Pipeline Safety Research Articles

Effect of temperature rise on the mechanical behaviour of deep-sea clay surrounding oil and gas pipelines

The mechanical behaviour of deep-sea clay is crucial for oil and gas pipeline safety and is traditionally assessed through in situ penetration testing, which is usually conducted in a low-temperature environment (usually at temperatures lower than 4 °C). However, the temperature of deep-sea clay surrounding high-temperature pipelines can reach approximately 20 °C. To date, the influence of temperature variation on the mechanical properties of deep-sea clay, especially strain-softening behaviour, has been largely overlooked, posing a safety risk to practical engineering. Consequently, this study first establishes a temperature-controlled cyclic penetration testing platform. Then, an enhanced mini-spherical penetrometer is employed to investigate the undrained strength and strain-softening behaviour of undisturbed deep-sea clay samples at 4 °C, 10 °C and 20 °C. In particular, an improved framework is proposed to quantitatively characterize the mechanical behaviour. Finally, a strain-softening law considering temperature variations is established and rigorously validated through in situ tests. The findings underscore several significant observations: (a) deep-sea clay in its intact state exhibits a heightened sensitivity to temperature variations compared to that in its remoulded state; (b) temperature variations influence the strain-softening parameters of deep-sea clay, resulting in distinct behaviours; and (c) the proposed strain-softening law considering temperature variations is verified to be more logical.

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

Dynamic response and performance assessment of subsea sandwich pipelines with different interlayer subjected to underwater explosion

The sandwich pipelines have been developed and applied in subsea due to their better resistance than single-layer pipelines. Explosive shock loads can cause fatal damage to subsea pipelines, thus the objective of this paper is to systematically evaluate the safety of subsea sandwich pipelines under explosion loading. The uniqueness of this study is to analyze the dynamic response of sandwich pipeline filled with different interlayer materials subjected to underwater explosion, especially considering the fluid-solid interaction (FSI) effect. Based on the Arbitrary Lagrangian-Eulerian (ALE) algorithm, the propagation of the blast shock wave in sea water and the FSI effect with the pipeline were investigated by using LS-DYNA code. The accuracy of the numerical method was verified, and then the evolution of the explosive pressure in sandwich pipes with different interlayer material was simulated. The energy absorbing capacity of each layer was analyzed. The structural dynamic response and deformation of the subsea sandwich pipelines was investigated by considering the influencing factors including the diameter-to-thickness ratio (λ) of the inner pipe and outer pipe, the interlayer material, and the explosive charge (Me). The results show that with the increase of λ and Me, the ovality of the pipe increase correspondingly. Moreover, the results demonstrate that with different material as the interlayer, each layer of the sandwich pipe shows different ability to absorb the internal energy caused by explosive shock wave, thus the deformation of the pipe differs significantly. This study can provide a reference for the optimized design of subsea sandwich pipelines to support the operational safety of deep-sea pipeline systems.

Temporal dynamics of urban gas pipeline risks

Urban gas pipelines pose significant risks to public safety and infrastructure integrity, necessitating thorough risk assessment methodologies to mitigate potential hazards. This study investigates the dynamics of population distribution, demographic characteristics, and building structures to assess the risk associated with gas pipelines. Using geospatial analysis techniques, we analyze population distribution patterns during both day and night periods. Additionally, we conduct an in-depth vulnerability assessment considering multiple criteria maps, highlighting areas of heightened vulnerability in proximity to gas pipelines and older buildings. This study incorporated the concept of individual risk and the intrinsic parameters of gas pipelines to develop a hazard map. Hazard analysis identifies areas with elevated risks, particularly around main pipeline intersections and high-pressure zones. Integrating hazard and vulnerability assessments, we generate risk maps for both day and night periods, providing valuable insights into spatial risk distribution dynamics. The findings underscore the importance of considering temporal variations in risk assessment and integrating demographic and structural factors into hazard analysis for informed decision-making in pipeline management and safety measures.

