Natural Gas Transmission Research Articles

Natural-Gas Transmission Pipeline-Leak Detection Model Based on Acoustic Emission and Machine Learning

Natural-Gas Transmission Pipeline-Leak Detection Model Based on Acoustic Emission and Machine Learning

Modeling of high-pressure transient gas-liquid flow in M-shaped jumpers of subsea gas production systems

Two-phase flow with low liquid loads is common in high-pressure natural gas offshore gathering and transmission pipelines. During gas production slowdowns or shutdowns, an accumulation of liquid in the lower sections of subsea pipelines may occur. This phenomenon is observed in jumpers that connect different units in deep-water subsea gas production facilities. The displacement of the accumulated liquid during production ramp-up induces temporal variations in pressure drop across the jumper and forces on its elbows, resulting in flow-induced vibrations (FIV) that pose potential risks to the structural integrity of the jumper. To bridge the gap between laboratory experiments and field conditions, transient 3D numerical simulations were conducted using the OpenFOAM software. These simulations facilitated the development of a mechanistic model to elucidate the factors contributing to increased pressure and forces during the liquid purging process. The study examined the influence of gas pressure level, pipe diameter, initially accumulated liquid amount, liquid properties, and gas mass flow rate on the transient pressure drop and the forces acting on the jumper's elbows. The critical gas production rate required for complete liquid removal of the accumulated liquid was determined, and scaling rules were proposed to predict the effects of gas pressure and pipe diameter on this critical value. The dominant frequencies of pressure and force fluctuations were identified, with low-pressure systems exhibiting frequencies associated with two-phase flow phenomena and high-pressure systems showing frequencies attributed to acoustic waves.

Experimental study on hydrate blockage formation and decomposition in gas-dominated Fluctuating pipes

Current research pertaining to hydrate flow assurance in the transportation of deep-sea natural gas through undulating pipelines remains sparse. Employing a novel undulating pipeline equipped with multipoint-distributed sensors, we delve to the processes of hydrate generation, migration, deposition, blockage, and decomposition. Our findings reveal that the upward inclination sections of both lifting and underlying pipes are particularly vulnerable to hydrate blockage. The lifting pipe is not conducive to hydrate deposition and blockage, whereas the underlying pipe poses a heightened risk for hydrate formation. Increasing subcooling leads to a reduction in blockage duration and sloughing frequency, alongside an extension in decomposition time. Furthermore, increasing the water injection rate and pipeline curvature prolongs the blockage of the lifting pipe. However, the impact of the water injection rate on the underlying pipe is negligible. Increasing pipeline curvature promotes the blockage of the underlying pipe. We propose models that encapsulate hydrate blockage and decomposition based on varying gas–liquid distributions. This research fills the gap in the study of hydrate blockage and decomposition in undulating pipelines and provides a reference for monitoring hydrate risks in natural-gas transmission pipelines.

Quantitative risk assessments of hydrogen blending into transmission pipeline of natural gas

Recently, considerable attention has been paid to blending hydrogen with natural gas transmission pipelines. However, risk assessments using different hydrogen blend concentrations in natural gas pipelines have not been thoroughly investigated. Thus, the main objective of this study is to carry out a quantitative risk assessment of hydrogen blending into the transmission of natural gas. For this, six different concentrations of hydrogen blending, three pipes and four different release holes are used in this study. The effect distance decreased with increasing hydrogen concentration for the jet fire case. For case of vapor cloud explosions, the downwind distance of the maximum explosion overpressure increased with increasing hydrogen concentration. It was also found that as the hydrogen concentration increased, the individual risk was sensitive to adjacent to the pipelines, however, it was less for far-fields. This study is expected to contribute to the safety measures for business sites with hydrogen blending into natural gas pipelines.

Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry.

