Long-distance Natural Gas Pipeline Research Articles

An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features

Pipeline feature recognition is crucial for the reliability and safety of long-distance natural gas pipelines. Utilizing manual or machine learning methods to recognize pipeline features is not only inefficient, but also has problems such as high misjudgment rate and poor robustness. To overcome the above problems, this paper proposes a pipeline feature recognition method based on Multi-scale Attention Convolutional Neural Network (MACNN) and Gated_Twins_Transformer. MACNN is used to extract multi-scale information of pipeline features, and then the attention mechanism in it to focus on the more important feature information and suppress the less important feature information. It is then transmitted to the Gated_Twins_Transformer model, which uses the gated mechanism and the twins encoder module to determine the importance of the data length and input dimensions, focusing on the feature information of both with different weights, and the Transformer enhances the extraction of global features. Finally, the measured pipeline bending strain data are used as model input, trained and tested, and compared with other advanced models, the superiority of the proposed model in this paper is verified by comparing the metrics of Accuracy, Precision, Recall and F1-score.

Study on Suitability Evaluation Method of Non-Metallic Seals in Long Distance Hydrogen-Doped Natural Gas Pipelines

Hydrogen doping using existing natural gas pipelines is a promising solution for hydrogen transportation. A large number of non-metallic seals are currently used in long-distance natural gas pipelines. Compared with metallic seals, non-metallic seals have the advantages of corrosion resistance, light weight, and easy processing, which can improve the safety and economy of pipelines. In order to ensure the long-term safe use of seals in hydrogen-doped natural gas pipelines, this paper selects the non-metallic seals commonly used in long-distance natural gas pipelines and carries out the hydrogen-doped sealing test, hydrogen-doped aging test, and hydrogen-doped anti-explosion test on the non-metallic seals under the conditions of different hydrogen-doped ratios. At the same time, combined with the actual working conditions of a hydrogen-doped natural gas pipeline, the external environment, and other factors, the applicability evaluation index system was established, and the applicability evaluation model based on hydrogen-doped physical and chemical properties, fuzzy comprehensive evaluation, and the structural entropy weight method was developed and applied in the field. The results show that the evaluation result of nitrile rubber in soft seals is 1.7845, and the evaluation result of graphite-polytetrafluoroethylene material in hard seals is 1.5988, and both of them are at a good level. This paper provides technical support and judging strategies for the selection of non-metallic sealing materials for hydrogen-doped natural gas pipelines.

Exergy and dynamics analyses in centrifugal turbomachinery pressurized long-distance natural gas pipelines based on Hamiltonian model

Natural gas pipelines represent intricate systems comprising diverse components, which is crucial to industry, yet demanding substantial energy consumption. However, the nuanced energy and exergy transfer dynamics within these pipelines, particularly during dynamic processes, remain relatively unexplored. Given the distributed nature and complicated component coupling among pipeline elements, coolers, and compressors, traditional energy analysis tools face challenges in their application. In this study, we introduce a novel approach utilizing Hamiltonian formalism to establish an energy and exergy analysis framework for natural gas pipelines. Specifically, we develop Hamiltonian energy functions for various components, including pipes, centrifugal compressors, and coolers, accounting for dynamic mechanisms. Through comprehensive simulations covering static and dynamic scenarios, we elucidate energy and exergy transfer processes, leveraging Hamiltonian energy function terms. Our findings validate the efficacy of the proposed analysis framework in accurately characterizing energy transfer and dissipation processes. Furthermore, validation with field data demonstrates the capability of our method to online exhibit energy transfer features effectively.

Experimental and numerical study on natural gas explosion evolution in open space under different initial conditions

The vapor cloud explosion in open space are greatly affected by the initial conditions of gas cloud and the surrounding environment, while in the gas compressor station, these factors are ubiquitous. While the researches of external influencing factors like the obstacle and initial turbulence effect on natural gas explosion in open space are rare. In this paper, a series of natural gas explosion experiments were conducted and a long-distance natural gas pipeline compressor station was taking as an example to simulate the natural gas leakage and explosion in the typical location. Results showed that the greater the initial turbulence, the greater the peak overpressure of gas explosion. When the initial wind speed is 5 m/s, the peak overpressure can promote 73.4 % higher than that without initial turbulence. However, the overpressure doesn't linearly increase with the increasing of initial wind speed. What's more, the flame speed showed the same law with the overpressure, and the effect of concentration on overpressure is lower than that of initial turbulence and obstacle density. The explosion damaging range in the condenser area is larger than that in the separator area, and the maximum overpressure can reach about 21 kPa and the temperature can reach above 2280K, which provides an important basis for the safety prevention and personnel protection in natural gas compressor station.

