Search Results (9)

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems, and potentially causing operational issues and economic losses in the energy supply chain. However, current methods for detecting deterioration and regularly inspecting these submerged pipelines remain limited, as they rely heavily on divers, which is both costly and inefficient. Due to these challenges, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field, offering a more effective and reliable solution for pipeline monitoring and maintenance. In this study, we conducted an underwater pipeline tracking and damage detection experiment using a remote-controlled unmanned underwater vehicle (UUV) with autonomous features. The primary objective of this research is to demonstrate that UUV systems provide a more cost-effective, efficient, and practical alternative to traditional, more expensive methods for inspecting submerged natural gas pipelines. The experimental method included vehicle (UUV) setup, pre-test calibration, pipeline tracking mechanism, 3D navigation control, damage detection, data processing, and analysis. During the tracking of the underwater pipeline, damages were identified, and their locations were determined. The navigation information of the underwater vehicle, including orientation in the x, y, and z axes (roll, pitch, yaw) from a gyroscope integrated with a magnetic compass, speed and position information in three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the vehicle’s thrusters, enabling autonomous operation by providing these values as input to the thruster motors. In this study, 3D movement was achieved by activating the vehicle’s vertical thruster to maintain a specific depth and applying equal force to the right and left thrusters for forward movement, while differential force was used to induce deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle’s camera. The images for damage assessment were processed using a convolutional neural network (CNN) algorithm, a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle’s navigation system. The damages in the underwater pipeline were successfully detected using the CNN algorithm. The training accuracy and validation accuracy of the CNN algorithm in detecting underwater pipeline damages were 94.4% and 92.87%, respectively. The autonomous underwater vehicle also followed the designated underwater pipeline route with high precision. The experiments showed that the underwater vehicle followed the pipeline path with an error of 0.072 m on the x-axis and 0.037 m on the y-axis. Object recognition and the automation of the unmanned underwater vehicle were implemented in the Python environment. Full article

The aim of this paper is to simulate the European natural gas system in extreme situations and to determine its weaknesses in terms of demand coverage. An assessment has also been made of the targets set for existing energy efficiency regulations and their effects on the coverage of future natural gas demand. This document assesses the potential for energy efficiency improvements associated with European countries and the effect of such improvements on the lessening of the natural gas demand. Once the efficiency improvement potential has been identified, the results of demand coverage in various scenarios of natural gas supply cut-off via pipelines were studied. The expected result reflects the study of the effect of the presumed demand reduction, due to the improvement of energy efficiency, on the self-sufficiency of the natural gas network and the improvement of energy coverage for EU countries. To carry out this study, an evaluation of the current infrastructures was developed, the existing resources were optimized, and the independence of the system was quantified in relation to the current situation of natural gas consumption at the European level. The proposed model has resulted in improvements in the coverage of the demand of certain countries and has detected those with systems that are not robust enough to face extreme crisis situations. The main conclusions are that the natural gas system has improved considerably from 2009 to the present, and that, in the event of massive gas cuts, there is a real risk of being unable to cover the natural gas demand of several countries with a very high dependence on gas from Russia. Full article

Natural gas is the most growing fossil fuel due to its environmental advantages. For the economical transportation of natural gas to distant markets, physical (i.e., liquefaction and compression) or chemical (i.e., direct and indirect) monetisation options must be considered to reduce volume and meet the demand of different markets. Planning natural gas supply chains is a complex problem in today’s turbulent markets, especially considering the uncertainties associated with final market demand and competition with emerging renewable and hydrogen energies. This review study evaluates the latest research on mathematical programming (i.e., MILP and MINLP) as a decision-making tool for designing and planning natural gas supply chains under different planning horizons. The first part of this study assesses the status of existing natural gas infrastructures by addressing readily available natural monetisation options, quantitative tools for selecting monetisation options, and single-state and multistate natural gas supply chain optimisation models. The second part investigates hydrogen as a potential energy carrier for integration with natural gas supply chains, carbon capture utilisation, and storage technologies. This integration is foreseen to decarbonise systems, diversify the product portfolio, and fill the gap between current supply chains and the future market need of cleaner energy commodities. Since natural gas markets are turbulent and hydrogen energy has the potential to replace fossil fuels in the future, addressing stochastic conditions and demand uncertainty is vital to hedge against risks through designing a responsive supply chain in the project’s early design stages. Hence, hydrogen supply chain optimisation studies and the latest works on hydrogen–natural gas supply chain optimisation were reviewed under deterministic and stochastic conditions. Only quantitative mathematical models for supply chain optimisation, including linear and nonlinear programming models, were considered in this study to evaluate the effectiveness of each proposed approach. Full article