Environmental Benefits of the West-East Natural Gas Transmission Project: Cross-Regional Energy Optimization and Transformation for Sustainable Development

Environmental Benefits of the West-East Natural Gas Transmission Project: Cross-Regional Energy Optimization and Transformation for Sustainable Development

Multiscale graph based spatio-temporal graph convolutional network for energy consumption prediction of natural gas transmission process

Energy consumption prediction is crucial for planning and managing natural gas networks, enhancing transmission efficiency. However, most of relevant studies fail to consider the topology of natural gas network. Therefore, a multiscale graph based spatio-temporal graph convolutional network model (MG-STGCN) is proposed, which can extract the multi-scale relationship of data. Based on graph convolutional and temporal gated convolution network, data correlation, spatial dependence and time dependence are considered to improve the prediction effect. In order to verify the effect of the model, the model is tested based on two natural gas networks in different region of China. The single step prediction results of MG-STGCN are significantly better than spatio-temporal graph convolutional network (STGCN), long short-term memory (LSTM), gate recurrent unit (GRU) and temporal convolutional network (TCN), which proves the effectiveness and generalization ability of MG-STGCN. The training time of MG-STGCN is reduced by 20.8 % compared with STGCN. In different prediction time step, the average prediction effect of MG-STGCN is better than that of other methods, and the performance improvement rate is not less than 8.6 %. MG-STGCN can well capture the parameter correlation, spatial and temporal dependence of data, and achieve effective prediction of pipeline energy consumption.

The moving goalposts - tightening design goals during a sound impact study for a New York State Article VII filing

Article VII of the New York State Public Service Law has a review process for applications to site, construct, and operate large-scale electric or natural gas transmission facilities. A transmission facility developer proposed to rebuild 12 miles of an above-ground electric transmission line and an adjoining substation in southern New York. In the middle of the permitting process, one reviewing entity issued a new guidance document that outlined sound study methodologies and sound level design goals for projects filing Article VII applications. This arrived after the initial sound level impact study was finished and the Article VII application was deemed complete for the project. Epsilon Associates, Inc., the acoustical consultant, revisited the sound model and then implemented mitigation in the form of a 30-foot-tall barrier for the proposed substation upgrade to meet the more stringent limits outlined in the guidance. Although the project was ultimately approved and constructed, additional time was spent both by the consultant and the developer which also accrued additional costs for the project attributable to this new guidance.

Planning natural gas supply chain pathways for energy system decarbonisation: An Australian case study

The future role of natural gas systems in a deeply decarbonised world is very uncertain. This work seeks to characterise the changes that the clean energy transition is likely to induce in the natural gas chain, across extraction and transmission, distribution networks and gas consumption/use trends, for various scenarios with different demand and supply drivers. First, the results of a least-cost macro-scale energy system planning optimisation are explored to identify future natural gas demand and supply requirements. Second, those findings are used as inputs to a detailed analysis of the full gas supply chain, from extraction to final use, to examine the evolution of i) stored resource levels; ii) transmission and distribution networks operation and financing; and iii) required capacity and siting of gas generation; all, with a realistic account of the related infrastructure. Australia is investigated as a case study that relies on natural gas for both domestic and export purposes. Results project a shift towards a more localised gas consumption to be largely supplied by fewer basins, with associated potential transmission and extraction bottlenecks. They anticipate an overall less utilised, ageing distribution network with potential challenges to asset reliability and financing. Finally, they demonstrate both the scheduled and early retirement of fossil generation assets can allow the siting of considerable amounts of new gas power firming capacity on brownfield sites, thus offering an opportunity to reduce total system costs.

Generator Start-Up Strategy for Power System Restoration Considering Interdependency With Natural Gas Transmission Network

Generator Start-Up Strategy for Power System Restoration Considering Interdependency With Natural Gas Transmission Network

The performance of transmission pipelines on February 6th, 2023 Kahramanmaras earthquake: a series of case studies

AbstractThe Mw 7.8 earthquake that struck Kahramanmaras on February 6, 2023, caused significant damage to hydrocarbon and water transmission lines due to permanent ground deformations. Explosions occurred in the Kahramanmaras–Gaziantep Natural Gas transmission pipeline causing disruption of gas in the cities of Gaziantep and Hatay Fault Displacement Hazard. Water transmission pipelines were also damaged due to extreme fault offsets. This paper presents the findings from the earthquake affected zones. Particular attention was given to the performance of the 2600 mm diameter Duzbag–Gaziantep water transmission pipeline due to three reported challenges: (#1) The behavior of the pipe in the Duzbag tunnel, (#2) The pipe bend behavior at the fault crossing, and (#3) The pull-out of the pipe at valve room. Based on field observations, extreme damage forms at pipes were presented. A three-dimensional nonlinear numerical analysis was performed to simulate the observed damage of the reoriented water pipe at the fault crossing for Case #2. The nonlinear finite element model was able to capture the complex nature of the post-yield behavior of steel pipe as well as the damage locations along the pipe and their sequence of occurrences. As a mitigation measure for new pipes, the need for using flexible connections at critical crossings was highlighted. For existing pipes, the importance of developing secondary (by-pass) lines was emphasized.

