Search Results (564)

Energy price shocks have been the main drivers of inflation in Europe during the last decade. This paper examines cross-country differences in the transmission and magnitude of energy supply shocks in the Visegrád Group countries (Poland, Czechia, Hungary, and Slovakia) over the period 2015–2026. To identify energy-related disturbances and evaluate their macroeconomic effects, we estimate Bayesian Structural Vector Autoregressive (BSVAR) models with sign restrictions and complement the analysis with Local Projections. The results indicate substantial heterogeneity in inflation responses across countries. Oil supply shocks increase inflation by approximately 0.8 percentage points in Czechia and 0.7 percentage points in Hungary, compared with around 0.5 percentage points in Poland and Slovakia. Similar response patterns are observed for gas supply shocks, suggesting that stronger commodity price pass-through contributes to the larger inflationary effects in Czechia and Hungary. The analysis also reveals methodological challenges in identifying the effects of increased LNG imports. Both models indicate LNG-related shocks are highly correlated with pipeline gas supply shocks. In fact, its imports have mitigated energy shortages and reduced price pressures. Full article

Magnetic leakage detection is one of the most commonly used methods for pipeline inspection, which mainly uses magnetic sensors to detect the magnetic leakage field on the internal and external surfaces of the pipeline to determine whether there are defects in the pipeline. The defect quantification algorithm includes a forward model and an optimization algorithm, in which the estimation of target defects using optimization algorithms is one of the key aspects of defect inversion. Most of the existing optimization algorithms are based on particle swarm algorithms (PSOs) and genetic algorithms (GAs), which are prone to premature problems and have low convergence accuracy. To address the problems in the process of defect inversion, this paper proposes a new inversion algorithm, which obtains part of the prior knowledge from the application context of defect inversion, and adopts the decay oscillation function as the nonlinear convergence factor based on the whale optimization algorithm (WOA). In addition, referring to the concepts of “genetic” and “mutation” in the GA, a diversity variation strategy based on dynamic step size is designed. The algorithm designed has the advantages of fast operation and high search accuracy. At the end of the paper, two sets of experiments are designed to compare the improved WOA with other existing optimization algorithms. The results demonstrate that the algorithm is significantly superior to other algorithms, both in the ideal case of simulation experiments and in the practical application of defect inversion. Full article

River-channel washout is one of the common geological hazards threatening the safety of long-distance oil and gas pipelines, particularly under storm-flood conditions, when pipeline sections crossing rivers and gullies are more susceptible to damage. Existing assessment methods for river-channel washout are effective for single river cross-sections or post-disaster field investigations; however, their application remains limited when dealing with long-distance pipeline systems characterized by numerous river- and gully-crossing sections and large spatial variability in upstream catchment conditions. To address this issue, this study investigates storm-flood discharge and scour-depth calculation methods suitable for river- and gully-crossing sections of long-distance oil and gas pipelines, and establishes a quantitative evaluation index system that considers river-channel washout susceptibility, pipeline vulnerability, and pipeline failure consequences. Based on investigation results of river-channel washout hazards along multiple pipeline systems, including the Zhongxian–Yichang section of the Zhongwu Pipeline, the Hubei–Hunan section of the Lanzhou–Zhengzhou–Changsha Pipeline, and the Phase I Jiangxi Natural Gas Pipeline Network, the hazard characteristics and influencing factors of river-channel washout affecting long-distance oil and gas pipelines are analyzed and summarized. The proposed method was applied to 19 river- and gully-crossing pipeline sections in the Phase I Jiangxi Natural Gas Pipeline Network under different rainfall intensities. The results show that, under light-to-moderate rainfall conditions, 15 sites were classified as relatively low risk and 4 sites as medium risk. Under both the 50-year and 100-year return-period rainstorm scenarios, 12 sites were classified as relatively low risk, 6 sites as medium risk, and 1 site as relatively high risk. The results also indicate that the risk probability of some sites increases with increasing rainfall intensity. Among them, Site No. 19 shows the highest risk probability, increasing from 0.0997 under light-to-moderate rainfall conditions to 0.1474 and 0.1488 under the 50-year and 100-year return-period rainstorm scenarios, respectively. The proposed method can provide a reference for meteorological risk assessment of river-channel washout hazards along long-distance oil and gas pipelines. Full article