In the current international situation, energy storage is an important means for countries to stabilize their energy supply, of which underground storage of natural gas is an important part. Depleted gas reservoir type underground gas storage (UGS) has become the key type of gas storage to be built by virtue of safety and environmental protection and low cost. The multi-cycle high injection and production rate of natural gas in the depleted gas reservoir type UGS will cause the in-situ stress disturbance. The slip risk of fault in the geological system increases greatly compared with that before the construction of the storage engineering, which becomes a great threat to the sealing of the gas storage. Reasonable injection and production strategy depend on the reliable assessment of the shear behavior of the fault belt, which can guarantee the sealing characteristics of the UGS geological system and the efficient operation of the UGS. Therefore, the shear behavior of the fault is studied by carrying out experiments, which can provide important parameters for the evaluation of fault stability. However, there is a large gap between the rock samples used in the previous experimental study and the natural faults, and it is difficult to reflect the shear failure characteristics of natural faults. In this paper, similar fault models based on high-precision three-dimensional scanners and engraving machines, filled with three types of fault gouge, are prepared for a batch of representative direct shear tests. The results show that the peak shear strength of the fault rocks with a shear surface is higher than that of the fault rocks with a tensile surface. Compared with the clay mineral content, the roughness of the fault surface is much more significant for the shear strength of the fault rock. For the fault rocks with similar fault surface morphology, the higher the clay content in the fault gouge, the greater the shear strength of the fault rocks. For the fault rocks with different fault surface morphology and the same fault gouge, the cohesion and internal friction angle of the tensile type is generally smaller than that of the shear type. Full article

Non-energy use of natural gas is gaining importance. Gas used for 183 million tons annual ammonia production represents 4% of total global gas supply. 1.5-degree pathways estimate an ammonia demand growth of 3–4-fold until 2050 as new markets in hydrogen transport, shipping and power generation emerge. Ammonia production from hydrogen produced via water electrolysis with renewable power (green ammonia) and from natural gas with CO₂ storage (blue ammonia) is gaining attention due to the potential role of ammonia in decarbonizing energy value chains and aiding nations in achieving their net-zero targets. This study assesses the technical and economic viability of different routes of ammonia production with an emphasis on a systems level perspective and related process integration. Additional cost reductions may be driven by optimum sizing of renewable power capacity, reducing losses in the value chain, technology learning and scale-up, reducing risk and a lower cost of capital. Developing certification and standards will be necessary to ascertain the extent of greenhouse gas emissions throughout the supply chain as well as improving the enabling conditions, including innovative finance and de-risking for facilitating international trade, market creation and large-scale project development. Full article

Because reliance on gas for electricity generation rises over time, the natural gas and electricity markets are highly connected. However, both of them are susceptible to various risk factors that endanger energy security. The intricate interactions among multiple risks and between the two markets render risk assessment more challenging than for individual markets. Taking a systematic perspective, this study first undertook a thorough analysis of the evolution mechanism that indicated the key risk factors and dual interactions, with real-world illustrative examples. Subsequently, a system dynamics model was constructed for understanding the causal feedback structures embedded in the operation of a coupled natural gas–electricity market in the face of risks. Quantitative experiments were conducted by using data from China’s Energy Statistical Yearbook, China’s Statistical Yearbook and other reliable sources to assess the effects of individual risks, depict the evolutionary behavior of coupled markets and compare the risk response strategies. The findings revealed the evolution of dominant risk factors and the aggregated effects of multiple risks in multiple markets, suggesting the need to comprehensively monitor dynamic risks. Moreover, risk factors can propagate from one market to another via interactions, yet it depends on multiple aspects such as the severity of the risk and the intensity of the interactions. Demand compression and emergency natural gas supply behave differently throughout the market’s recovery, necessitating a balance between short-term and long-term risk response strategies. Full article

