Pipeline Defects Research Articles

Methodical approach to determining the maximum permissible velocity of the gas flow with mechanical impurities using numerical modeling of erosion processes of the main technological elements of wellhead equipment

Long-term operation of gas fields is accompanied by changes in the stress-strain state of the productive reservoir, including irreversible processes of its destruction. The destruction of a productive reservoir is one of the reasons for the removal of mechanical impurities at the wellhead, abrasive wear, metal degradation, the appearance of defects in pipelines, equipment and wellhead binding. In order to avoid serious consequences from the development of defects due to the removal of impurities, various examinations are carried out using non-destructive testing methods. The need to monitor the actual condition of pipelines and wellhead equipment of wells operating the Cenomanian and Valanginian formations is due to the fact that during operation significant damage and destruction of the main elements of technological equipment may occur due to the removal of mechanical impurities, water, as well as the formation of hydrates, sand jams, etc., this can lead to serious economic losses, as well as the negative impact on the environment as a result of leaky connections and assemblies. Thus, in order to ensure reliable operation of producing gas wells in conditions of destruction of the bottom-hole zone of the well, it is necessary to choose a reasonable technological mode of operation, in which the risk of formation of sand and liquid plugs is reduced, and the negative impact of the removal of impurities on the condition of the equipment is minimized. Keywords: gas well; numerical modeling; bottom-hole zone; technological mode.

APPLICATION OF DEEP NEURAL NETWORK ON AUTONOMOUS UNDERWATER VEHICLES (AUV) IN SUBSEA PIPELINE INSPECTION

The purpose of this study is to investigate the application of deep neural network for the Automated Underwater Vehicle in the subsea pipeline inspection. In today’s modern world, we see more and more sophisticated and precise computer vision object detection technology being implemented in our daily lives. To name a few, security cameras, self-driving cars, drones and more. This research suggests that computer vision pipeline defect detection is an attractive solution for the future subsea pipeline defect detection as it relies less human intervention and is more reliable. In this paper, review of the current methods of subsea pipeline inspection was done. Some of the methods with visual detection and their limitations are also discussed. Apart from that, the machine learning algorithm of our focus, Faster RCNN is studied. Beyond that, we have explained the methods of our experimentation and the process of training as well as validating the custom dataset. The outcomes are separated into different sections, training curve, visual data representation as well as the model accuracy in different operating environment. In conclusion, we found that the model is able to detect the underwater pipeline with up to 1mm leak size. However, the low accuracy due to the insufficient dataset is recognized as a bottleneck and some of the recommendations are suggested for future improvement.

Classification and sizing of surface defects of pipelines based on the results of complex diagnostics by ultrasonic, eddy current and visual and measuring methods of nondestructive testing

The issues of classification and determination of parameters of surface operational defects according to the results of ultrasonic, eddy current and visual and measuring methods of nondestructive testing are considered. At the same time, the visual and measuring method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented, which has been applied to classify the images obtained from a TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics, in which the obtained models are applied, and determines the accuracy of this algorithm by the RMSE metric, which was calculated within the studying test data set and amounted to 0.011 mm.

Investigation into the mechano-electrochemical interaction of internal and external corrosion defects on pipe surfaces

Revealing the mechanical-electrochemical interactions (M-E) on internal and external defects in pipelines is of paramount importance for ensuring the safe operation of pipelines. The present study establishes a mechano-electrochemical multi-physics coupling model and develops a two-dimensional finite element (FE) model of internal and external defects on the pipe surface to investigate the M-E interaction of X100 steel pipe with corrosion defects on both internal and external surfaces. The results demonstrate that the M-E interaction is significantly influenced by the geometrical shape, relative position, and internal pressure of defects. Moreover, altering a single defect parameter can simultaneously impact the M-E interaction of two defects. As the depth of defects increases, the cross-sectional area between internal and external defects decreases, leading to an intensified interaction between mechanics and electromagnetics and a gradual concentration of M-E interaction at the center of defects. The magnitude of the M-E interaction exhibits a negative correlation with the length of the defect, while the presence of a long defect minimally impacts the M-E interaction on the opposing side. As the external defects gradually distance themselves from the internal defects, the M-E interaction diminishes and the concentration area of M-E interaction shifts in the opposite direction to defect migration, while stress in the non-defect region adjacent to both internal and external defects progressively intensifies. The increase in internal pressure significantly enhances the M-E interaction of internal and external defects, with the M-E interaction primarily focused on the minimum cross-sectional area of radial overlap between these defects. At low internal pressures, however, the M-E interaction becomes negligible.

Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing

Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing

Magnetic flux leakage defect size estimation method based on physics-informed neural network.

Magnetic flux leakage (MFL) is a magnetic method of non-destructive testing for in-pipe defect detection and sizing. Despite the fact that recent developments in machine learning have revolutionized disciplines like MFL defect size estimation, the most current methods for quantifying pipeline defects are primarily data-driven, which may violate the underlying physical knowledge. This paper proposes a physics-informed neural network-based method for MFL defect size estimation. The training process of neural network is guided by the MFL data and the physical constraints that is mathematically represented by the magnetic dipole model. We use synthetic MFL data produced by a virtual MFL testing of pipeline defects to validate the proposed method through a comparison to purely data-driven neural networks and support vector machines. The findings imply that the physics-informed strategy can both improve predictive accuracy and mitigate physical violations in MFL testing, providing us with a better knowledge of how neural networks perform in defect size estimation. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

A cascaded deep learning approach for detecting pipeline defects via pretrained YOLOv5 and ViT models based on MFL data

Long-distance oil and gas pipeline has become the main mode of transporting oil and gas resources as the demand for fossil energy increases. The complex environment of long-distance pipeline makes the pipeline susceptible to surface defects, which pose a major safety threat. Magnetic flux leakage (MFL) detection is a non-destructive testing (NDT) method that can be used to map and monitor pipeline defects. However, the accuracy and reliability of MFL detection can be severely compromised by the data processing and analytics methods used. This paper presents a novel hybrid method for detecting pipeline defects by combining well-established computer vision algorithms, i.e., YOLOv5 and Vision Transformer (ViT), enabling accurate detection and classification of pipeline defects simultaneously. An in-house laboratory dataset generated by Pull Through Testing (PTT) was used for model training and verification, which contains the manipulated pipeline defects, e.g., metal corrosion, geometrical defect, metal material loss, and geometrical structures etc. The effect of different model architectures on “the defect identification performance was investigated. Comparisons show that the proposed cascaded approach surpasses the YOLOv5 algorithm alone in pipeline defect classification accuracy, while still preserving the versatile ability of defect detection with high precision.

Ground stability of ellipsoidal cavity due to pipeline defects

The problem of road-related sinkhole stability has long been one of the main safety concerns to pavement engineers. Collapse and blowout failures are two typical types of pipeline-related sinkhole failures. Although much research on sinkhole stability analyses can be found in the literature, most of them assume a simple circular or rectangular shape of a cavity. In this study, the problem of sinkhole stability in an ellipsoidal cavity is investigated under both blowout and collapse conditions using advanced finite element limit analysis with adaptive meshing in an axisymmetric condition. A dimensionless pressure ratio is defined to represent sinkhole stability that is a function of many design parameters such as depth ratio, elliptical shape ratio, and soil strength ratio. Selected results are compared with published solutions, and comprehensive solutions of the parametric study are presented in the form of charts for use by design engineers. The present study contributes to the understanding of sinkhole stability under an ellipsoidal cavity, and it should be of interest to the road engineering community.