Digital Twin (DT) technology has emerged as a transformative approach in pipeline engineering, enabling real-time monitoring, predictive analytics, and enhanced decision-making across the asset lifecycle. This review critically examines recent advancements in the application of digital twins for pipeline systems, with a particular focus on condition monitoring, leak detection, corrosion assessment, and predictive maintenance. The study synthesizes findings from a wide range of literature to identify key enabling technologies, including Internet of Things (IoT) sensors, data-driven modeling, computational fluid dynamics (CFD), and machine learning algorithms. Special attention is given to the integration of physics-based and data-driven models for improving the accuracy and reliability of digital twin frameworks. In addition, this paper proposes a unified reference architecture for pipeline digital twins, supported by a mathematical formulation of synchronization and a comparative synthesis of existing approaches. The review highlights how digital twins facilitate early fault detection and operational optimization by continuously synchronizing physical assets with their virtual counterparts. The review also emphasizes the importance of uncertainty-aware and reliability-informed digital twin frameworks for robust decision-making in safety-critical pipeline applications. Applications in subsea, oil and gas, and water distribution pipelines are explored, demonstrating the versatility of DT systems under different environmental and operational conditions. Despite significant progress, challenges remain in data integration, model validation, scalability, and cybersecurity. Furthermore, the lack of standardized architectures and interoperability frameworks limits widespread adoption. This paper concludes by outlining future research directions, including the development of hybrid modeling techniques, edge computing integration, and AI-driven autonomous decision systems. Overall, digital twin technology represents a paradigm shift in pipeline engineering, offering substantial potential to enhance safety, efficiency, and sustainability in complex infrastructure systems. Full article

During crack detection in subsea oil and gas pipelines under strong-noise conditions, balanced field electromagnetic technique (BFET) exhibits limited capability to reconstruct the amplitude and phase features of crack signals and produces a relatively low signal-to-noise ratio (SNR). To address these limitations, a high-SNR detection method based on the joint optimization of sampling parameters and reference accuracy is proposed. A finite-element model of the balanced field electromagnetic sensor was established. The time-domain voltage signal of the crack detection signal and its amplitude–phase features were then obtained. By fitting the crack detection signals and introducing Gaussian white noise and quantization noise, the effects of different sampling frequencies, sampling accuracies, and reference accuracies on the reconstruction accuracy of the amplitude–phase features and on the SNR of the detection signals were systematically investigated. The experimental results show that, at an excitation frequency of 1 kHz, a sampling frequency of 64 kHz, and both a sampling accuracy and reference accuracy of 16 bits, the amplitude and phase errors of the crack detection signals are significantly reduced, while the SNR exceeds 31.74 dB. The proposed method provides a reference for evaluating the influence of sampling parameters and reference accuracy on the detection SNR and the reconstruction accuracy of amplitude–phase features. Full article

Oil and gas pipelines are the core infrastructure of energy transportation, and their safe operation is crucial to national energy security. Aiming at the difficulty of feature extraction and insufficient diagnosis accuracy of pipeline acoustic fault, a fault diagnosis method based on dual-branch parallel feature fusion of the original time-series signal and time–frequency map was proposed. In this method, the time–frequency map of the one-dimensional acoustic signal was generated by continuous wavelet Transform (CWT), and the original signal was input into the dual-branch network, respectively. The spatial–frequency domain features were extracted by using lightweight depthwise separable convolution (LDconv) embedded with coordinate attention (CA) in the upper branch. The lower branch mines local details and temporal dependencies through deformable convolution v4 (DCNv4) and Gated Recurrent Unit (GRU). The dual-branch features were concatenated and fused by Global Average Pooling (GAP), and finally the classification results were output by the fully connected network and Softmax. Experiments on industrial field data show that the average diagnostic accuracy of the proposed method is 98.87%, which can effectively extract weak fault features under complex noise, and has significant advantages in early fault recognition and generalization performance. Full article

