Pipeline Safety Research Articles

DESIGN OF A CAMERA-ENABLED MOBILE ROBOT FOR IN-PIPE INSPECTION

The role of pipeline infrastructure is crucial in modern society, as it transports necessary resources such as water, gas, and oil. It is essential to prioritize the maintenance of these pipelines to prevent leaks, environmental harm, and economic losses. Traditional inspection techniques, which involve manual inspection, can be inefficient, laborious, and susceptible to mistakes. To overcome these obstacles, this article introduces a camera-equipped mobile robot specifically created for automated crack detection in pipelines. The robot, which is equipped with a high-resolution camera and advanced image processing algorithms, navigates inside pipelines on its own to capture detailed images of the pipe walls. These images are then processed using computer vision techniques to identify and categorize potential cracks. The algorithms utilize machine learning models that have been trained on a dataset of labelled crack images, enabling accurate and efficient crack detection. This paper provides a comprehensive overview of the robot system's design, development, and validation, detailing the critical hardware components such as the mobility platform, camera system, and sensor integration, as well as the software architecture, which encompasses image processing and machine learning frameworks. Field studies demonstrate the robot's ability to effectively detect cracks across various pipe materials and under diverse environmental conditions. Results indicate that the automated system not only improves inspection efficiency and accuracy but also significantly reduces the need for human intervention, thereby enhancing overall pipeline safety Keywords: pipeline inspection, crack detection, mobile robot, image processing, computer vision.

On the evolution of root weld microstructure and properties during automatic girth welding of high strength pipeline steel

ABSTRACT Properties of root weld are important for service safety of pipelines, while microstructure and properties of root weld would alter due to heat affect of following welding passes. Evolution of root weld microstructure and properties during automatic girth welding of X80 pipeline was studied. Microstructure of the original root weld primarily consists of acicular ferrite (AF) and side-plate ferrite (SPF), and it is mostly affected by hot pass and first filling pass weld, after which SPF and AF coarsened significantly and some interlock ferrite combined with each other. During subsequent welding, no significant microstructural changes was found. However, impact toughness of root weld slightly increased after hot pass and first filling pass in spite of microstructure coarsening. In contrast, hardness of root weld notably dropped due to microstructure coarsening of hot pass and first filling pass, and then it was retrieved in following welding passes due to tempering effect.

Towards resilient pipeline infrastructure: lessons learned from failure analysis

Understanding the mechanisms of pipeline failures is crucial for identifying vulnerabilities in gas transmission pipelines and planning strategies to enhance the reliability and resilience of energy supply chains. Existing studies and the American Society of Mechanical Engineers’ (ASME) Code for Pressure Piping primarily focus on corrosion, recommending inspections every 10 years to prevent incidents due to this time-dependent threat. However, these guidelines do not provide comprehensive regulation on the likelihood of incidents due to other causes, especially non-time-dependent events (i.e. do not provide any indication of the inspection frequency or the most likely time for an incident to occur). This study adopts an innovative approach adopting machine learning, particularly artificial neural networks (ANNs), to analyse historical pipeline failure data from 1970 to 2023. By analysing records from the US Pipeline & Hazardous Materials Safety Administration, the model captures the complexity of various degradation phenomena, predicting failure years and hazard frequencies beyond corrosion. This innovative approach allows adopting more informed preventive measures and response strategies, offering deep insights into incident causes, consequences, and patterns. The results provide practical insights for maintenance planning, offering an estimation of periods when a pipeline may be more susceptible to incidents based on various factors. However, since all models inherently present uncertainties, both in the data and the modelling process, these estimates should be interpreted as probabilistic assessments. This study provides operators with a strategic framework to prescriptively address potential vulnerabilities, thereby promoting sustained operational integrity and minimising the occurrence of unexpected events throughout the service life of pipelines. By expanding the scope of risk assessment beyond corrosion, this study significantly advances the field of pipeline safety and reliability, setting a new standard for comprehensive incident prevention.

