these factors alone was sufficient to cause girth weld failure. To prevent such failures from occurring again, the following measures are recommended: strengthen on-site welding quality control of girth welds, conduct inspections for defects in girth welds of in-service pipelines, and promptly address any defects that exceed acceptable limits.

A wavelet-based denoising method for pipeline dent assessments

Strain-based assessments of dents in buried steel oil and gas pipelines are commonly carried out in practice. Dent signals obtained from the caliper inspection tool contain noises that can have a large impact on the accuracy of the estimated strain. This paper proposes a novel wavelet-based denoising method for dent signals based on the overcomplete expansion with the corresponding dictionary constructed using the stationary and hyperbolic wavelet transforms. The proposed method is validated based on noise-free and noisy dent signals generated from elasto-plastic finite element analyses of a pipe segment subjected to an indenter and shown to be more effective than the commonly used wavelet transform-based hard- and soft-thresholding methods in terms of the root mean square error and the accuracy of the effective dent strain estimated from the denoised signal. The proposed method is further employed to denoise 42 real dent signals from in-service pipelines to illustrate its effectiveness and potential practical application to facilitate strain-based dent assessments.

Model for the Failure Prediction Mechanism of In-Service Pipelines Based on IoT Technology

With the rapid increase in pipeline mileage in China, the accurate prediction of corrosion issues in in-service pipelines has become crucial for ensuring safe pipeline operation. Traditional pipeline leakage monitoring methods are significantly limited by human factors and equipment precision, making it challenging to predict and identify leakage points accurately. Therefore, aligned with the trend of intelligent pipeline development, this study aims to construct a failure pressure prediction mechanism model for corroded pipelines based on IoT technology. This model leverages intelligent sensing and prediction to assess the safety status of corroded pipeline sections. Ultrasonic phased array technology detects specific corrosion points and detailed defect parameters within pipeline sections. The parameters are then utilized in the Simdroid domestic finite element analysis model to simulate the ultimate burst pressure of the pipeline. A single-variable approach is employed to analyze the sensitivity of different parameters to the pipeline’s ultimate burst pressure, with the minimum burst pressure point of multi-point corroded sections selected as the overall segment failure pressure. Finite element simulation data are integrated into a neural network database to predict the pipeline failure pressure. The real-time operational data of the pipeline are monitored using negative-pressure wave sensing. The operational pressure of the corroded points is compared with the algorithm-predicted failure pressure; if the values approach a critical threshold, an alarm is triggered. Moreover, the remote control terminals evaluate the pipeline’s self-rescue time, providing a buffer for pipeline leakage self-rescue. The failure prediction mechanism model for in-service pipelines was applied to the Fujian–Guangdong branch of the West–East Gas Pipeline III to verify its accuracy and feasibility. The research results offer technical support for the maintenance and emergency repair of pipeline leakage scenarios, leveraging intelligent pipeline technology to reduce costs and increase the efficiency of pipeline operations, thereby supporting the sustainable development of China’s oil and gas pipelines with theoretical and technical backing.

Research on settlement stress detection and control measures for in-service pipelines in valve chamber

Research on settlement stress detection and control measures for in-service pipelines in valve chamber

A probabilistic framework for external pitting corrosion growth modelling for buried steel pipelines considering soil properties

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power-law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect-specific growth models. The framework is applicable regardless of the availability of matched or non-matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where time-dependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

A Finite Element Analysis-Unascertained Measure Theory-Based Hybrid Approach to Safety Assessment for Pipelines Subject to Landslide Disasters

Abstract Pipeline safety faces a prevalent threat in mountainous areas due to landslides. The advent of landslides introduces the risk of pipeline leaks or ruptures, posing a significant threat to the environment, with the potential for casualties. Throughout the occurrence of landslides, uncertainties abound, yet few studies have addressed the incorporation of uncertainties in assessing pipeline safety. This work proposes a novel hybrid approach to the safety assessment for pipelines under landslides. The use of finite element analysis (FEA) models the pipeline under the action of landslides. The numerical outcomes, combined with unascertained measure theory (UMT), develop a multi-indicator unascertained measure (UM) matrix. Random forest (RF) algorithm is employed to determine the weight of indicators in the matrix. The hybrid application of set pair theory and the UM evaluation vector finally determine the pipeline safety degree and level. The proposed methodology has been well-validated through a case study on an in-service pipeline. The results indicate that the case pipeline safety degree is 2.777, 2.132, 3.132, 3.904, and 2.240, respectively. The corresponding safety level is III, II, III, IV, and II, respectively, which is consistent with the pipeline's actual condition. Different from the conventional safety assessment approach, the proposed methodology demonstrates the enhanced effectiveness, facilitating a more precise evaluation of the pipeline's safety condition.

