Natural Gas Transportation Research Articles

Stable heterometallic metal-organic frameworks and pellet composites for low-temperature natural gas storage

Liquefied natural gas coupled with adsorbed natural gas charging (LNG-ANG) offers an efficient solution for recycling boil-off gas, providing a promising strategy for the competent storage and transportation of natural gas. The key challenge in LNG-ANG coupling systems is identifying adsorbents that exhibit superior methane adsorption performance and excellent stability under cryogenic conditions (∼159 K). In this work, we systematically investigated the methane adsorption performance of a stable metal–organic framework, PCN-250-Fe2Ni, under LNG-ANG related conditions. The results demonstrated that PCN-250-Fe2Ni exhibited a high methane capacity of 286 cm3 (standard temperature and pressure, STP) cm−3 (0.227 g g−1) at 6 bar/159 K. Furthermore, the pellet composite of PCN-250-Fe2Ni and polyvinyl butyral also displayed a high methane adsorption capacity with excellent cycling stability. Combined with grand canonical Monte Carlo simulations, our findings revealed that the cage-type pores serve as initial methane adsorption sites, followed by the channel-type pores, both of which contribute to the framework’s high low-temperature methane storage performance at saturation.

A rapid method for composition tracking in hydrogen-blended pipeline using Fourier neural operator

Blending hydrogen into natural gas for transportation is a crucial approach for achieving the widespread utilization of hydrogen. Tracking the concentration of the hydrogen within the pipeline is important for monitoring gas quality and managing pipeline operations. This study develops a rapid computational model to predict the hydrogen and natural gas concentrations within the pipeline during transportation based on the Fourier Neural Operator (FNO), an operator neural network capable of learning the differential operator in the partial differential equation. In the proposed model, the numerical method is employed to generate datasets, with the spline interpolation used to enhance data smoothness. The initial and boundary conditions are taken as the inputs to accommodate varying transportation scenarios. Comparison results indicate that the proposed model can notably reduce the time needed to predict the hydrogen and natural gas concentrations while maintaining prediction accuracy. The accuracy of the proposed model is validated by comparing its calculated results with the analytical solution and the concentrations of hydrogen and natural gas within the pipeline under two transportation scenarios, with relative errors of 0.49%, 0.31%, and 0.45%, respectively. Notably, the trained model demonstrates strong grid invariance, a type of model generalization. Trained on data generated from a coarse grid of 101 × 41 spatial-temporal resolution, the proposed model can accurately predict results on a fine grid of 401 × 81 spatial-temporal resolution with a relative error of only 0.38%. Regarding the prediction efficiency, the proposed model achieves an average 17.7-fold speedup compared to the numerical method. The positive results indicate that the proposed model can serve as a rapid and accurate solver for the composition transport equation.

Tourism activities significantly contributed toatmospheric pollutants in a remote town of Tianshan revealed by the continuous observation of two years

The large reduction in tourism activities in 2020 provided a unique opportunity to investigate their contribution on atmospheric pollutants in Bayanbulak, a remote town in Tianshan, China. In this scenic and sensitive region, we measured total suspended particulates (TSP) along with concentrations of polycyclic aromatic hydrocarbons (PAHs), organic carbon (OC), elemental carbon (EC) and water-soluble inorganic ions (WSIIs) during the lockdown in 2020 and compared these to levels in the pre-pandemic period of 2019. The study revealed significant reductions in PAHs, OC, EC, and some WSIIs concentrations during the tourist season (May–October) of 2020 compared with 2019, whereas variations between non-tourist seasons were less pronounced. Furthermore, by comparing pollutant characteristics in tourist seasons of these two years, we found that 5- and 6-ring groups were main PAH constituents in 2019, while 5- and 3-ring groups contributed more to total PAHs in 2020. OC/EC ratio increased from 2019 to 2020, indicating an increase of biomass burning contribution after the lockdown. Using positive matrix factorization, we determined that emissions from tourism-related transportation and combustion of natural gas and coal (mainly for cooking) contributed 21.9% and 46.6%, respectively, to certain atmospheric pollutants in 2019, whereas they decreased significantly to 3% and 19.4% in 2020 with dust and biomass contributions increased substantially. The levels of PAHs, OC, and EC from tourism-related sources were as high as 9.31 ng m−3, 1.28 μg m−3 and 0.11 μg m−3 and decreased by 54.37%, 25.16%, and 9.54%, respectively, compared to 2019. Hence, the impact of tourism must be considered during the assessment of local air pollutant emissions, particularly in remote areas.