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.

Simulation of defect detection for the buried petroleum pipe by the X-ray backscatter imaging

Ensuring the integrity, safety, and compliance of oil pipelines is crucial for operators. Two significant factors affecting pipeline integrity are metal loss due to corrosion or air gouging and the presence of cracks. This paper introduces an X-ray backscatter nondestructive testing model on a smart pig(an intelligent robot) for internal inspection of petroleum pipelines. The paper investigates the X-ray source and detection system, essential components of the backscatter imaging technology, to provide technical guidance for model design. The imaging system’s layout is modeled using GEANT4, and its performance in detecting defects of various formats on pipe walls is studied. The research demonstrates the feasibility of using X-ray backscatter imaging for pipeline inspection and provides insights into different defect detection. The findings contribute to designing and optimizing the robot’s scanning head for effective pipeline inspection.

Strategic Analysis of Past and Modern Hydrogen Storage Methods and Prospects for Its Future

This research report provides a comprehensive strategic analysis of past and modern hydrogen storage methods and prospects for its future. A literature review was conducted to identify the most promising materials and technologies for hydrogen storage and to compare their properties to current storage methods. The findings suggest that solid-state storage materials, such as metal-organic frameworks (MOFs) and porous carbon materials, show great promise due to their high storage capacities and favorable thermodynamic properties. However, technical challenges such as slow kinetics and low durability need to be addressed to achieve practical application. Furthermore, the integration of hydrogen storage into existing energy infrastructure presents technical and regulatory barriers that need to be overcome, such as pipeline compatibility and safety regulations. Overall, this research sheds light on the prospects and challenges of hydrogen storage and provides insights for future research and development in this field.

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

Localizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing

Ensuring the safe transportation of energy relies heavily on the timely safety inspection and pigging of energy pipelines using in-pipe inspection robots known as Smart/Intelligent PIGs. Addressing the potential risks of PIG block incidents and the limitations of excessive speed in the maintenance, real-time localization and tracking of these inspection robots has become imperative. However, traditional localization and tracking methods present a challenge due to their resource-intensive nature, requiring significant manpower and resources. To overcome this limitation and enhance the intelligence of the robot operation monitoring, this paper proposes an innovative artificial intelligence (AI) integration algorithm framework based on distributed optical fiber sensing (DOFS) for real-time localization and tracking of robots. It adopts noise reduction and reconstruction techniques in signal processing, effectively enhancing the quality of optic fiber vibration signals. Notably, the integration of two distinct features that complement each other enables dual verification of tracking detection, ultimately bolstering the system’s effectiveness and trustworthiness. Besides, a logical reasoning-based localization decision strategy further enhances the system’s capabilities, allowing for controlled tracking intervals and step sizes that can be tailored to meet the task requirements under diverse working conditions. The collaboration of three modules makes it feasible to monitor dynamic changes of the detection robot in the pipeline along the direction of operation. The experimental results convincingly demonstrate that the integrated framework possesses several key advantages of remarkable robustness, real-time performance, and minimal error. It underscores the system’s potential to ensure energy pipeline safety efficiently and effectively.

RHiREM: Intelligent diagnostic framework for pipeline Eddy Current Internal Inspection based on reinforcement learning with hierarchical reward exploration mechanism

Pipeline safety is of paramount importance for socio-economic development, necessitating regular inspection and maintenance. The complexity of the internal pipeline environment and the presence of irregular noise in detection data, however, pose significant challenges to pipeline inspection technologies. Additionally, the reliance on manual expertise for inspection and the absence of standardized assessment criteria further complicate the development of automated inspection methods in this context. To address these challenges, this paper proposes a pipeline anomaly detection framework based on reinforcement learning with hierarchical reward exploration mechanism (RHiREM). It includes two main aspects: Firstly, the hierarchical reward mechanism. By deeply simulating the process of defect recognition based on expert personal experience, the original pipeline data is first adaptively divided into sets of windows with different sizes, and then attribute and type judgments are performed on them. In this way, the approach achieves accurate identification of defects and pipeline structures in scenarios with minimal noise interference. Secondly, the hierarchical exploration mechanism. By leveraging the temporal exploration and spatial exploration, the mechanism enables further deep search and feature learning on complex pipeline signals, and facilitates comprehensive assessment of the relationships between global features and local features across different signals, effectively resolving the difficulties associated with identifying defect signals in the presence of high noise interference. the proposed framework has been demonstrated to automate the detection of complex on-site pipeline internal signals and successfully detected the common anomalies with high F1-score over conventional techniques.

Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning

This paper presents a novel methodology utilizing machine learning (ML) techniques to accurately forecast the failure pressure of corroded pipelines, which are pivotal for oil, gas, and petroleum product transport. In contrast to prior approaches, this study overcomes their limitations by integrating physically significant factors tied to corroded pipeline failure mechanisms and delivering interpretable models. Robust ML models are constructed using dependable experimental data from pertinent literature sources, ensuring research rigor. To bolster model interpretability, the SHAP method is deployed, facilitating a comprehensive grasp of each feature's impact on predictions. The performance and efficacy of the developed ML models are rigorously assessed using real-world pipeline incidents reported by the PHMSA in the US, further validating their practical utility. The findings not only affirm the models' precision and dependability but also yield valuable insights, underscoring their importance. This study makes a substantial contribution to the enhancement of pipeline integrity management by enabling precise failure pressure predictions in corroded pipelines. The insights gleaned from this research hold practical implications, enabling well-informed choices in pipeline design, integrity management, and risk assessment. Thus, it stands as a valuable resource for pipeline operators aiming to optimize resource allocation and bolster overall pipeline safety and efficiency.

Effect of Microstructure on Mechanical Properties of X65MS Welded Pipe Joint

The mechanical properties of welded joints have a crucial impact on the safety of pipelines. This article focuses on examining the correlation between the microstructure and mechanical properties of welded joints of X65MS pipeline steel for sour service. The results indicate that the welding thermal cycle forms the microstructure consisting of acicular ferrite, granular bainite, and polygonal ferrite. The hardness across the joint ranges from 210 and 270 HV, peaking within the welded zone and decreasing towards the base metal (BM). At −40 °C, the welded zone shows the lowest impact energy of 53 J, while heat‐affected zones exhibit superior impact toughness exceeding 270 J with the highest impact energy of 326 J. This can be attributed to the exceptionally fine‐grained microstructure, a small quantity of small‐sized martensite‐austenite (M/A) constituents in the heat affected zone. As for the low toughness of the weld, the main factors can be attributed to the presence of large‐sized elongated M/A constituents and inclusions.

Hydraulic conveying characteristics of particles in bend based on numerical simulation and explainable stacking machine learning model

As a hydraulic lifting pipeline structure widely used in deep-sea oil, gas transportation, and sediment dredging projects, the pipeline configuration is related to the improvement of transportation efficiency and pipeline safety. Particularly, the bending section consumes the most energy and withstands severe erosion. Understanding and predicting the conveying characteristics of two-phase flow in bends is therefore crucial. In this study, CFD-DEM (computational fluid dynamics-discrete element method) simulation method is employed to calculate various cases, considering five parameters: pipeline bending radius and angle, conveying velocity, particle diameter, and concentration, to explore the influence of these parameters on pressure drop and erosion rate of pipeline and result in a data set of hundreds of cases. Based on this data set, seven machine learning models are trained to predict pressure drop and erosion rate, respectively. To enhance model accuracy, the stacking method in ensemble learning is employed to combine multiple models with good performance. Additionally, the Optuna and SHAP (SHapley Additive exPlanation) methods are utilized to optimize hyperparameters and explain the degree to which parameters impact the predictions. The result demonstrates that pressure drop is almost unaffected by bending radius, while erosion rate initially decreases and then increases with bending angle, and both increase with other parameters. Among the evaluated models, artificial neural network, XGBoost, and random forest all demonstrate high prediction accuracy. The stacking model further improves the accuracy, with mean absolute error improving by 21.7% and 32.2%, and the SHAP method demonstrated good interpretability, which is basically consistent with CFD-DEM results.