Study on Suitability Evaluation Method of Non-Metallic Seals in Long Distance Hydrogen-Doped Natural Gas Pipelines

Hydrogen doping using existing natural gas pipelines is a promising solution for hydrogen transportation. A large number of non-metallic seals are currently used in long-distance natural gas pipelines. Compared with metallic seals, non-metallic seals have the advantages of corrosion resistance, light weight, and easy processing, which can improve the safety and economy of pipelines. In order to ensure the long-term safe use of seals in hydrogen-doped natural gas pipelines, this paper selects the non-metallic seals commonly used in long-distance natural gas pipelines and carries out the hydrogen-doped sealing test, hydrogen-doped aging test, and hydrogen-doped anti-explosion test on the non-metallic seals under the conditions of different hydrogen-doped ratios. At the same time, combined with the actual working conditions of a hydrogen-doped natural gas pipeline, the external environment, and other factors, the applicability evaluation index system was established, and the applicability evaluation model based on hydrogen-doped physical and chemical properties, fuzzy comprehensive evaluation, and the structural entropy weight method was developed and applied in the field. The results show that the evaluation result of nitrile rubber in soft seals is 1.7845, and the evaluation result of graphite-polytetrafluoroethylene material in hard seals is 1.5988, and both of them are at a good level. This paper provides technical support and judging strategies for the selection of non-metallic sealing materials for hydrogen-doped natural gas pipelines.

Research on Safety of Aero-Engine Oil Pipe Under Heating Conditions Based on Fluid-Solid Thermal Coupling.

This paper examines the safety of aero-engine pipelines under different heating conditions. Based on the fire test standard documents, a model of an aero-engine oil pipe was constructed, and its safety under heating conditions that meet the standard was analyzed using fluid-solid thermal coupling. The pipe material was stainless steel 1Cr18Ni9Ti, and the oil inside the pipeline was China RP-3 kerosene. To simulate the different working conditions or pump failure scenarios, various kerosene inlet flow rates were used for the calculations. The results indicate that the pipe wall exhibits an uneven temperature distribution under standard heating conditions. As the kerosene flow rate decreases, the pipe wall temperature rises, and heat transfer deterioration occurs. The increase in the pipe wall temperature reduces the material's strength, while the uneven temperature distribution generates thermal stress, further increasing the safety risk. When the kerosene flow rate is reduced to a certain level, the equivalent stress in the pipe wall exceeds the material's yield strength, leading to a high risk of rupture.

Divisional intuitionistic fuzzy least squares twin SVM for pipeline leakage detection

Improving the accuracy of pipeline leak detection in noisy environments is significant for maintaining pipeline safety. Currently, the weighted support vector machine (SVM) is extensively employed. Among them, Intuitionistic fuzzy twin SVM (IFTSVM) has good robustness due to the integration of intuitionistic fuzzy set theory. However, IFTSVM does not fully consider the position information of samples in the feature space, cannot reduce the impact of heterogeneous clustering noise. Moreover, it requires solving two quadratic programming problems (QPPs), which is inefficient. To address the aforementioned issues, this paper proposes a divisional intuitionistic fuzzy least squares twin SVM (DIFL-TSVM) for pipeline leakage detection. This approach proposes a novel divisional intuitionistic fuzzy membership calculation strategy to reduce the impact of heterogeneous clustering noise and simplify the calculation of sample membership. In addition, DIFL-TSVM transforms the original QPPs into two linear equations using the least squares method to improve the efficiency of model. This paper verifies that the DIFL-TSVM model not only has the ability to eliminate the effects of discrete noise and heterogeneous clustering noise but also more efficiency on water supply pipeline leakage diagnosis experiments and UCI datasets. The experimental results show that DIFL-TSVM has higher leak identification with a detection accuracy of 95.6 %.