Brief Discussion on Non-destructive Testing Methods for Pressure Pipeline Defects

With the development of China's oil and gas energy industry, the specifications of pressure pipelines for oil and gas transport are also becoming higher and higher, such as large caliber, ultra-thin wall, high pressure and other requirements, and the defects of pressure pipelines are becoming increasingly prominent. Due to the use of the environment, the material of the pipeline and other reasons, the pressure pipeline will produce a variety of defects in use. Non-destructive testing should be carried out on the defects caused by the pipeline in a timely manner, and according to the test results, defects should be graded according to the corresponding testing standards, so as to prevent the in-service pipeline from containing excessive defects and prevent pipeline breakage or explosion accidents.

Addressing the inspection selection challenges of in-service pipeline girth weld using ensemble tree models

Natural gas transportation predominantly utilizes pipelines, and the safety of these systems largely hinges on the integrity of girth welds. However, once pipelines are buried post-construction, pinpointing weld inspection sites becomes complex. Presently, weld risk assessments rely on costly and specialized inspections. Hence, enhancing the accuracy in identifying defective welds and discovering superior inspection techniques is paramount. Our research employed machine learning, specifically an ensemble model of RandomForest and CatBoost, to prioritize weld inspections. After analyzing the West-East Gas Pipeline Girth Weld Dataset and addressing data imbalances using the SMOTE technique, we refined the model parameters. The culminating model yielded an F1-score of 0.815 and an average accuracy of 0.836, outperforming standalone models. Employing the SHAP method improved the model’s transparency, facilitating a more informed decision-making process regarding pipeline inspections. This study underscores the potential of machine learning in elevating oil and gas pipeline safety measures.

A risk-based approach to inspection planning for pipelines considering the coupling effect of corrosion and dents

RBI, referring to a risk-based approach to inspection planning, is an established pipeline integrity management method. Both corrosion and dents are the primary threats to pipeline integrity. However, they are often treated separately in RBI without considering their interactions. This coupling may lead to a synergic effect on integrity degradation. The present study proposes an RBI planning framework for pipelines considering external corrosion and dents. Time-dependent pipeline deterioration by dents and corrosion is modeled probabilistically using a Dynamic Bayesian Network (DBN), in-line inspection (ILI) data, and corrosion propagation knowledge. Two failure scenarios (leakage and burst) are considered. The hybrid method, integrating Monte Carlo Simulation (MCS) and Latin Hypercube Sampling (LHS) technique, estimates the pipeline's Probability of Failure (PoF) over time. The pipeline failure risk is quantified by monetizing the Consequence of Failure (CoF). An optimization model of loss-maintenance total expected cost is introduced to determine the optimum inspection period using maximum acceptable risk (MAR) and the lowest total expected cost. A cost-benefit analysis (CBA) is finally implemented to choose appropriate risk reduction measures. The proposed framework is robust and well-validated by a case study on an in-service pipeline.

Influence of heat input on temperature and stress field of X80 steel pipeline cirumferential weld using type-B sleeve repairing

Abstract The multi-pass fillet welds of casing pipe were welded onto the in-service pipeline, which is widely used in pipeline repairs. However, the influence of welding heat input of B-type sleeve circumferential fillet weld on the temperature and stress field at the pipe circumferential weld is still unclear. In this paper, a double ellipsoid heat source model is developed to investigate the weld temperature field, residual stress field, and deformation field of the repair welding in service. The results show that the increase of weld heat input can effectively increase the size of the weld pool and the size of the high-temperature zone of the B-type sleeve circumferential fillet weld. And the peak temperature of the inner wall of the pipe girth weld increases with the increase of welding heat input of the sleeve circumferential fillet weld. The axial stress peak of the inner wall of the pipe girth weld reduces with the increase of welding heat input of sleeve circumferential fillet weld, and the axial deformation peak of the inner wall of the pipe girth weld increases first and then decreases with the increase of welding heat input of sleeve circumferential fillet weld.

Simulating non-homogeneous non-Gaussian corrosion fields on pipelines based on inline inspection data

This study presents a methodology to simulate non-homogeneous non-Gaussian corrosion fields on the external surface of buried steel pipelines by using inline inspection (ILI) data. It is assumed that the non-homogeneous non-Gaussian corrosion field consists of multiple homogeneous non-Gaussian anomalies that can be characterized by the marginal distribution and spatial autocorrelation function of the corresponding corrosion depths. Based on corrosion data collected from in-service pipelines, empirical relationships are developed to estimate parameters of the marginal distribution and autocorrelation function from the ILI information. To generate a synthetic corrosion field, one first generates realizations of corrosion anomalies and then merge the generated anomalies into a single non-homogeneous field by applying a spatial modulating function to each anomaly. The proposed methodology will improve the accuracy of the fitness-for-service assessment of corroded pipelines in practice as the burst capacity of the corroded pipeline can be more accurately evaluated by using the synthetic corrosion field than using the idealized corrosion field obtained from the ILI data.