Rapid identification of erosion thinning and scaling thickening of inner wall of circular tube based on inverse heat conduction problem method

Due to the erosion of fluid and the deposition of fouling in pipes, the internal defects of pipelines are widespread and seriously threaten the safety of industrial production. In this paper, an indirect inversion model based on modified one-dimensional correction method (MODCM) is proposed to identify the erosion thinning and fouling deposition of the inner wall of a two-dimensional circular tube. By constructing the relationship between the equivalent thermal conductivity and the geometric parameters of defect, the inversion of defects is transformed into the inversion of the equivalent thermal conductivity, which overcomes the difficulty of re-gridding caused by the change of the geometric domain during the iterative process, and effectively improves the calculation efficiency. In order to explore the validity and timeliness of the constructed models, the numerical experiments are carried out. The influence of measurement errors, thermal conductivity of pipe and thermal conductivity of fouling on the identification results are systematically analyzed. The research results show that the thermal conductivities of pipes and fouling have little effect on the inversion results, and the inversion accuracy decreases with the increase of measurement errors. When the measurement error is σ = 0.5 ℃, the identification error remains about 0.2 %, which shows that the method has good robustness. In addition, compared with the typical direct identification models of Levenberg-Marquardt method (LMM) and conjugate gradient method (CGM), the proposed indirect inversion model is superior in computational efficiency and tracking ability of discontinuous boundaries.

A Pipeline Defect Instance Segmentation System Based on SparseInst.

Deep learning algorithms have achieved encouraging results for pipeline defect segmentation. However, existing defect segmentation methods may encounter challenges in accurately segmenting the complex features of pipeline defects and suffer from low processing speeds. Therefore, in this study, we propose Pipe-Sparse-Net, a pipeline defect segmentation system that combines StyleGAN3 to segment the complex forms of underground drainage pipe defects. First, we introduce a data augmentation algorithm based on StyleGAN3 to enlarge the dataset. Next, we propose Pipe-Sparse-Net, a pipeline segmentation model based on SparseInst, to accurately predict the defect regions in drainage pipes. Experimental results demonstrate that the segmentation accuracy of this model can reach 91.4% with a processing speed of 56.7 frames per second (FPS). To validate the superiority of this method, comparative experiments were conducted against Yolact, Condinst, and Mask R-CNN, and the model achieved a speed improvement of 45% while increasing the accuracy by more than 4%.

Defect identification method for ultrasonic inspection of pipeline welds based on cross-modal zero-shot learning

Ultrasonic inspection of pipeline welds still uses the traditional visual inspection signal method to identify pipeline defects. The identification of defects relies entirely on the subjective judgment of practitioners and is highly dependent on their level of experience. Deep learning models have achieved very good results in classification tasks, but they rely on a large number of annotated data samples for each category. However, it is difficult to collect a large number of samples with different defects and annotate them for the classification of pipe welding defects. Based on the idea of zero-shot learning (ZSL), which makes full use of experts’ semantic descriptions of defect categories, artificial semantic features are integrated cross-modally with ultrasonic inspection signal features. In this way, a common semantic space containing seen and unseen classes is constructed to achieve the detection of various defects. Meanwhile, to alleviate the problem of extreme imbalance of training data between the seen and unseen classes in ZSL model training, a ZSL model Feature-GAN-ZSL (FGZ) fused with a generative adversarial network (GAN) is proposed. The model utilizes a Feature-GAN network to generate unseen class features during training and adds a classifier to enhance the generation of features with stronger discriminative power. In the experiments, sample data for porosity, incomplete penetration, and cracks were used as visible classes, and samples for incomplete fusion and slag entrapment were used as unseen classes. Five state-of-the-art models in the ZSL domain were compared. The results show that the FGZ model has a good ability to recognize various defects, not only the types of defects that participated in the training but also the defects that did not participate in the training. This plays a perfect role in dealing with various pipeline welding defects.