Research on Strain of Unequal Wall Thickness Pipeline of the X80 Pipeline under Lateral Load

X80 steel is a material with high strength and good toughness, which is widely used in long-distance natural gas pipelines. Pipelines will have different wall thicknesses, depending on the safety level requirements of different regions. Two sections of pipes with different wall thickness form unequal wall thickness of pipeline (UWTP) that is joined together by welding. UWTP pass through some geohazard areas, such as landslides. The frequency of landslides is extremely high in mountainous areas, which can seriously affect the safe operation of UWTP. In this paper, a model of a 12.8 mm wall pipe section and a 15.6 mm wall pipe section are linked by girth welds. The strain between the pipe section and the weld was quantitatively analyzed. The results show that the strain at the girth weld in the 3 o’clock direction of the pipe increases sharply. The strain in the 9 o’clock direction of the pipe is generally lower than the strain in the 3 o’clock direction. The strain value of the 12.8mm wall thickness pipe section is generally greater than the strain value of the 15.6mm wall thickness pipe section.

Optimization of Emergency Resource Dispatch for Overseas Long-distance Natural Gas Pipeline Leaks

Overseas long-distance natural gas pipelines usually span across multiple regions or even countries, facing more severe environmental conditions and more prone to leakage and other accidents, and the study of the optimization method of emergency resource scheduling for overseas long-distance natural gas pipeline leakage accidents can help to reduce the casualties and property losses. Therefore, this paper establishes an emergency resource scheduling model in line with the emergency disposal mechanism of overseas long-distance natural gas pipeline leakage accidents, takes into account the variability of the operating ability of maintenance personnel, and improves the accuracy of the scheduling program to a certain extent, and also considers the actual environment of overseas long-distance natural gas pipelines, takes into account the impact of landslides on the emergency scheduling time, and calculates the probability of landslides occurring in the landslide point by using the interpolated value of rainfall The rainfall interpolation is used to calculate the probability of landslide at the landslide point, and the emergency dispatching time is corrected to make the dispatching program more in line with the actual demand. The analysis of the results shows that the emergency resource scheduling time is dominant in the case of small amount of maintenance and repair work and short total maintenance and repair time.

Evaluation of disaster-bearing capacity for natural gas pipeline under third-party damage based on optimized probabilistic neural network

Third-party damage has become a significant factor leading to pipeline failures and leaks, resulting in severe ecological damage and human casualties. Traditional safety risk analysis methods in the past have focused on severing the causal chain of accidents, while neglecting the unique characteristics of different pipeline segments and environmental factors on their ability to bearing disasters. This paper presents a framework for evaluating the disaster-bearing capacity of natural gas pipeline third-party damage based on probabilistic neural networks. A comprehensive disaster-bearing capacity index system for evaluating third-party damage to natural gas pipelines has been constructed by analyzing the causal factors and potential consequences of third-party damage risk incidents. Next, the cluster analysis and indicator weighting information is used for achieving the comprehensive disaster-bearing capacity assessment. Finally, the data samples are constructed with the index levels and comprehensive disaster-bearing capacity levels of each evaluation unit. An optimization algorithm, Particle Swarm Optimization (PSO), is utilized to improve the parameter selection process of the Probabilistic Neural Network (PNN) model, in order to evaluate the disaster-bearing level of each unit. A typical long-distance natural gas pipeline in Zhejiang Province was selected as the case study, and a total of 200 research units were obtained through pipeline analysis. Furthermore, 14 indicator data were obtained by leveraging multiple sources of data surrounding the pipeline. With the utilization of cluster analysis, it was determined that Gaussian mixed model clustering method ultimately identified a total of 5 different levels of disaster-bearing capacity. The optimal smoothing factor can be accurate from 0.5 to 0.588 by using the PSO-PNN model, and the final accuracy rate is improved from 88% to 98%, which is obviously the introduction of PSO optimization algorithm in the PNN model can improve the accuracy of the model. This represents a significant improvement in accuracy when compared to traditional PNN models. The proposed evaluation method can provide scientific basis for the prevention and control of third-party damage in oil and gas pipelines and pipeline planning.