A novel empirical model for peak strain prediction of pipelines under circular landslides

In-service pipelines in slope sections may experience a sudden increase in strain due to circular landslides. Obtaining the peak strain of pipelines (PSP) in engineering is often necessary to quickly evaluate the pipeline's safety status. Due to the lack of consideration of the influence of sliding surface shape, pipe laying location and diameter, the existing empirical model for peak strain prediction (EMPSP) does not perform satisfactorily, and the calculated error of PSP is far more than the acceptable range. A novel EMPSP for pipelines is proposed in this paper. First, a 3D nonuniform circular sliding finite element model (FEM) is established by the strength reduction method combined with the displacement function. Second, a detailed analysis is conducted on the impact of nondimensional parameters on the PSP. Finally, detailed discussions are presented to develop the coupling form between multiple parameters in the EMPSP, and a flowchart for pipeline design and evaluation is established. By comparing engineering case data, the accuracy of the novel EMPSP model has been improved by 59% or even higher compared to previous models. In conclusion, the novel EMPSP has a wide range of applications and can efficiently guide the evaluation of pipeline design based on strain.

Condition assessment of underground corroded pipelines subject to hydrogen damage and combined internal pressure and axial compression

In this work, a 3D finite element (FE) based model was developed to assess the condition of an underground hydrogen transmission pipeline containing a corrosion defect under combined internal pressure and soil movement-induced axial compression. The use of mechanical properties of X100 pipeline steel under different hydrogen charging time models the degree of hydrogen damage in pipelines. Parameter effects, i.e., axial compressive stress, hydrogen damage, defect geometries, and pipeline diameter-to-thickness ratio, were determined. The results demonstrated that the synergistic effect of axial compression, internal pressure, corrosion, and hydrogen damage can lead to a significant decrease in the failure pressure of pipelines. The failure pressure decreased with the wall thickness reduction and increased hydrogen damage, axial compressive stress, defect length, defect depth, and pipe diameter. The competitive effect was observed between the degree of metal loss and hydrogen damage in determining the burst capacity of pipelines. In situations where the pipeline integrity was severely compromised, the failure pressure exhibited minimal reduction despite the increasing severity of hydrogen damage. The stress distribution at the defect zone was influenced by axial compressive stress but remained unaffected by hydrogen damage under normal operating conditions (i.e., an internal pressure of 10 MPa). This work is expected to help operators understand the applicability of elder and in-service pipelines for hydrogen transmission.

Verification Analysis of X70 Type B Sleeve for In-service Pipeline Repair

Verification Analysis of X70 Type B Sleeve for In-service Pipeline Repair

Quantitative study on internal detection signal of weak magnetic stress in oil and gas pipelines based on force-magnetic noncoaxial effect

The weak magnetic stress internal detection technology is globally an important technical challenge in pipeline inspection. However, the stress concentration area in the in-service pipeline is very complex, and the influence of the stress direction on the magnetic signal is worthy of further assessment. To improve the accuracy of the magnetic signal analysis of ferromagnetic materials under stress, the angle compensation of classical model is carried out, and a finite element model of non-coaxial force and magnetic was proposed. Compared with the existing model, the improved model improves the detection accuracy within the weak magnetic stress, can more effectively assess the stress state of the pipeline, and timely repair of the pipeline according to the stress state can effectively reduce the possibility of serious accidents in the pipeline. According to the results, in the angle compensated model, the accuracy of the magnetic signal was elevated by 61%, respectively, leading to accurately predict the variation trend of magnetic signal. Gradient total characteristic parameter Stotal increased linearly with the elevation of internal pressure. With the increase of stress angle with the magnetic field, the axial amplitude parameter Rx varied 33% more than the radial amplitude parameter Ry, and the axial angle factor fx decreases at a 50% higher rate than the radial angle factor fy. It indicated that the axial component of the magnetic signal was more sensitive to the change of stress direction.