Research Progress on Novel Inspection of Oil and Gas Pipeline Integrity

Oil and gas pipeline integrity is the key to ensure the safety of energy transportation, with the development of science and technology, the research and application of new detection technology has been increasingly emphasized. This paper firstly describes the background and practical significance of the selected topic, aiming at exploring more efficient pipeline inspection methods to ensure the stable operation of energy networks. In terms of theoretical framework, we have sorted out the theoretical system of integrity management, including the core elements of risk assessment, inspection technology, and maintenance strategy, which provides a theoretical basis for the subsequent research. Then, we systematically review the latest progress of oil and gas pipeline integrity testing at home and abroad. From the dimensions of physical principle and application practice, we analyze the advantages and disadvantages of conventional ultrasonic and magnetic particle flaw detection, as well as the emerging acoustic emission and fiber grating inspection technologies, and show the application cases of each technology in actual projects. Through comparative analysis, we find that although conventional technologies meet the inspection needs to a certain extent, new inspection technologies such as remote monitoring based on the Internet of Things (IoT) and artificial intelligence-assisted diagnosis are gradually becoming a research hotspot due to their high precision, high efficiency and intelligent features. The development of these technologies not only improves the comprehensiveness and accuracy of inspection, but also provides strong support for realizing the whole life cycle management of pipelines. In the summary review section, we emphasize the future development trend of oil and gas pipeline integrity inspection, including the integration and intelligence of technology, as well as the deep mining and application of data. At the same time, we also point out the challenges in the current research, such as the unification of inspection standards and the establishment of data sharing mechanisms, which require the joint efforts of the research community and the industry to achieve the continuous improvement of oil and gas pipeline safety. To summarize, this paper aims to comprehensively show the research progress of new detection technology of oil and gas pipeline integrity, provide reference for researchers and engineering practitioners in related fields, and promote the further improvement of China's oil and gas pipeline safety management level.

Real-time detection of urban gas pipeline leakage based on machine learning of IoT time-series data

China is advancing urban gas pipeline safety through the strategic deployment of methane detectors. This study introduces a systematic approach for identifying underground gas leaks, and overcoming challenges such as biogas interference and environmental factors. Abnormal methane sequences, defined by expert experience, undergo data preprocessing, including cleansing, compressing, and extracting rising segments. Feature engineering is performed on these rising segments across three dimensions: temporal, volatility, and temperature features. Different classification algorithms, including K-nearest neighbor, decision tree, and support vector machine, are employed for gas leakage recognition. The decision tree proves most effective, achieving a 92.86% recall rate for methane leakage segments. Additionally, feature importance ranking from the decision tree model highlights the maximum value of preprocessed methane concentration time series and the fitting slope of the rising segment as crucial features for methane leakage detection.

Evaluation of Cumulative Damage and Safety of Large-Diameter Pipelines under Ultra-Small Clear Distance Multiple Blasting Using Non-Electric and Electronic Detonators

The safety assessment and control of large-diameter pipelines under tunnel blasting at ultrasmall clear distances is a significant problem faced in construction. However, there has been no reference case for the quantitative comparison of the disturbance degree of surrounding rock by using two blasting schemes of non-electric detonator design and electronic detonator design under a similar total blasting charge consumption. In this study, the blasting test was carried out based on the engineering background of drilling and blasting methods to excavate the tunnel under the water pipeline at a close distance. The peak particle velocity (PPV), stress, and deformation responses of the pipeline under the two construction methods of non-electric and electronic detonators were comparatively analyzed. The PPV can be remarkably reduced by 64.2% using the hole-by-hole initiation of the electronic detonators. For the large-diameter pipeline, the PPV on the blasting side was much larger than that on the opposite side because the blasting seismic wave propagated a longer distance and attenuated more rapidly, owing to its greater cavity vibration reduction effect. The PPV of the electronic detonators decayed more slowly than that of the non-electric detonators. The cumulative damage caused by consecutive hole-by-hole blasting using electronic detonators was less than that caused by simultaneous multi-hole initiation using non-electric detonators, with a reduction of about 50.5%. When the nearest peripheral holes away from the pipeline are detonated, the cumulative damage variable D and damage range increase rapidly. The PPV, dynamic tensile strength, and cumulative damage variables were used to evaluate the safety of the pipelines.

Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis

Gas pipelines are buried and installed across rivers to supply the gas necessary for daily life. When crossing rivers, gas pipelines are typically installed on bridges; however, when installation on bridges is not feasible, the pipelines are buried in riverbeds. This study utilized both a one-dimensional model (HEC-RAS) and two-dimensional models (SMS) to simulate river flow and estimate the potential for scour and deposition around buried pipelines. The hydrodynamic simulations considered critical factors, including sediment transport, river discharge, and geological characteristics, to derive the maximum scour depth and assess the risk of pipeline exposure. The findings from the long-term and short-term simulations confirmed that riverbed changes due to natural hydrological events do not exceed the minimum burial depth standards, thereby ensuring pipeline safety. In addition, the study proposed specific reinforcement measures tailored to local site conditions, addressing concerns of continuous subsidence and ensuring long-term structural stability. This research offers important insights into pipeline risk management and contributes to the development of more effective regulatory standards for gas pipelines buried in riverbeds, enhancing both their safety and environmental sustainability

Study on fracture behaviour of pipeline girth welds based on curved wide plate specimens: Experimental and numerical analysis

Understanding the fracture characteristics and strain capacity of pipelines with girth welds under external loads is crucial for evaluating pipeline safety. The use of curved wide plate (CWP) specimens closely resembling full-scale (FS) pipelines in geometry provides a more realistic representation of crack tip constraint states compared to small-size fracture toughness test specimens in laboratory settings. This study aims to investigate the ductile fracture characteristics of center cracks on the outer surface of girth welds in high-grade steel pipelines under external loads through large-scale CWP tensile tests and advanced numerical simulations. Tensile tests were conducted on CWP specimens using a kiloton tensile testing machine, with double crack opening displacement (COD) gauge monitoring crack mouth opening displacement (CMOD), and high-precision displacement sensors and strain gauges tracking deformation and strains at various positions. The study yielded significant experimental results, and a finite element (FE) model of the CWP was developed using the nonlinear finite element method (FEM) to validate the experimental data against simulation results. The FE model was then used to investigate the influence of material properties on crack propagation resistance and driving force curves. A method for determining the ultimate tensile strain capacity (UTSC) of pipelines based on actual material fracture toughness was proposed. This research contributes valuable experimental data for further exploration of fracture behaviour in large-scale pipeline girth welds and offers insights for safety evaluations of such welds.

Automated matching and visualisation of magnetic flux leakage data in shale gas pipeline based on ICP and DBSCAN algorithm

ABSTRACT Magnetic flux leakage (MFL) is a non-destructive testing method for shale gas pipeline safety monitoring. Despite many pipelines have completed two or more rounds of internal inspection and have accumulated substantial data, effectively analyzing and utilizing this MFL data remains a significant challenge. To that end, this study proposes a novel combined model that integrates Iterative Closest Point (ICP) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) fusion to match multiple MFL data. The model outputs the matching result and visualisation images of MFL data acquired in different years. Furthermore, RMSE (root mean square error) and overlap rate have been used to evaluate the model. The results indicate that the average distance error of matched defects between two datasets decreased by 72.5%, and the overlap rate increased by 20%. Additionally, the DBSCAN Fusion shows better computational efficiency with an increase in defect quantity within the pipe segments. Finally, the impact of different datasets on the model’s matching accuracy is discussed in this study. The findings show that small-scale datasets or higher false detection rates lead to decreased accuracy. The proposed model fully leverages MFL data to achieve rapid matching of defects, offering a dependable and effective technical solution for safety monitoring and corrosion prediction in shale gas pipelines.