Multi-parameter distributed fiber optic sensing using double-Brillouin peak fiber in Brillouin optical time domain analysis.

In this paper, we demonstrate a multi-parameter fiber sensing system based on stimulated Brillouin scattering in a double-Brillouin peak specialty fiber with enhanced Brillouin gain response. The amplitude level of the second Brillouin gain peak, which originated from the higher-order acoustic modes, has been improved with an approximately similar amplitude level to the first Brillouin gain peak from the fundamental acoustic mode. Compared to other multi-Brillouin peak fibers presented in the literature, the proposed fiber significantly reduces the measured Brillouin frequency shift error, thus improving strain and temperature accuracies. By utilizing the sensitivity values of the strain and temperature associated with each Brillouin gain spectrum (BGS) peak, a successful discriminative measurement of strain and temperature is performed with an accuracy of ±13 μɛ, and ±0.5 °C, respectively. The proposed double-Brillouin peak fiber appears to be a possible alternative to other multi-BGS peak fibers, for instance, large effective area fiber and dispersion compensating fibers, which are inherently accompanied by large measurement errors due to the weak Brillouin gain values originating from the higher-order acoustic modes. The demonstrated results show different strain and temperature coefficients of 47 kHz/µɛ, 1.15 MHz/°C for peak 1 and 51 kHz/µɛ, 1.37 MHz/°C for peak 2. Moreover, the enhanced BGS peak gains having nearly the same amplitude levels enable the discriminative measurement of strain and temperature. Such fibers in Brillouin interrogation eliminate the need for complex monitoring setups and reduce measurement errors. We recommend that for long-distance natural gas pipeline monitoring, where discriminative strain and temperature measurement is crucial, the proposed double-Brillouin peak fiber can be highly beneficial.

Function Optimization of Self-shutoff Protection System for -Valve Chambers of Long-distance Natural Gas Pipelines

The rate of pressure drop set value of shut-off valve in gas pipeline is uncertain to a certain extent, and the function of self-shutoff protection may disable under some leakage conditions. A gas pipeline leakage model is established using simulation software, to simulate the leakage, flow rate reduction, and crossover, and obtain the pressure changes in upstream and downstream shut-off valve chambers under different working conditions[1]-[3]. The rate of pressure drop is calculated by using the rate of pressure drop algorithm adopted by Line Guard 2300 system. Simulation results indicate that the gas transmission volume has little influence on the rate of pressure drop, and the leakage diameter has great influence on the rate of pressure drop, showing a positive correlation. When the pipeline runs in parallel, it is necessary to avoid opening the crossover valve under large pressure difference. Finally, the recommended rate of pressure drop threshold at the trunk line shut-off valve chamber is given, and the leakage condition of 1/2 diameter at 32km upstream can be detected under this recommended value.The simulation software is first applied in multinational pipelines, which can effectively protect pipeline defect points. Reduced methane cold venting, greatly improving the contribution to achieving emission reduction goals.

Using the meteorological early warning model to improve the prediction accuracy of water damage geological disasters around pipelines in mountainous areas

This paper focuses on the threat of water damage geological disasters brought by the complex terrain along the long-distance natural gas pipeline. The role of rainfall factors in the occurrence of such disasters has been fully considered, a meteorological early warning model for water damage geological disasters in mountainous areas based on slope units has been constructed to improve the prediction accuracy of such disasters and timely early warning and forecasting. An actual natural gas pipeline in a typical mountainous area of Zhejiang Province is taken as an example. The hydrology-curvature combined analysis method is chosen to divide the slope units, and the SHALSTAB model is used to fit the slope soil environment to calculate the stability level. Finally, the stability level is coupled with rainfall data to calculate the early warning index for water damage geological disasters in the study area. The results show that compared with the separate SHALSTAB model, the early warning results coupled with rainfall are more effective in predicting water damage geological disasters. The early warning results are compared with the actual disaster points, among the nine actual disaster points, most of the slope units around seven disaster points are in the state of needing early warning, the early warning accuracy rate reaches 77.8 %. The proposed early warning model can carry out targeted deployment in advance according to the divided slope units, and the prediction accuracy of geological disasters induced by heavy rainfall weather is significantly higher and more suitable for the actual location of the disaster point, which can provide a basis for accurate disaster prevention in the research area and areas with similar geological environments.