The continuity of gas-supply service is a major concern for all gas-supply operators. A safety review of a gas-supply system could help to mitigate the potential repercussions of supply disruptions. Disruptions occur at random due to systemic failures in gas distribution networks. Assessing the operational safety of gas distribution networks is challenging and complex, especially when operational data are limited or associated with high uncertainty. This paper focuses on gas leak incidents. Natural gas leaks disrupt the production process and endanger the ecosystem. Mechanically originated damage in pipelines is found to be the major cause of leaks, according to our investigations. This paper proposes a three-parameter risk matrix to be used in the safety analysis of gas-supply systems. This paper then raises the possibility of using grey-system theory. Grey-system theory has been used to overcome the limitations of the conventional matrix method. This choice is motivated by two facts: our data are heterogeneous with a high level of uncertainty, and some of the data are based on experts’ judgement and maintenance reports using qualitative metrics. It is underlined how GST provides insight for the decision-making process, even in the absence of complete information sets. The method developed here is advocated in the context of procedures ensuring the safety and the supply continuity of gas-supply systems. Full article

Gas supply system risk assessment is a serious and important problem in cities. Existing methods tend to manually build mathematical models to predict risk value from single-modal information, i.e., pipeline parameters. In this paper, we attempt to consider this problem from a deep-learning perspective and define a novel task, Urban Gas Supply System Risk Assessment (GSS-RA). To drive deep-learning techniques into this task, we collect and build a domain-specific dataset GSS-20K containing multi-modal data. Accompanying the dataset, we design a new deep-learning framework named GSS-RiskAsser to learn risk prediction. In our method, we design a parallel-transformers Vision Embedding Transformer (VET) and Score Matrix Transformer (SMT) to process multi-modal information, and then propose a Multi-Modal Fusion (MMF) module to fuse the features with a cross-attention mechanism. Experiments show that GSS-RiskAsser could work well on GSS-RA task and facilitate practical applications. Our data and code will be made publicly available. Full article

Methane hydrates (hereinafter, MH), for many reasons, are widely recognized as a form of sustainable energy due to their environmentally friendly nature. MH, while burning, produce fresh water, which could in turn offer one possible solution to worldwide shortages of water. MH also maintains the capacity to change the landscape of the global energy supply. According to recent scientific evaluations, the potential global supply of MH is even higher than the total storage of traditional crude oil and conventional natural gas. However, its offshore extraction process could be linked to both catastrophic and non-catastrophic events that may contribute to global warming and climate change, cause harm to human health and life, endanger the flora and fauna, and threaten the very global environment as a whole. Therefore, from a legal viewpoint, an efficient and effective system of civil liability rules seem crucial to control the risks, and to compensate the victims to which damages may occur. This article takes into consideration China’s legal framework in assessing the risks connected to MH offshore extraction. Such a choice for examination is justified by China’s leading position for implementing the technology necessary for extracting MH. This analysis shows that China’s current legal instruments are still far from fully equipped to prevent the risks associated with the offshore extraction of MH, as well as to offer effective remedies for the victims once any damages have occurred. Therefore, more efficient measures and remedies should be considered (or even imposed) to address the specific risks of offshore methane hydrate extraction. Indeed, in the past few decades, China’s environmental protection laws and regulations have mainly focused on the environmental risks that may occur during the process of extracting conventional resources; however, they do not address methane hydrates specifically. This presents a legal challenge for environmental protection laws. The potentially catastrophic events that may occur as a result of the offshore MH extraction processes in particular present a legal challenge for environmental protection laws in China and across the globe. Thus, this article focuses on how to prevent these risks before they even occur, followed by a careful attempt to address compensation efforts for any damages caused by said catastrophes. Full article