Deep Learning Application as a Method for Classification Equipment Failure on Natural Gas Pipeline

Predicting maintenance and preventing failures in natural gas pipelines is critical for safety and efficiency in the oil and gas industry. This research utilizes machine learning (ML) and deep learning (DL) methods to predict failures in natural gas transmission pipelines. Using a dataset from the U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA), the study examines failure incidents from 2010-2019 and non-failure data from 2020-2023. The research involves comprehensive data preprocessing, addressing missing values and dataset imbalances. Key pipeline attributes such as diameter, wall thickness, age, and operating pressure are used for failure prediction. The ML approach employs logistic regression, optimized for binary classification tasks, to establish the probability of pipeline failures. For the DL approach, an Artificial Neural Network (ANN) with Feed Forward and Back Propagation algorithms is developed. Key parameters, including activation functions and hyperparameters, are finely tuned to enhance predictive performance. ReLU activation functions are used in input and hidden layers, while the sigmoid function is applied to the output layer. Performance evaluation of both ML and DL models is based on accuracy, F1 Score, and Area Under Curve (AUC). In summary, ML showed better performance than DL, with value of accuracy is 0.74, F1 Score is 0.85, and AUC is 0.55 while the value of accuracy, F1 score, AUC from DL is 0.61, 0.003, and 0.58 respectively. DL offers superior performance in handling complex and huge amounts of data. In the other hand, ML offers more robust models and simpler adjustable models than DL. Because of the amount of computed data in this research, it is better to have ML as a predictive algorithm.

Research on internal leakage detection of the ball valves based on stacking ensemble learning

Abstract Natural gas is an important clean energy source that is mainly transported through pipelines. The ball valve is a crucial piece of control equipment for the pipeline transportation system for natural gas, and the failure of internal leakage of the ball valve will seriously affect the natural gas transmission and increase the risk of sudden safety accidents. In response to the problems of the limitations of a single machine learning model in the traditional ball valve internal leakage rate prediction methods and failure to qualitatively analyze unilateral and bilateral internal leakage recognition of ball valve, a study of ball valve internal leakage detection based on Stacking ensemble learning is proposed. A total of 15 time and frequency domain feature parameters were obtained by feature extraction of 125 and 96 sets of raw acoustic emission signals from the ball valve; the parameters of a single machine learning model were adjusted by Bayesian optimization grid search. An internal leakage rate prediction model and an internal leakage recognition model are constructed, and the proposed model is compared and analyzed with a single model through a field ball valve internal leakage test. The results indicate that the Stacking ensemble learning model outperforms each single machine learning model in terms of SMAPE (17.2583), RMSE (1.1009), and MAE (0.9375) for internal leakage rate prediction. The Stacking ensemble learning model outperformed the single machine learning model in terms of accuracy (1), recall (1), precision (1), FAR(0), and F1-score (1) for internal leakage recognition. Stacking ensemble learning significantly enhances the model's ability to detect internal ball valve leaks.

Numerical analysis of the effect of hydrogen doping ratio on gas transmission in low-pressure pipeline network