Distributed acoustic sensing (DAS) is increasingly used for third-party intrusion (TPI) detection in oil and gas pipeline monitoring, but labeled DAS data are often scarce, leading to overfitting, poor generalization, and increased false alarms and missed detections. Conventional data augmentation, GAN-based synthesis, and transfer learning may generate physically implausible samples or fail to cover the event feature space. To address this, we propose a physics-guided large language model (LLM) prompt-engineering framework for DAS data augmentation and pipeline intrusion detection. The framework establishes a physically grounded feature-indicator framework for DAS disturbance-event classification by mapping primary event mechanisms to measurable signal indicators, and then uses a standardized four-module prompt template to guide LLM-based synthesis-script generation. A two-stage iterative verification procedure is further introduced to constrain the generated samples in terms of physical-mechanism compliance and feature-parameter consistency. Synthetic data are combined with real data to train a lightweight PatchTransformer model for TPI detection, while an additional CNN is used to assess cross-architecture applicability. Using the public DAS1K benchmark with five-fold stratified cross-validation and a univariate controlled experiment (0–800 synthetic samples per category), the results show that the use of synthetic data improves detection performance overall. The configuration with 600 synthetic samples per category achieves 92.27% accuracy and 92.38% macro-F1, outperforming the conventional augmentation baseline by 4.74 and 4.86 percentage points, respectively. An additional CNN experiment also showed consistent performance gains across the tested augmentation settings, indicating that the benefit of the proposed synthetic data was not restricted to the PatchTransformer architecture. These findings indicate that LLM-assisted data augmentation can effectively improve the generalization of DAS-based pipeline intrusion detection when field-labeled samples are scarce. Full article

UAV-based remote sensing excels in rapid response, high timeliness, simple operation, and high degrees of automation, and has been widely applied for geological hazard monitoring. Deep learning methods based on unitemporal UAV images can only analyze the static appearance of a scene, while bitemporal change detection can capture the dynamic evolution of hazards; however, due to diverse geological landforms and topography, environmental noises such as vegetation cover, and dynamic weather conditions, change detection of geological hazards from UAV images based on traditional deep learning technology is not always effective. Therefore, there is an urgent need to utilize large vision-language models (LVLMs) to further improve the accuracy and robustness of the change detection model. Motivated by this, this paper proposes a novel remote sensing model for geological hazard monitoring, referred to as CLIFF (CLIP-BIT-EfficientNet), based on the multi-modal LVLM Contrastive Language–Image Pre-training (CLIP), the change detection network Bitemporal Image Transformer (BIT), and the classification network EfficientNet, along with corresponding datasets and model fine-tuning strategies. The proposed transfer fusion module bridges the CLIFF and BIT networks by aligning their feature distributions and dimensions, allowing the general knowledge of the LVLM and the task-specific knowledge of the learnable branch to reinforce each other. Furthermore, this integrated pipeline addresses the scarcity of labeled hazard data by allowing the BIT to train on larger public datasets, while fine-tuning EfficientNet on smaller hazard-classification datasets within the change area, making the approach more efficient and reliable than direct classification methods. Experimental results show that the proposed CLIFF algorithm outperforms state-of-the-art deep learning algorithms such as LightCDNet and ChangeFormer, with an IoU of 75.74% and an F1 score of 0.8689 for change detection. Meanwhile, CLIFF has an overall accuracy rate of 86.89% in identifying geological hazards along gas pipelines, such as crude oil spills, collapses, landslides, and floods, with per-class accuracies of 87.32% and 86.17% for crude oil spills and landslides, respectively. Full article

The decarbonization of essential water supply infrastructure is a critical challenge for water-stressed and geographically constrained regions, particularly islands where both water and electricity systems are highly dependent on external or fossil-based resources. In Northern Cyprus, approximately 70% of domestic water demand is met through imported water via pipeline, while electricity generation relies predominantly on fuel oil, resulting in high greenhouse gas emissions and environmental burden. This study evaluates an integrated renewable energy-based supply system using a medium-scale concentrating solar power (CSP) plant with parabolic trough collectors coupled to thermal desalination. The proposed configuration is assessed as an alternative energy-driven infrastructure option for reducing dependence on imported water and fossil-based electricity. System performance was evaluated by estimating electricity and freshwater production under local climatic conditions, demonstrating that the proposed configuration can meet both the associated electrical energy requirements and domestic water demand in the selected region. A cradle-to-gate life cycle assessment (LCA) was conducted to quantify the environmental impacts of the integrated system and support sustainability-oriented decision-making. The LCA results identify residual fossil-based electricity, phosphoric acid consumption, and brine discharge as the main environmental hotspots. Overall, the findings show that CSP-driven desalination can provide a viable and more sustainable option for integrated energy and water supply in water-scarce coastal regions with high solar potential, highlighting its relevance for renewable energy integration, water-energy nexus planning, and resource-efficient infrastructure development. Full article