A coupling model of multi-feature fusion and multi-machine learning model integration for defect recognition

As Oil and gas pipelines are vulnerable to physical defects such as dents, cracks and corrosion leading to the risk of pipeline leakage. In this context, the recognition of these pipeline defects by using magnetic flux leakage (MFL) signal can provide a reliable reference and a good evaluation standard for predicting the remaining life of in-service pipelines. Nevertheless, the complexity of the MFL signal brings severe challenges to the recognition accuracy. This study, therefore, proposes a new coupling model for defect recognition based on multi-feature fusion and multi-machine learning model integration. This model is divided into three analysis phase, namely feature extraction phase, feature fusion phase and defect recognition phase. In the first phase, three kinds of features are extracted to obtain as much defect MFL signal information as possible. In the second phase, a multi-feature fusion method based on Dempster-Shafer (DS) evidence theory and Apriori algorithm is proposed, eliminating redundant features and realizing information complementation between different feature sets. In the third phase, we introduce a multi-machine learning model integration network (Stacking) to improve the defect recognition accuracy, which is inputted by the feature subset obtained from multi-feature fusion. By combining multi-feature fusion method with the Stacking mechanism, this coupling defect recognition model realizes the recognition of defect MFL signals. Experimental work shows that the proposed model improves the recognition accuracy and has the advantages of strong anti-jamming ability.

Application of wavelet transforms to the simulation of corrosion fields on buried pipelines

The present study presents a framework to analyze and simulate nonhomogeneous non-Gaussian corrosion fields on the external surface of buried in-service pipelines by using continuous and discrete wavelet transforms. The considered transforms are the two-dimensional continuous wavelet transform (CWT) using the complex Morlet wavelets, dual-tree complex discrete wavelet transform (DT-CDWT), and dual-tree complex discrete wavelet with hyperbolic wavelet transform scheme (DT-CHWT); the natural corrosion field is measured using a high-resolution laser scan. Scalograms and marginal distribution of the measured corrosion field are incorporated into the iterative power and amplitude correction (IPAC) algorithm to generate synthetic corrosion fields. The framework is explained and illustrated using a numerical example. The results indicate that the proposed framework can generate synthetic corrosion fields that effectively capture probabilistic characteristics of the measured corrosion field in terms of the scalogram, textural features, and burst capacity of the pipe segment containing the corrosion field. The results suggest that DT-CDWT and DT-CHWT combined with IPAC are efficient and viable options to simulate synthetic corrosion fields to facilitate the pipeline corrosion management practice, whereas the use of CWT in conjunction with IPAC is associated with high computational cost.

Application of On-line Digital Radiographic Inspection for Pipeline with Insulation

Pipes with insulation are widely used in the industrial field. The traditional nondestructive testing technology for on-line pipes with insulation often requires the removal of a large number of insulation layers and the forcing of the owner to stop for testing. The traditional testing mode not only has low detection efficiency, but also forces the owner to spend a lot of manpower, material resources and financial resources. In recent years, a series of new nondestructive testing (NDT) technologies have different advantages for in-service pipeline testing with coated layers. However, Digital radiographic (DR) testing can satisfy the integrity inspection of on-line pipeline with insulation well. In this paper, the basic principle of DR testing and the configuration of instrument parameters are introduced. Based on the analysis and discussion of the actual testing cases, it is proved that digital ray testing can effectively solve the quality testing of on-line insulation pipeline, and it is a new nondestructive testing technology worthy of further study and popularization.

Status diagnosis and feature tracing of the natural gas pipeline weld based on improved random forest model

The health state of natural gas pipeline welds usually needs to be judged by experienced experts based on the results of excavation works or internal inspections. Due to the low sensitivity of internal detection method, high cost, slow speed and large amount of information of excavation work, maintenance personnel are difficult to accurately and efficiently evaluate the health status of in-service pipeline weld. This paper proposes a model based on the improved random forest for weld joint status diagnosis and feature tracing as a solution to that issue. Firstly, the raw data is cleaned and downscaled by data preprocessing, variance threshold function and correlation heat map. Then an improved random forest model based on the cross-validation is established to predict and analyze the above data. Finally, the model's scores, the features' importance scores, and scores can be acquired. And the parts of the relevant features are analyzed retroactively combined with experts' experience. The experimental results show that the improved random forest model has an average score of 0.941, which is higher than that of the support vector machine model (SVM), k-nearest neighbor classification (KNN), decision tree and random forest models, the decisions generated from the results can help maintain and exclude services of the weld joint.

Effect of local heat treatment on residual stresses in an in-service repair welded pipeline

Abstract The multi-pass fillet welds of casing pipe were welded onto the in-service pipeline, which is widely used in pipeline repairs. However, the residual stress of repairing welded joints is larger, and it is easy to cause cracks in the heat-affected zone. In this paper, the local heat treatment was used to adjust the residual stress of the repaired welded joint of the in-service pipeline. The results show that the local heat treatment is very useful for reducing the residual stress of the repair welding joint of the in-service pipeline.