In the context of a global shift towards clean energy, the integration of hydrogen into existing natural gas pipeline networks is essential for facilitating the energy transition. Specifically, low-pressure natural gas pipeline networks are crucial for transporting gas efficiently and with minimal losses from the source to end-users in nearby cities. The physical properties of hydrogen differ significantly from those of natural gas, which implies that introducing hydrogen into the pipeline network will modify operational parameters and impact the safety and efficiency of gas transmission. Therefore, this study concentrates on the efficiency of transporting and scheduling hydrogen-blended natural gas through existing low-pressure direct pipelines. Utilizing the BWRS (Benedict-Webb-Rubin-Starling) equation of state, mathematical models were developed to calculate gas properties and to simulate both the steady and transient states of hydrogen-blended natural gas networks. These models were then validated with TGNET software. This study examines a low-pressure natural gas transmission and distribution network that transports gas directly from a production site in Sichuan to urban areas. It conducts simulations of hydrogen blending and analyzes the impact of various hydrogen blend ratios on the transport and scheduling responses of the network. Research suggests that maintaining the hydrogen blend below 30% ensures the safe operation of low-pressure gas pipelines and prevents the transmission pressure from exceeding 3 MPa. Additionally, under a fixed energy flow output, blending hydrogen intensifies the challenges associated with pressure drops and temperature increases due to high flows in small diameter pipes, and further complicates the management of interruptions resulting from faults. Therefore, it is recommended that the hydrogen blend ratio be maintained at no more than 30%, and that accident handling procedures be established in advance to control pressure variations following a shutdown.

Germany's Way from Russian Natural Gas to Green Hydrogen: Network Data, Procedures, and Topologies for Feasible Transition Paths to a Green Hydrogen Transport Infrastructure in Germany, GreenHyDE [gri:n'haɪ̯də] –

Hydrogen—and thus hydrogen transport—will play an important role in the German and European transition to climate neutrality. A German hydrogen transport network is currently being planned, but its appropriate structure and size highly depend on the spatial distribution of hydrogen demand and supply and their development over time. Moreover, the security of supply with both, hydrogen and natural gas, needs to be guaranteed during the transition period, as repurposing of the existing natural gas transmission system is a highly advantageous option for the building of hydrogen networks. Therefore, the integrated analysis of natural gas and hydrogen networks is a necessary and urgent, though complex, field of research. For this purpose, the article presents the database for the existing German natural gas transmission system (which in parts will be repurposed for hydrogen transport) and procedures for the development and evaluation of network topologies for both gases. The results for the German case show feasible development steps for the design of green hydrogen transport networks and the transition from natural gas in different scenarios. Open access to data and tools for technoeconomic assessment is provided.

Comparison of KNN and LSTM on the Prediction of the Operational Conditions of Natural Gas Pipeline Transmission Networks

During the gas distribution process, a sequence of compressors creates a pressure difference, causing gas to move from regions of high pressure to areas with comparatively lower pressure. The Natural Gas transmission process experiences variations in pressure and temperature, primarily caused by frictional losses, differences in altitude, gas velocity, and the Joule-Thompson effect. Additionally, effective heat transfer to or from the environment contributes to temperature changes throughout the pipeline. The presence of liquid and density changes (hydrate) within the channel also has an impact on the pressure, influencing both pressure and temperature conditions.. This study implements the KNN and LSTM models to predict pressure conditions in natural gas transmission pipelines to analyze the performance comparison of the best model performance using several appropriate parameters to support maximum method performance results. The results show that the LSTM model is better at predicting pressure conditions in natural gas pipeline transmission networks, with an R2 score of 99.45, compared to the KNN model, with an R2 score of 92.82. This study also obtained prediction results from the KNN and LSTM models; the KNN model tends to produce the same pressure value for eight months, while the LSTM model produces pressure values that tend to vary.

Convex Relaxations of Maximal Load Delivery for Multi-Contingency Analysis of Joint Electric Power and Natural Gas Transmission Networks

Recent increases in gas-fired power generation have engendered increased interdependencies between natural gas and power transmission systems. These interdependencies have amplified existing vulnerabilities in gas and power grids, where disruptions can require the curtailment of load in one or both systems. Although typically operated independently, coordination of these systems during severe disruptions can allow for targeted delivery to lifeline services, including gas delivery for residential heating and power delivery for critical facilities. To address the challenge of estimating maximum joint network capacities under such disruptions, we consider the task of determining feasible steady-state operating points for severely damaged systems while ensuring the maximal delivery of gas and power loads simultaneously, represented mathematically as the nonconvex joint Maximal Load Delivery (MLD) problem. To increase its tractability, we present a mixed-integer convex relaxation of the MLD problem. Then, to demonstrate the relaxation’s effectiveness in determining bounds on network capacities, exact and relaxed MLD formulations are compared across various multi-contingency scenarios on nine joint networks ranging in size from 25 to 1191 nodes. The relaxation-based methodology is observed to accurately and efficiently estimate the impacts of severe joint network disruptions, often converging to the relaxed MLD problem’s globally optimal solution within ten seconds.