A Data Driven Machine Learning Model for Fault Detection in Industrial Pipelines

Industry 4.0 has marked a paradigm shift in the way production and manufacturing operates. Process monitoring and fault diagnosis are important for the safety and reliability of industrial processes especially for smart manufacturing. As a data-driven process monitoring methodology, multivariate statistical analysis techniques, such machine learning based approaches have become extremely critical for automation. Pipelines carrying oil and gas are essential for a nation's economic sustainability. In order to maximise their function and prevent product losses during the transportation of petroleum products, they must be carefully inspected. However, they are susceptible to failure, which could have negative effects on the environment, financial loss, and safety. Therefore, evaluating the pipe's state and quality would be crucial. Despite being time-consuming and expensive, a number of inspection procedures are used to assure the safety of pipelines. However, due to the time consumption and error prone nature of manual inspection, data driven models are being explored for forecasting failure in oil and gas pipelines. The proposed work presents a Bayesian Regularized classifier for forecasting failure and the results show that the proposed approach outperforms the existing baseline techniques in terms of forecasting accuracy. Keywords:- Industry 4.0, oil pipelines; failure forecasting, Bayesian Regularization, Confusion Matrix, Classification Accuracy.

Image enhancement method for low-light pipeline weld X-ray radiographs based on weakly supervised deep learning

X-ray inspection is the most intuitive approach for the non-destructive testing (NDT) of pipeline weld defects to avoid pipeline safety accidents. However, identifying pipeline weld defects in dark X-ray images is difficult due to low greyscale values. This paper proposed a weakly supervised network for denoising and enhancing low-light pipeline weld X-ray images. First, a semi-supervised network based on an improved Retinex-Net which implemented by self-paced learning was proposed to enhance illumination, yielding more natural X-ray images without artifacts, distortion, and overexposure. A new denoising network constrained by the X-ray images themselves was designed to achieve denoising while preserving the image detail. Qualitative comparison and quantitative analysis indicated that the proposed method outperformed other industrial image enhancement methods used for pipeline weld detection in terms of both subjective visual effects and objective metric values.

Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

Pipeline integrity and safety depend on the detection and prediction of stress corrosion cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion-monitoring techniques are used. The observed data exhibit characteristics of nonlinearity, multidimensionality, and noise. Hence, data-driven modeling techniques have been widely utilized. To accomplish intelligent corrosion prediction and enhance corrosion control, machine learning (ML)-based approaches have been developed. Some published papers related to SCC have discussed ML techniques and their applications, but none of the works has shown the real ability of ML to detect or predict SCC in energy pipelines, though fewer researchers have tested their models to prove them under controlled environments in laboratories, which is completely different from real work environments in the field. Looking at the current research status, the authors believe that there is a need to explore the best technologies and modeling approaches and to identify clear gaps; a critical review is, therefore, required. The objective of this study is to assess the current status of machine learning’s applications in SCC detection, identify current research gaps, and indicate future directions from a scientific research and application point of view. This review will highlight the limitations and challenges of employing machine learning for SCC prediction and also discuss the importance of incorporating domain knowledge and expert inputs to enhance the accuracy and reliability of predictions. Finally, a framework is proposed to demonstrate the process of the application of ML to condition assessments of energy pipelines.

