Gas Pipeline Research Articles

Corrigendum to “Hydrogen blending in gas pipelines: Fluid-dynamic insights, risks, and recommendations” [J Hydrogen Energy 120 (2025) 67–77

Corrigendum to “Hydrogen blending in gas pipelines: Fluid-dynamic insights, risks, and recommendations” [J Hydrogen Energy 120 (2025) 67–77

Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study

Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study

Mixing mechanisms of hydrogen-blended natural gas in subsea pipelines: Effects of injection angles

Mixing mechanisms of hydrogen-blended natural gas in subsea pipelines: Effects of injection angles

Research progress on crack propagation and failure probability prediction of hydrogen-blended natural gas pipeline

Research progress on crack propagation and failure probability prediction of hydrogen-blended natural gas pipeline

Corrosion failure prediction in natural gas pipelines using an interpretable XGBoost model: Insights and applications

Corrosion failure prediction in natural gas pipelines using an interpretable XGBoost model: Insights and applications

Thermo-economic analysis of blending hydrogen into natural gas pipeline with gaseous inhibitors for sustainable hydrogen transportation

Thermo-economic analysis of blending hydrogen into natural gas pipeline with gaseous inhibitors for sustainable hydrogen transportation

Preparation of anti-hydrate coating on natural gas pipeline and the inhibition performance of hydrate nucleation and adhesion

Preparation of anti-hydrate coating on natural gas pipeline and the inhibition performance of hydrate nucleation and adhesion

Whether hydrogen blending in natural gas pipelines induce detonation? A numerical investigation of explosions in confined pipeline compressor plants

Whether hydrogen blending in natural gas pipelines induce detonation? A numerical investigation of explosions in confined pipeline compressor plants

Risk Analysis of the Failure of Oil and Gas Pipelines due to External Corrosion Based on a Dynamic Bayesian Network

Risk Analysis of the Failure of Oil and Gas Pipelines due to External Corrosion Based on a Dynamic Bayesian Network

Self-organized criticality study in natural gas pipeline systems: A system & data science approach

Self-organized criticality study in natural gas pipeline systems: A system & data science approach

A study on the leakage consequence and risk analysis of hydrogen-blended natural gas pipeline in different failure modes

A study on the leakage consequence and risk analysis of hydrogen-blended natural gas pipeline in different failure modes

Experimental study on the leakage identification for the buried gas pipeline via vibration signals

Experimental study on the leakage identification for the buried gas pipeline via vibration signals

Numerical simulation of the external acoustic field characteristics of gas pipeline leakage

Numerical simulation of the external acoustic field characteristics of gas pipeline leakage

Intelligent identification of internal leakage in natural gas pipeline control valves based on Mamba-ARN

Intelligent identification of internal leakage in natural gas pipeline control valves based on Mamba-ARN

Random risk assessment model and risk-based maintenance decisions for natural gas pipelines

Random risk assessment model and risk-based maintenance decisions for natural gas pipelines

A Multiscale Approach to Cyber-Mechanical Threat Modeling for Predicting and Preventing Failures in Critical Energy Infrastructure

The increasing convergence of cyber and mechanical domains in critical energy infrastructure, including electric power grids, oil and gas pipelines, and renewable energy systems, has significantly expanded the attack surface for sophisticated cyber threats, particularly ransomware. These hybrid systems, known as cyber-mechanical systems (CMS), present complex vulnerabilities across multiple temporal and spatial scales, which are inadequately addressed by traditional security frameworks that treat cyber and physical layers in isolation. This study introduces a novel multiscale AI/ML-based framework for predictive resilience modeling and real-time anomaly detection in CMS environments. The proposed architecture integrates a Convolutional Long Short-Term Memory 3D (ConvLSTM3D) model for high-resolution spatiotemporal anomaly detection, and a Graph Neural Network (GNN) for dynamic threat propagation analysis across system hierarchies. The framework was evaluated using both synthetic simulations and the CICIDS 2017 dataset, yielding a test accuracy of 88.86%, an AUC of 0.89, and an F1-score of 0.38 in highly imbalanced ransomware detection scenarios. A simulated ransomware attack on a SCADA-controlled energy network demonstrated the model’s ability to detect threats at the micro (≤1s), meso (1s–1h), and macro (>1h) levels, with detection precision exceeding 95% for short-duration anomalies. These results confirm that modeling cyber-mechanical interactions across multiple scales significantly enhances early threat detection and supports situational awareness. Future research should explore federated learning, continual adaptation, and explainable AI to enable real-time deployment and broader generalizability. By bridging cyber-physical modeling, machine learning, and resilience engineering, this study contributes an actionable framework for safeguarding critical energy infrastructure from increasingly sophisticated and coordinated cyber threats.