Digital Twin Model Study of Piping Acoustic Induced Vibration Risk

Abstract The research and calibration of digital twin models are crucial for developing an intelligent monitoring system for pipeline acoustic induced vibration. This study addresses the rapid assessment of acoustic induced vibration risks during the design stage using a digital twin model. First, the likelihood of failure (LOF) due to acoustic induced vibration was calculated based on EI guideline, providing a quantitative risk assessment. Concurrently, numerical simulations were conducted to validate these results. The findings show a high consistency between the two methods, indicating their complementary and validating roles. Therefore, the integrated use of methods based on EI guidelines and numerical simulation provides robust technical support for early-stage assessment of acoustic induced vibration risk in pipeline design, enhancing both accuracy and reliability of the evaluation, and laying a solid foundation for pipeline design and safety assurance.

Study on low temperature and narrow heating bands of post weld heat treatment for girth welds stress reduction of long-distance pipelines

Residual stress affects the service safety of pipelines, but there are few efficient methods to reduce the residual stress, especially for long-distance pipelines with large diameters. In this paper, a Secondary Post Weld Heat Treatment (S-PWHT) stress reduction method with low temperature and two narrow heating bands is proposed, and this method fully considers the failure of the corrosion resistance layer caused by high temperature. The temperature distribution and stress distribution under different S-PWHT parameters are compared to obtain the optimal S-PWHT parameters using finite element numerical simulation. The thermocouples are conducted to verify the temperature, and the coercivity experiment is used to verify the stress distribution. Results show that two heating bands width of 80 mm and a peak temperature of 120°C are the optimal S-PWHT parameters with the stress reduced by 61%. Besides, the levels of stress reduction on the inner surface and the HAZ are higher than those on the outer surface and the base metal, respectively. The high-stress zone is transferred from the inner surface to the outer surface, and from the HAZ to the base metal. Furthermore, the stress reduction mechanism of S-PWHT has been revealed through constraint theory. Temperature change is the initial force, and the constraint is the driving force for stress reduction.

Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models

Accurate burst pressure prediction is critical for ensuring oil and gas pipeline safety, guiding maintenance decisions, and lowering costs and risks. Traditional methods have limitations, including high experimental costs, conservative empirical models, and computationally expensive numerical algorithms. Machine learning (ML) models have supplanted traditional methods in recent years. However, small and imbalanced datasets are the big challenge to build a ML model that can generate more accurate results. Moreover, the lack of generalization in ML models trained on a dataset of pipelines with specific material grids prevents them from producing superior results on other pipeline types. First, FEA was used to make a dataset. Then, a new way to improve machine learning (ML) model generalization for burst pressure prediction is suggested: combine publicly available datasets of different pipeline specifications. In this combined dataset, some pipelines have a higher number of data samples, and some have fewer, which causes a class imbalance issue. The Synthetic Minority Oversampling Technique (SMOTE) technique was applied to address the issue of class imbalance. The performance of various ML models, Extra Trees (ET), Extreme Gradient Boosting (XGBR), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Decision Tree (DT), was evaluated to validate the model's prediction and generalization on pipelines of various material grids. Results show that all the selected ML models produced high R-squared, i.e., >0.95, on balanced data compared to the imbalance dataset. These results show that SMOTE-based augmentation is a beneficial way to fix dataset imbalance and make ML models better at predicting burst pressure in oil and gas pipelines.

Applied and expert research on fire and industrial safety of main pipelines

The article provides an analytical assessment of the consequences of violations of industrial and fire safety rules during repair work on the example of main oil pipelines. The issue is urgent due to explosions and fires, as well as staff injuries, which requires an analysis and assessment of the causes of their occurrence and reliability improvement during repair work at hazardous production facilities in Irkutsk region. The factual basis of the study is data on incidents related to the personnel training and repair sites, explosions (pops), the dynamics of the emergency situation and consequences for personnel and the economy. The study concludes that in terms of eliminating explosions and fires and minimizing their consequences, the industrial and fire safety system has not been properly developed for repair and other works at hazardous production facilities.