Underground natural gas storage facility operations and well leakage events in the United States

Federal minimum safety standards for underground natural gas storage (UNGS) operations in the United States (U.S.) were set by the Pipeline and Hazardous Materials Safety Administration (PHMSA) in the aftermath of the 2015 Aliso Canyon well leakage incident. Beginning in 2017, these standards required operators of U.S. UNGS facilities to publicly report: 1) leakage events that meet established criteria for incidents; 2) annual details of well construction, maintenance, and integrity testing activities; and 3) the occurrence of leading indicators of leakage (i.e., safety-related conditions). We reviewed both PHMSA reports and previously published studies to compile a dataset of 53 well leakage events at UNGS facilities in the U.S. that align with the PHMSA reporting requirements. Casings (55.0%) and wellheads (32.5%) were the most common failed well components identified. Human intervention through incorrect operation or outside force damage was the most common (39.3%) cause of well leakage events. Well leakage events caused by corrosion resulted in significantly greater emissions than those with other causes. UNGS annual reports detailed operations at 406 UNGS facilities with 17,993 wells—14,469 were injection and the remainder monitoring. Of these wells, 11,446 (63.6%) were reported to have a design that is potentially susceptible to a single point of failure, which is more than three times the previous estimate. Additional information about the status and construction of individual wells is needed to confirm the number of operational wells with a single-point-of-failure design. These data would help regulators identify single-point-of-failure wells and ensure the elevated risks associated with them are appropriately mitigated by proposed risk management plans. Statistically significant differences in the characteristics of UNGS facilities that have and have not experienced well leakage events were observed among those operating in depleted hydrocarbon reservoirs. Depleted hydrocarbon UNGS facilities that experienced well leakage events were generally larger, which suggests that focusing regulatory inspections and reviews on these facilities may be justified. The UNGS data reported were not sufficient to build a predictive model that forecasts well leakage events. Modeling efforts were limited by the data reporting format, which requires UNGS operators to aggregate data by facility in their annual reports. Gathering and evaluating information for individual wells at UNGS facilities would be valuable and may provide additional insights into well leakage risks to better inform risk management planning efforts.

Environmental safety of main gas pipelines

Analysis of the causes of accidents indicates the prevailing influence of corrosion factors on the structural reliability of main gas pipelines. During operation, main pipelines are exposed to the in-pipe pressure of the pumped product and ground electrolytes, which leads to corrosion-mechanical damage. Moreover, these factors can act in the most unfavorable combinations. This combined action reduces the durability of the material. This leads to much faster destruction of the pipe metal. Therefore, it is necessary to monitor not only general corrosion damage, but also local damage, which, when subjected to mechanical stress, can become a source of corrosion crack. The article discusses the main factors influencing the environmental safety of main gas pipelines, as determining the required level of quality at the time of operation.

Research on organic polyacid system suitable for water injection wells in low permeability reservoirs

Abstract Currently, the pollution and clogging problems of injection wells in low-permeability reservoirs are becoming more and more serious, leading to a series of issues, such as the decline of fluid production in wells and the increase of pressure in injection wells. To cope with these challenges, SY organic polyacid solution was developed in this paper and its dissolution efficiency was thoroughly investigated. The experimental results show that SY organic polyacid solution has a high dissolution rate of 88.7%, which exhibits excellent scale dissolution effect, and the solvent's dissolution has a good slow-release performance to ensure the long-lasting action time. At the same time, the corrosion rate of the solvent on the N80 steel sheet is only 0.6291 g/m²-h, much lower than the industry standard, which effectively protects the safety of the construction pipeline and production column. In addition, the dissolution rate of SY organic polyacid on clay reaches 10.14% in five minutes, indicating that it prevents the ground collapse caused by excessive dissolution of clay and has good profound penetration ability. In summary, using SY organic polyacid solution can effectively solve multiple problems such as reservoir mud, inorganic scale, organic scale and water injection fouling oil, which provides an effective solution for the contamination and clogging of water injection wells in low permeability reservoirs.

Mutual Supervision of MFL Heterogeneous Signals for Insufficient Sample Defect Detection on Pipeline Safety Operation

Mutual Supervision of MFL Heterogeneous Signals for Insufficient Sample Defect Detection on Pipeline Safety Operation