Heat Transfer Model of Natural Gas Pipeline Based on Data Feature Extraction and First Principle Models

The rapid development of natural gas pipelines has highlighted the need to utilize SCADA (supervisory control and data acquisition) system data. In this paper, a heat transfer model of a natural gas pipeline based on data feature extraction and first principle models, which makes full use of the measured temperatures at each end of the pipeline, is proposed. Three methods, the NARX neural network (nonlinear autoregressive neural network with exogenous inputs), time series decomposition, and system identification, were used to model the changes of gas temperatures of the pipeline. The NARX neural network method uses a cyclic neural network to directly model the relationship of temperature between the start and the end of the pipeline. The measured temperature series at the pipeline inlet and outlet were decomposed into trend items, fluctuation items, and noise items based on the time series decomposition method. Then the three items were fitted separately and combined to form a new temperature prediction series. The system identification method constructed the first-order and second-order transfer function to model the temperature. The simulation of the three data-driven models was compared with those of the physics-based simulation models. The results showed that the data-driven model has great advantages over the physics-based simulation models in both accuracy and efficiency. The proposed models are more suitable for applications such as online simulation and state observation of long-distance natural gas pipelines.

Numerical Simulation of Mechanical Behavior of Gas Pipeline in High and Steep Slope

Long-distance natural gas pipelines can often pass through the common slope safely. However, when pipelines inevitably pass through high and steep slopes(HSS) for some reasons, it is difficult for engineers to predict the stress state of pipelines. Therefore, in order to investigate the safety of natural gas pipelines in HSS areas, the finite element simulation calculation model of the pipeline in HSS was established. The predicted numerical model shows that the maximum Von Mises stress and axial strain of the pipeline in the HSS area appear at the lower elbow of the pipeline, which should be paid more attention to in the risk assessment; The stress and deformation of the pipeline is mainly related with the wall thickness and internal pressure, appropriately increasing the wall thickness and reducing the internal pressure can improve the safety of HSS pipelines.

Research on explosion characteristics and consequences of compressor room in compressor station of long-distance natural gas pipeline

As the core place in the compressor station, the compressor room has the possibility of gas leakage and explosion during its operation. In this paper, an equal proportional physical model is established based on FLCAS software, the effects of different stoichiometric ratios on the explosion temperature, flame propagation velocity and overpressure in the compressor room are investigated. The results show that when the stoichiometric ratio is in the range of 0.8–1.4, the indoor temperature peak, flame propagation velocity and peak overpressure generated by the explosion in the compressor room all increase first and then decrease with the increase of the stoichiometric ratio. The outdoor temperature peak and the flame propagation velocity increase with the increase of the stoichiometric ratio in the range of 0.8–1.4, and the flame acceleration during the flame propagation process can effectively promote the increase of the explosion overpressure inside the flame surface. In addition, in order to avoid more serious consequences in the event of an explosion in the compressor room, a fire sprinkler system should be laid between the adjacent compressor room doors, and the doors of the adjacent compressor rooms should be installed in staggered positions to prevent the occurrence of secondary danger.

Management and Safety Control of Long-distance Natural Gas Pipeline Construction Project

Management and Safety Control of Long-distance Natural Gas Pipeline Construction Project

Exploration on Centralized Monitoring and Management Operation of Long-distance Natural Gas Pipeline

Exploration on Centralized Monitoring and Management Operation of Long-distance Natural Gas Pipeline

Self-adapting anti-surge intelligence control and numerical simulation of centrifugal compressors based on RBF neural network

Anti-surge control of centrifugal compressors is an essential issue for the operation of long-distance natural gas pipeline systems. A suitable controller can make a centrifugal compressor runs smoothly and stably and improve the economy. This work presents a new intelligence control strategy with self-adapting ability. The strategy includes the proportional integral (PI) control self-tuned by radial basis function neural network (RBF-NN), recycle trip control, special derivative control, surge line correction, and asymmetric output of the controller. A hybrid numerical simulation platform is built to validate the anti-surge strategy, and a real centrifugal compressor is simulated. The results show that the strategy makes the anti-surge valve respond quickly, decreases the surge control line’s margin and backflow rate, and improves the economy. In the controller, the special derivative control can make the anti-surge valve open earlier and effectively reduce the fluctuating of inlet flow rate. Aiming at the problem that the gradient descent method is more sensitive to the initial value when solving RBF-NN, a hybrid algorithm of k-means, recursive least square, and gradient descent (KRG algorithm) is proposed. It is successfully applied in the anti-surge controller. Even if the given RBF-NN initial parameters are not good enough, the KRG algorithm illustrates good learning stability and increases the adaptive ability of RBF-NN.