This study developed the Deformable Large-kernel Context-fused Spatial (DLCS)-YOLO model to address various challenges involved in permanent magnetic field perturbation (PMFP)-based defect detection for long-distance oil and gas pipelines, including a high false-positive rate, susceptibility to background noise interference, difficulty in identifying small-scale defects, low precision in feature representation and defect type discrimination, and poor adaptability to multiscale defects. The proposed model is an improved version of You Only Look Once (YOLO) v11n. The backbone of the proposed model contains the C3k2-Deformable Attention (C3k2-DAttention) module and the Spatial Pyramid Pooling-Fast-Large Separable Kernel Attention (SPPF-LSKA) module, which is used in place of the SPPF module to enhance robustness to noise and fine-grained feature extraction for small-scale defects. In the feature fusion layer, the Context-Guided Feature Pyramid Network (Context-Guided FPN) module is used to replace the conventional concatenation operation, thereby improving feature representation and defect classification accuracy. Furthermore, the Spatially Enhanced Attention Module (SEAM) is incorporated into the detection head to enhance adaptability in complex scenarios, including those involving background interference and multiscale defects. Experimental results indicate that the proposed model achieves mAP@50 and mAP@50:95 values of 94.5% and 64.7%, respectively, on a self-constructed dataset, with a computational cost of only 6.2 GFLOPs. Compared with the baseline YOLOv11n model, the proposed model exhibits a 3.1% higher precision, a 3.8% higher mAP@50 value, and a 3.0% higher mAP@50:95 value and requires 0.1 fewer GFLOPs. The proposed algorithm effectively enhances the accuracy and efficiency of pipeline defect detection, demonstrating considerable practical value and broad application prospects for detecting defects in oil and gas pipelines. Full article

Magnetic Flux Leakage (MFL) sensors are pivotal for the non-destructive inspection of oil and gas pipelines. However, the accuracy of defect quantification is severely compromised by pervasive noise in field-acquired MFL sensor signals, leading to substantial measurement uncertainty. To address this, we introduce a novel hybrid denoising framework that synergizes Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Wavelet Packet Decomposition (WPD). The key innovation is the employment of the Rime Optimization Algorithm (RIME) to automatically fine-tune the critical parameters of ICEEMDAN—the signal-to-noise ratio (SNR) and the number of noise additions—thereby customizing the decomposition for superior sensor signal enhancement. This optimization effectively suppresses mode aliasing and yields intrinsic mode functions that faithfully represent underlying defect features. The framework’s efficacy is rigorously validated through mathematical modeling, COMSOL Multiphysics 6.3-based finite element simulation, and real-field MFL sensor data. Results demonstrate remarkable improvements in sensor signal quality: a 53.69% increase in the SNR and reductions of 61.03% in MAE and 62.05% in RMSE over conventional methods. Crucially, the method achieved a Feature Preservation Rate (FPR) of 97.18% on simulated defects, underscoring its exceptional capability to retain critical metrological features for defect sizing. This work provides a robust signal-processing framework that significantly advances the measurement fidelity of MFL sensors, enabling more reliable pipeline integrity assessment. Full article

Valve leakage in marine oil and gas pipelines is a critical failure mode that threatens operational safety, ecological integrity and production economic benefits, creating an urgent demand for accurate, real-time and robust fault diagnosis systems. Acoustic Emission (AE) technology captures transient acoustic signatures generated by leakage to enable non-intrusive online monitoring, while deep learning supports intelligent analysis through automatic signal feature extraction. Nevertheless, traditional AE-based leakage diagnosis methods rely heavily on manual feature engineering and fixed signal processing rules. Existing AE-driven deep learning methods fail to simultaneously deliver high detection accuracy, low inference latency and strong noise immunity, hindering their practical deployment on offshore platforms. To address these limitations, this paper proposes a Parameter-free Star-shaped Attention Fusion Network (SAFNet) for lightweight valve leakage localization using AE signals. Centered on the Temporal Pyramid Encoder (TPE) and Progressive Lightweight Star-shaped Attention (PLSA) module, SAFNet integrates Dual Bilinear Star Mapping (DBSM), Energy-Driven Feature Refiner (EDFR) and Multi-Scale Gated Attention Fusion (MS-GAF) modules. This architecture achieves efficient multi-scale temporal feature extraction, parameter-free nonlinear enhancement, noise-resistant refined feature processing and adaptive hierarchical feature fusion. The proposed method is applicable to valve leakage diagnosis of marine oil and gas pipelines under variable pressure and complex marine noise conditions. Comprehensive experiments are conducted on a dataset constructed by combining laboratory controlled leakage signals with real marine background noise recorded from the Liwan 3-1 offshore platform. The experimental results reveal that SAFNet balances high detection accuracy, compact model size and low inference latency simultaneously. Specifically, the network maintains a stable detection accuracy above 95% under pipeline pressures ranging from 2 MPa to 5 MPa, and exhibits excellent stability under extreme heavy noise environments. Ablation experiments further validate the synergistic performance gain brought by all core modules. The presented network delivers an efficient lightweight solution for valve leakage localization under simulated marine acoustic conditions, promotes the development of intelligent monitoring technologies for marine pipeline systems, and comprehensively improves offshore operational safety and marine ecological protection capacity. Full article