Intelligent identification of girth welds defects in pipelines using neural networks with attention modules

Girth weld defects (crack, lack of penetration, lack of fusion, and edge nibbling) can cause pipeline cracking failure accidents. Internal magnetic flux leakage (MFL) detection can successfully identify pipeline defects, while the intelligent identification of MFL signals based on deep learning can promote the accurate determination of pipeline girth weld defects. Although the YOLOv5 model can effectively identify abnormal image objects, it exhibits no attention preference during the feature extraction process, proving insufficient for small objects. This study targeted minor defects in the girth weld of the pipeline and used the Convolutional Block Attention Module (CBAM) to optimize the YOLOv5 network model structure, increasing detection network attention preference toward extracting small-target defect signals. The CBAM+YOLOv5 model improved the detection accuracy of the MFL signal of the girth weld in the pipeline from 89.33% to 98.11% and correctly identified and classified the MFL signal of the pipeline girth weld with 85% confidence, with minor anomalies. The CBAM+YOLOv5 model effectively improved the identification accuracy of the girth weld defect signal in the pipeline, providing technical support for safety grade assessment and excavation verification.

Internal and External Pipe Defect Characterization via High-Frequency Lamb Waves Generated by Unidirectional EMAT

Periodic permanent magnet(PPM) electromagnetic acoustic transducers (EMATs) are commonly employed for axial defect inspection in pipelines. However, the lowest-order shear horizontal waves (SH0) guided waves have difficulties in distinctly differentiating internal and external defects. To enhance the signal-to-noise ratio and resolution, a unidirectional electromagnetic acoustic transducer (EMAT) based on Circumferential Lamb waves (CLamb waves) is developed. Through structural parameter optimization and excitation frequency adjustment, high-amplitude and low-dispersion CLamb waves are successfully generated in the high-frequency-thickness product region of the dispersion curve. Finite element simulations and experimental validation confirm the capability of this EMAT in exciting CLamb waves for the detection of crack-like defects. Experimental results demonstrate that the excitation efficiency of the CLamb EMAT exceeds that of the periodic permanent magnet electromagnetic acoustic transducer by more than tenfold. The defect reflection signal of the CLamb EMAT exhibits higher resolution and more significant amplitude compared to the PPM EMAT. The integration of this method with SH0 mode detection allows for the inspection of both internal and external defects in pipelines, offering a new avenue for EMAT applications in pipeline inspection.

Probabilistic finite element-based reliability of corroded pipelines with interacting corrosion cluster defects

The random distribution of pitting corrosion defects in pipelines normally leads to interacting cluster defects that behave noticeably different from single metal loss defects. Generally, the probabilistic approaches employ explicit burst pressure limit states for corroded pipelines subjected to only internal pressure. However, a significant level of conservatism is typically associated with the probabilistic assessments of corroded pipelines using the closed-form explicit limit state functions, which presents considerable challenges in maintenance planning and risk management. Therefore, this paper proposes a pathway for developing efficient performance functions for the assessment of interacting pipeline corrosion clustering defects using probabilistic finite element-based reliability method. This seven-stage framework combines multiple uncertainty representation schemes to evaluate the probability of failure. The impact of the critical design variables such as the elastic and plastic material properties, corrosion features, and interacting cluster defect characterisations are identified to guide the burst pressure design, operations and maintenance optimisation. The employed surrogate-based active learning reliability approach yielded an efficient probability estimate at a lesser computational cost than the simulation-based reliability methods. The proposed framework reduces the conservatism and computational cost related with explicit burst pressure limit state functions for corroded pipelines and offers informed decision-making on risk and maintenance management.

Research on Geometric Parameter Prediction Algorithm for Oil and Gas Pipeline Defects

Aiming at the problem of insufficient feature extraction of magnetic leakage signals by traditional neural networks, this paper proposes an attention depth convolutional neural network model (ECA-VGG16), which adds an attention mechanism combined with convolutional neural network on the basis of deep convolutional neural network VGG16, so that the neural network can focus on the key information of the input data, further improve the ability of the grid to extract image data features, and realize the accurate expression of defect features.