The Case of Renewable Methane by and with Green Hydrogen as the Storage and Transport Medium for Intermittent Wind and Solar PV Energy

Long-duration energy storage is the key challenge facing renewable energy transition in the future of well over 50% and up to 75% of primary energy supply with intermittent solar and wind electricity, while up to 25% would come from biomass, which requires traditional type storage. To this end, chemical energy storage at grid scale in the form of fuel appears to be the ideal option for wind and solar power. Renewable hydrogen is a much-considered fuel along with ammonia. However, these fuels are not only difficult to transport over long distances, but they would also require totally new and prohibitively expensive infrastructure. On the other hand, the existing natural gas pipeline infrastructure in developed economies can not only transmit a mixture of methane with up to 20% hydrogen without modification, but it also has more than adequate long-duration storage capacity. This is confirmed by analyzing the energy economies of the USA and Germany, both possessing well-developed natural gas transmission and storage systems. It is envisioned that renewable methane will be produced via well-established biological and/or chemical processes reacting green hydrogen with carbon dioxide, the latter to be separated ideally from biogas generated via the biological conversion of biomass to biomethane. At the point of utilization of the methane to generate power and a variety of chemicals, the released carbon dioxide would be also sequestered. An essentially net zero carbon energy system would be then become operational. The current conversion efficiency of power to hydrogen/methane to power on the order of 40% would limit the penetration of wind and solar power. Conversion efficiencies of over 75% can be attained with the on-going commercialization of solid oxide electrolysis and fuel cells for up to 75% penetration of intermittent renewable power. The proposed hydrogen/methane system would then be widely adopted because it is practical, affordable, and sustainable.

Numerical Simulation of the Dovetail Tee and Hydraulic Optimization of the Height Difference for Pipeline in a Liquefied Natural Gas Filling Station

Certain configurations of liquefied natural gas refueling stations exhibit a deficiency in managing boil-off gas. Furthermore, the ill-conceived linkage between the submersible pump and the gas storage tank pipeline leads to impeded natural gas transmission. This study employed the computational fluid dynamics (CFD) methodology to scrutinize the hydrodynamic attributes of the T-type tee and dovetail tee configurations implemented in the pipeline design connecting the submersible pump and storage tank in a liquefied natural gas (LNG) filling station across diverse operational scenarios. The T-type tee induces detachment of the primary flow from the inner wall due to inertial forces, which results in vortex formation and heightened resistance, accompanied by increased energy dissipation. The transition of the rounded inner wall of the dovetail tee results in the reduction of eddy current generation and a smaller separation zone, thus minimizing resistance and energy loss. The maximum static differential pressure between the inlet and outlet of the dovetail tee is reduced by 52.52% compared to that of the T-type tee. In practical engineering applications, the use of dovetail tees leads to a reduction in the height difference for the pipeline by 17.58%, resulting in more uniform and stable flow rates and pressures in the flow field. These improvements contribute to engineering efficiency and environmental sustainability and are particularly evident in the design of LNG filling stations.

A comparison of onshore oil and gas transmission pipeline incident statistics in Canada and the United States

This study analyzes the mileage and incident data between 1995 and 2016 corresponding to the onshore oil and natural gas transmission pipelines regulated by the Canada Energy Regulator (CER) and Pipeline and Hazardous Materials Safety Administration (PHMSA) of the United States. The analysis indicates that the material/weld/equipment failure is the leading failure cause for both CER and PHMSA pipeline incidents. The annual average incident rates of the CER and PHMSA pipelines are in the order of 10−3 per km except for the PHMSA gas pipelines, the annual incident rate of which is in the order of 10−4 per km. The annual average rupture rates of the CER and PHMSA pipelines vary from 3.5 × 10−5 to 4.5 × 10−5 per km. The F-N curves for the PHMSA pipelines are developed based on the mileage and incident data to quantify the societal risks posed by the pipeline in general.