This study assessed the leachability and methylation potential of total mercury (THg) associated with internal deposits in subsea oil and gas pipeline segments that are abandoned on the seafloor. The studies were conducted for up to 28 weeks under anaerobic conditions in static microcosms in which mercury-contaminated scale was exposed directly to sediments. The scale had an XRF-measured surficial THg concentration ranging between 15 and 3000 µg/cm². Release to sediment contacting the scale was rapid with no significant changes noted in THg concentrations after 6 weeks. THg released to sediments from intact pipeline coupons with low THg (15.5–124 µg/cm²) released an average of 16% of the THg mass present in the coupon into the sediments while those with higher THg (457–1367 µg/cm²) released an average of just 0.85% of the THg present in the coupon. Additional studies were conducted with all scale shaved into microcosms to simulate rapid and complete corrosion of the pipeline. The resulting sediment concentrations showed that the XRF-measured surficial THg accounted for all of the THg in the scale. In both sets of experiments, peak methyl mercury (MeHg) sediment concentration (measured at 8 and 12 weeks) averaged just 0.05% of the THg in the sediment. MeHg in the sediment was not correlated with the mass of THg measured in the scale and showed only a weak correlation with sediment THg suggesting that much of the THg is unavailable for release and methylation. The sediment MeHg concentration was positively correlated with leachable THg measured as filtered THg (passing 0.45 µm filter) (R² = 0.81). The methods presented here provide a means of assessing mercury release from pipeline scale and its implications in other environments. Full article

Earthquake-induced accidents involving buried oil and gas pipelines in water-network regions are governed by coupled seismic, hydrological, geotechnical, and emergency-response factors, while complete accident records are scarce. To support scenario-based consequence analysis under sparse-data conditions, this study develops an accident scenario analysis framework that integrates numerical simulation with Bayesian probabilistic inference. Scenario elements are organized according to four categories: disaster-causing factors, elements at risk, hazard-inducing environment, and emergency management. Finite element analysis and computational fluid dynamics are used to quantify pipeline mechanical response and hydraulic-scour effects, and the resulting physical responses are embedded in a dynamic Bayesian network as state evidence and transition constraints. Triangular fuzzy numbers are used to process expert evaluations and determine node probabilities. The resulting multi-mechanism simulation-Bayesian inference framework quantifies the accident chain from earthquake loading to pipeline deformation, leakage, fire or explosion, and emergency control. Forward reasoning estimates the probability of each scenario state, sensitivity analysis identifies key drivers, including strong earthquakes triggering landslides and rainfall during flood seasons, and disaster-chain analysis clarifies the dominant causative pathways. The framework provides a reproducible basis for scenario analysis, consequence assessment, monitoring and early warning, and emergency response planning for buried oil and gas pipelines exposed to seismic hazards in water-network regions. Full article

X65/Ni825 bimetallic composite pipes combine the load-bearing capacity of pipeline steel with the corrosion resistance of nickel-based alloys, making them promising candidates for harsh oil and gas transportation environments. However, their welded joints usually exhibit significant microstructural and compositional heterogeneities, especially in fusion zones and interpass transition regions, which can strongly affect local mechanical properties. In this study, X65/Ni825 bimetallic composite pipe welded joints were investigated, and the microstructure, elemental transition behavior, microhardness, and local mechanical properties of different weld passes and fusion zones in full high-alloy filler metal welded joints and hybrid filler metal welded joints were compared. The results show that all weld passes in the full high-alloy filler metal welded joint are mainly composed of γ-Ni cellular/columnar dendrites, showing good microstructural and mechanical uniformity. In contrast, the hybrid filler metal welded joint exhibits obvious microstructural passing. A compositional transition zone is formed between the transition and filler passes due to local remelting, dilution, and metallurgical mixing, accompanied by sharp changes in hardness and local strength. Overall, the full high-alloy filler metal system is more effective in reducing microstructural, compositional, and mechanical discontinuities within the weld, providing guidance for welding process optimization of X65/Ni825 bimetallic composite pipes. Full article