Techno-economic assessment of low-carbon hydrogen export from Western Canada to Eastern Canada, the USA, the Asia-Pacific, and Europe

The use of low-carbon hydrogen is being considered to help decarbonize several jurisdictions around the world. There may be opportunities for energy-exporting countries to supply energy-importing countries with a secure source of low-carbon hydrogen. The study objective is to assess the delivered cost of gaseous hydrogen export from Canada (a fossil-resource rich country) to the Asia-Pacific, Europe, and inland destinations in North America. There is a data gap on the feasibility of inter-continental export of hydrogen from an energy-producing jurisdiction to energy-consuming jurisdictions. This study is aimed at addressing this gap and includes an assessment of opportunities across the Pacific Ocean and the Atlantic Ocean, based on fundamental engineering-based models. Techno-economics were used to determine the delivered cost of hydrogen to these destinations. The modelling considers energy, material, and capacity-sizing requirements for a five-stage supply chain comprising hydrogen production with carbon capture and storage, hydrogen pipeline transportation, liquefaction, shipping, and regasification at the destinations. The results show that the delivered cost of hydrogen to inland destinations in North America is between CAD$4.81/kg and CAD$6.03/kg, to the Asia-Pacific from CAD$6.65/kg to CAD$6.99/kg, and at least CAD$8.14/kg for exports to Europe. Delivering hydrogen by blending in existing long-distance natural gas pipelines reduced the delivered cost to inland destinations by 17%. Exporting ammonia to the Asia-Pacific provides cost savings of 28% compared to shipping liquified hydrogen. The developed information may be helpful to policymakers in government and the industry in making informed decisions about international trade of low-carbon hydrogen in both energy-exporting and energy-importing jurisdictions, globally.

Study on influencing factors and monitoring cycle of wall thickness at key locations of natural gas station

In recent years, the safe and stable operation of natural gas pipeline has become the focus of the whole society. Long distance natural gas pipelines are mostly large-diameter and high pressure pipelines, so their safe operation is particularly critical. In the high-pressure natural gas pipeline transportation industry, the wall thickness of key points (elbows and tees) will be thinned due to long-term erosion of gas, resulting in serious consequences. Therefore, its wall thickness monitoring is a key work of long-distance natural gas pipeline operation units. At present, most of them adopt off-line monitoring, but the standardized monitoring cycle cannot be effectively determined, and there are no relevant standards in the industry. The author uses fluent simulation software to establish the model, and then modifies the model combined with the field data. Then, the model is used to analyze the influencing factors of key points (elbow and TEE) one by one, and select more important influencing factors to formulate the monitoring cycle of different points. The monitoring cycle can provide reference for the daily work of on-site operators, and then ensure the safe and stable operation of long-distance natural gas pipeline.

Research on Explosion Characteristics and Consequences of Compressor Room in Compressor Station of Long-Distance Natural Gas Pipeline

Research on Explosion Characteristics and Consequences of Compressor Room in Compressor Station of Long-Distance Natural Gas Pipeline

Environmental analysis for the selection of long-distance natural gas pipeline routes using MACBETH

PurposeReliable operation of natural gas pipeline (NGP) is a critical factor in Iran’s economic development. NGP projects go through different ecosystems and considerably affect the environment. Environmental analysis is an essential step toward sustainable development. Tackling the challenges, this paper aims to develop a framework to systematically assess the environmental impact of NGPs.Design/methodology/approachThis study proposes a comprehensive framework for environmental impact analysis of NGPs using Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH). MACBETH protocol is used to calculate the overall environmental scores of projects.FindingsThe results indicate that the impact of implementing NGPs on protected areas is of the highest priority, while the impact on vegetation covers is of least priority for assessing the environmental impact of NGP.Practical implicationsThe practicality and validity of the model in the case of three candidate routes for the Polkale-Neizar project in Iran are examined. According to the results, the third alternative is selected based on its non-interference in protected areas and less environmental impacts. The proposed model can be modified and applied to perform environment appraisal of other linear projects such as energy, road and railway networks.Originality/valueThis model addresses a range of environmental impacts of implementing NGPs at two levels, with the second level being non-additive. The novelty of this study translates into considering the qualitative and quantitative features of each evaluation criterion applied to linear projects simultaneously using a multi-criteria value measurement.