A study of applicability of instrumented indentation method to determine the mechanical properties of thermoplastic

The article deals with the method of instrumental indentation as an effective tool for studying the mechanical properties of thermoplastics. The method allows to determine such material properties as hardness, modulus of elasticity, rheological characteristics. The paper presents a review of examples of using the method of instrumental indentation to solve research problems in studying the properties of various types of polymeric materials, describing the parameters that affect the results of measurements. Analyses and experimental studies of the method’s capabilities were carried out on the example of high-density polyethylene, which is used in the production of gas pipelines. The results showed the applicability of this method in studying the material properties of polyethylene (PE) gas pipeline pipes for diagnosing their technical condition. In this work, samples that have been in service for a long time and a sample of new pipe were investigated. It has been revealed that the hardness values of polyethylene samples of different service life as an indicator of resistance to plastic deformation under static loads are quite close, while the rheological properties differ significantly. This peculiarity should be taken into account when conducting various mechanical tests, since the rate of load increase during such tests will significantly affect their results. It was found that the values of hardness and modulus of elasticity decrease with increasing indenter dwell time under load due to stress relaxation in the deformed region under the indenter. The influence of polyethylene structure on its mechanical properties is considered. It is also revealed that the modulus of elasticity of polyethylene samples multiply increases when the degree of crystallinity changes from 48 to 56%. The article describes the prospectivity and efficiency of using the method of instrumental indentation to analyse the elastic-plastic and rheological properties of thermoplastics, as well as their relationship with the crystal structure.

Horizontal Directional Drilling: A Solution to Oil and Gas Pipeline Vandalism and Theft in Nigeria

Abstract: Nigeria, a major oil-producing country in Africa, has long been plagued by the persistent problem of oil pipeline vandalism. Sabotage and illegal tapping of oil pipelines have resulted in significant economic losses, environmental damage, and disruptions to the country's energy supply. This study explores the potential utilization of horizontal directional drilling (HDD) as a viable solution to help eliminate oil pipeline vandalism in Nigeria. The objective of this literature review is to showcase the effectiveness of HDD technology in preventing or reducing incidents of pipeline vandalism as compared to conventional methods of pipeline installation. By examining case studies and scholarly literature, this paper will analyze the advantages, challenges, and economic feasibility of implementing HDD techniques to mitigate oil and gas pipeline vandalism in Nigeria. Firstly, the study will explore the advantages of HDD technology, including its ability to install oil pipelines underground and mitigate accessibility to potential vandals, enabling the creation of subsurface pathways, reducing the visibility and potential vulnerability of infrastructure. In addition, this literature review will shed light on the challenges associated with implementing HDD in Nigeria. The economic feasibility of implementing HDD technology in Nigeria’s oil and gas sector will be assessed. A cost-benefit analysis will be conducted, comparing HDD with traditional methods of oil pipeline installation. The findings of this study will contribute to the existing body of knowledge regarding methods to combating oil and gas pipeline vandalism in Nigeria, thereby providing useful insights to policymakers, infrastructure developers and relevant stakeholders, encouraging them to consider HDD as a potent solution to mitigate oil and gas pipeline vandalism.

Multi-Model Collaborative Inversion Method for Natural Gas Pipeline Leakage Sources in Underwater Environments

The identification of leakage sources in underwater natural gas pipelines (UNGPs) remains a critical challenge due to complex environmental conditions. In this study, we propose a novel simulation–optimization method, integrating numerical bubble plume dynamics models with surrogate models to enable accurate leakage parameter inversion. First, a bubble plume underwater motion simulation model was developed based on the actual conditions of the study area to predict the future spatial and temporal variation characteristics of the bubble plumes in certain wave fields. Then, the simulation–optimization method was applied to determine the leakage velocity and offset distance of the underwater gas pipeline leakage source via inversion. To reduce the computational load of the optimization model by repeatedly invoking the simulation model, the Kriging method and a backpropagation (BP) neural network were used to build surrogate models for the numerical model. Finally, the optimized surrogate model was solved using the simulated annealing method, and the inverse identification results were obtained. The experimental results show that both methods can achieve a high inversion accuracy. The relative error of the Kriging model is no more than 12%, and the running time is 13 min. Meanwhile, based on the BP neural network surrogate model, the relative error of the BP neural network model is about 14%, and the running time is 2.5 min.

Security of subsea gas pipelines: technological solutions for safe and rapid response

The security of energy supplies is a primary objective for each country, and its relevance for national strategies has increased over the time, especially after the interruption of the gas flow via the Baltic Sea pipelines in September 2022. While the causes behind such damage may be investigated long term, some conclusions can already be drawn from a technical and engineering perspective. In case of potential damage, the most important action to undertake is a rapid inspection along the subsea pipeline to locate the damage and measure its extent so that emergency actions can be implemented to safeguard the asset and limit adverse consequences on production and to the environment. Such operation can be extremely dangerous for divers and human operators, since first damage assessment means they have to deal with unknown factors in the vicinity of a pipeline, which usually operates at high pressure. Since conventional remotely operated vehicles are constrained by tether length, the safest option would be to use a subsea drone with pre-programmed missions, hi-tech sensors and the capability to safely operate beyond the horizon. The same technological solution can be adopted for frequent and fully unmanned inspection missions, leveraging the artificial intelligence-driven navigation system enhanced by an active event-triggering mission re-planner for autonomous feature detection and localisation. This paper shares Saipem’s experience in developing and qualifying the Flatfish underwater drone, and how this can be used to enhance the security of subsea pipelines, or in the context of emergency operations.