Circumferential Damage Monitoring of Steel Pipe Using a Radar Map Based on Torsional Guided Waves

Ultrasonic guided wave technology has been successfully applied to detect multiple types of defects in pipes. However, the circumferential location and coverage of a defect are less studied because it is difficult to determine. In this study, the fundamental torsional mode T (0, 1) is selected to conduct monitoring of the circumferential defect in pipelines because of its almost non-dispersive property. A radar map of the peak wave signals at 30 circumferential positions is proposed to detect the damage. The circumferential defect of a steel pipe is thoroughly investigated using numerical simulation. First, the circumferential positioning of defects in various areas of the pipe is studied. Second, the results are compared to those based on longitudinal guide waves. Finally, the circumferential coverage of a defect in the pipeline is determined. The waves are excited and received using the pitch–catch approach, and the collected monitoring signals are processed using the Hilbert transformation. According to the findings, the circumferential defect in the pipe can be effectively identified from a ‘T’ shape in the radar image, and the monitoring method by the torsional guided wave is superior to the longitudinal wave method. The results clearly demonstrate the advantages of torsional guided waves in defect monitoring. The proposed method is expected to provide a promising solution to circumferential damage identification in pipelines.

Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines.

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Experimental Study on the Simulated Defects Detection in Submerged Transmission Pipeline

Abstract The transport of oil, natural gas, water, and other petrochemical substances is extensively supported by pipelines over long distances. However, long-term use without maintenance causes the residue in the pipeline to gradually settle in the pipeline due to physical or chemical action, and the pipeline becomes an accident. This leads to overpressure and leakages in the pipeline, which in turn affects the safety of the industry and people's lives. This study aims to develop a nondestructive inspection to measure defects in a water pipeline using an ultrasonic technique. An SCH80 carbon steel pipe with a standard thickness of about 11 mm was used for the experiments, and various sets of defects inside and outside of the pipe were simulated, such as holes and grooves. A submerged ultrasonic transducer was used to evaluate the simulated pipeline defects and then acquire the defect data in an imaging system using LabVIEW and origin software. In this way, the thickness and location of the defects were clearly located. In addition, the ultrasonic detection error was calculated to be less than 6.5%. It helps to use this technique and equipment for the inspection of underground fluid pipelines.

Adaptive Compressive Sensing: An Optimization Method for Pipeline Magnetic Flux Leakage Detection

Leakage from a submarine oil pipeline would have a great impact on the environment and ecological balance. Accurate detection of pipeline defects can ensure safety in the transportation of oil resources. The traditional detection optimization algorithm may lead to the absence of effective features. An adaptive compressive sensing image data augmentation method that analyzes the pixel distribution of small defect features has been proposed to solve these issues. On the basis of Focal-EIoU, a new box loss of Focal-GIoU is proposed which is suitable for pipeline defect detection. Furthermore, the incorporation of bi-level routing attention diminishes the reliance of Yolov5 on specific inputs effectively, thereby enhancing the generalization ability of the detection model. Comparative experiments show that compared with the conventional Yolov5 model, this method improves mAP50 and mAP50:95 by 6.4% and 15.1%, respectively, with mAP50 reaching 91.5% and mAP50:95 reaching 52.9%.

An investigation on the effect of widespread internal corrosion defects on the collapse pressure of subsea pipelines

Deepwater subsea pipelines are an essential part of offshore oil and gas infrastructure, and it is a statutory requirement for operators to monitor the degradation of pipelines left-in-place at cessation of production. Collapse is one of the failure modes for subsea pipelines subjected to external pressure. Surface corrosion defects in subsea pipelines, either while still operational or after cessation of production, usually manifest as widespread or localized corrosion pits that are randomly spread on the steel surface. Extensive work has been done on predicting the collapse pressure of pipelines containing single corrosion defects however, the same cannot be said for randomly distributed corrosion defects. In this study, we investigate, for the first time, the effects of the geometry and coverage extent of widespread internal corrosion defects with random spatial distribution on the collapse pressure of pipelines using finite element analysis. The results show that corrosion defect depth and coverage surface area are the main defect geometric parameters that significantly influence the collapse strength of pipelines with randomly distributed internal corrosion. An empirical function for predicting the collapse pressure of a corroded pipeline is developed to reduce the need for recurrent computational effort.