Differentiating three distinct kinds of flaws in oil and gas pipelines derived from the spacing effect of capacitive imaging sensors

Purpose This paper aims to apply the spacing effect of capacitive imaging (CI) sensors to inspect and differentiate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module in oil and gas pipelines simultaneously. Through experimental verification, a method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors has been demonstrated. Design/methodology/approach A 3Dimensions (3D) model for simulating the inspection of these flaws was established by using COMSOL. A novel CI sensor with adjustable working electrode spacing was designed, and a modular CI system was developed to substantiate the theoretical findings with experimental evidence. A method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors was established. Findings The results indicate that the method can successfully discriminate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module using CI sensors. Originality/value The method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors is vital for keeping the transportation safety of oil and gas pipelines.

Prediction of the internal corrosion rate for oil and gas pipelines and influence factor analysis with interpretable ensemble learning

Corrosion is one of the major threats to the safety and reliability of oil and gas pipelines, making accurate prediction of corrosion rate crucial for pipeline maintenance and repairment. Traditional prediction methods often ignore more critical factors and lack interpretability, which hinders the practical application. Here, an interpretable ensemble machine learning framework is proposed, not only improving prediction performance, but also enhancing interpretability for predicting the internal corrosion rate of oil and gas pipelines. In this work, ExtraTreeRegression model has demonstrated superior prediction accuracy relative to the other five machine learning models, and the determination coefficient of the ExtraTreeRegression model achieves 0.93 after feature engineering. Then, Shapley Additive exPlanations (SHAP) values is utilized to visually interpret the model locally and globally to help account for the contributions of the input features. Furthermore, the accumulated local effect (ALE) successfully explains how the features affect the internal corrosion rate of oil and gas pipelines. By collecting corrosion data of oil and gas pipeline and performing feature engineering and data preprocessing, we construct a comprehensive and reliable prediction model with interpretability. Experimental results demonstrate that the proposed interpretable ensemble machine learning approach outperforms other models in both accuracy and interpretability, providing valuable insights for pipeline management decisions.

Effect of pre-strain on thermal aging of austenitic stainless steel weld metal

Austenitic stainless steel welds (ASSWs) suffer from severe pre-strain during assembly process, which threatens the long-term operation safety of pipelines in nuclear power plant. In this work, the 316L weld metals (WMs) with 0 % and 8 % pre-strain were thermally aged at 400 °C for up to 39000 h to investigate the pre-strain effect on thermal aging of ASSWs. The results showed that the pre-strain caused work hardening and further promoted the hardening of thermally aged ferrite. After thermal aging for 39000 h, the nano-hardness increment of ferrite with 8 % pre-strain was about 1.6 GPa more than that without pre-strain. By microstructure characterization, it is found that the high dislocation density induced by pre-strain promoted spinodal decomposition and G-phase precipitation. The spinodal decomposition morphology and corresponding element concentration fluctuations were more obvious in the WM with 8 % pre-strain. Moreover, the size and density of G-phase along dislocations were larger in the ferrite with 8 % pre-strain than those without pre-strain.

A review of recent advances and applications of inorganic coating for oil and gas pipe systems

The oil and gas pipe systems are a crucial part of the infrastructure. The structural integrity of pipes is diminishing as a result of environmental damage. In recent years, the oil and gas industry has witnessed significant advancements in the development and application of inorganic coatings for pipeline systems. These coatings are essential for enhancing the durability, safety, and efficiency of pipelines by providing superior resistance to corrosion, abrasion, and chemical attacks. This review paper delves into the latest fabrication strategies for API pipe coatings, with a focus on the electroless method, electrodeposition method, thermal spray method, thermochemical method, and other emerging techniques. Each method is evaluated in terms of its principles, advantages, limitations, and suitability for different operational environments. Among the coating methods, the electroless process is one of the most popular methods. The paper also highlights innovative approaches and materials that have the potential to revolutionize the field. By providing a comprehensive overview of current technologies and their applications, this review aims to guide future research and development efforts, promoting the adoption of advanced inorganic coatings in the oil and gas industry to ensure the longevity and integrity of critical infrastructure.