Investigation on the methane emissions from permeation of urban gas polyethylene pipes under the background of hydrogen-mixed natural gas transportation

Methane, a more potent greenhouse gas than carbon dioxide, aggravates the greenhouse effect of ecological environment through its emissions. When transporting hydrogen-mixed natural gas through urban polyethylene pipes, methane will be emitted due to the permeation characteristic of polyethylene pipe material. Therefore, it is crucial to pay attention to the methane emissions from permeation of polyethylene pipes for protecting the environment and climate. In this paper, the solubility and diffusion coefficients of methane within hydrogen-mixed natural gas in polyethylene pipe materials are calculated through the Monte Carlo method and molecular dynamics simulation to determine the permeation coefficient of methane. Simultaneously, the methane emissions and equivalent carbon dioxide emissions of the hydrogen-mixed natural gas in polyethylene pipes are investigated. Results indicate the methane solubility coefficient decreases with increasing temperature and hydrogen mixing ratio. The methane diffusion and permeation coefficients increase with the rising temperature and pressure, and decreasing hydrogen mixing ratio. The pressure exerts minor influence while the temperature and hydrogen mixing ratio have more significant impact. That is, the lower the temperature and the higher the hydrogen mixing ratio, the less methane emissions caused by permeation. Therefore, the effective reduction of methane emissions can be achieved by adding the insulation layer to mitigate the impact of high temperature and increasing the hydrogen mixing ratio. For example, for the polyethylene pipe with the operating pressure of 4 bar and the size specification of SDR11, when the temperature drops from 300 K to 260 K, the methane emissions of hydrogen-mixed natural gas with hydrogen mixing ratios of 0 vol%, 20 vol%, 50 vol% and 80 vol% will be drastically reduced by 73.3%, 74.4%, 77.2% and 78.5% respectively, and when 20 vol%∼80 vol% hydrogen are mixed in the natural gas at 260 K, 300 K and 310 K, the methane emissions will be effectively reduced by 26.7%∼85.6%, 23.3%∼82.1% and 23.9%∼82.4% respectively. The findings of this study can serve as guidance for reducing the methane emissions from permeation of polyethylene pipes transporting hydrogen-mixed natural gas.

CONCESSÃO DO SERVIÇO DE GÁS CANALIZADO: ASPECTOS JURÍDICOS, REGULATÓRIOS E ECONÔMICOS

The concession of natural gas services in Brazil is regulated by the Federal Constitution, the Concessions Law (Law No. 8,987/1995) and the Gas Law (Law No. 14,134/2021), which regulates the transportation and commercialization of natural gas. With the approval of the New Gas Law (Law No. 14,134/2021), the sector underwent significant changes, aiming to promote competition and attract new private investments. The legislation seeks to guarantee the economic-financial balance of contracts, free access to infrastructure and reasonable tariffs. The gas distribution service is the responsibility of the States, and supervision is carried out by regulatory agencies. Recent jurisprudence from the Superior Court of Justice (STJ) has reaffirmed the need for efficiency and continuity in service, in addition to ensuring respect for consumer rights and balance between concessionaires and the granting authority. The evolution of the gas regulatory framework is an important step towards the country’s sustainable and energy development.

A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning

Natural gas is an emerging and reliable energy source in transition to a low-carbon economy. The natural gas transportation pipeline network systems are crucial when transporting natural gas from the production endpoints to processing or consuming endpoints. Optimizing the operational efficiency of compressor stations within pipeline networks is an effective way to reduce energy consumption and carbon emissions during transportation. This paper proposes an optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning (DRL). The mathematical simulation model is derived from mass balance, hydrodynamics principles of gas flow, and compressor characteristics. The optimization control problem in steady state is formulated into a one-step Markov decision process (MDP) and solved by DRL. The decision variables are selected as the discharge ratio of each compressor. By the comprehensive comparison with dynamic programming (DP) and genetic algorithm (GA) in three typical element topologies (a linear topology with gun-barrel structure, a linear topology with branch structure, and a tree topology), the proposed method can obtain 4.60% lower power consumption than GA, and the time consumption is reduced by 97.5% compared with DP. The proposed framework could be further utilized for future large-scale network optimization practices.

Transportation of Liquefied Natural Gas (LNG) via Long-Distance Low-Temperature Pipeline and Cascade Utilization of Cold Energy

In addition to methane, liquefied natural gas (LNG) typically contains valuable light hydrocarbons such as ethane, propane, and butane. Establishing LNG receiving stations for imported LNG can effectively alleviate regional energy supply-demand conflicts, optimize energy consumption structures, promote sustainable regional economic development, and improve ecological environments. The technology for long-distance low-temperature pipeline transportation of LNG facilitates the efficient transfer and cascading utilization of the cold energy contained in LNG, allowing for deep coupling with cold networks and natural gas pipelines, thereby promoting the integrated development of LNG, cold energy, and natural gas transportation. This paper systematically summarizes the current status of LNG long-distance low-temperature pipeline transportation technology, cold energy cascading utilization technology, pipeline regulation technology, and multi-network integration technology. It analyzes the key challenges these technologies face under new strategies and looks ahead to future development trends, aiming to provide scientific insights and directions for the application of LNG long-distance low-temperature transportation and multi-network integration in China.

Hydrate formation and its influence on natural gas pipeline: Simulation study

Natural gas transportation is plagued with the twin problem of hydrate and corrosion that occurs when a hydrate prone gas is conveyed in such a manner that the pipeline operating conditions fall within hydrate formation region of the hydrate phase envelop. This study simultaneously studied this twin problem, establishing their interdependent using gas sample from Niger Delta that was meticulously simulated and analysed for hydrate formation possibilities with the aid of Unism software. CO2 corrosion was simulated using NORSOK M-506 standard model in matlab. Major factors considered are the relationship between corrosion rate and temperature, corrosion rate and PH, corrosion-temperature relationship for varying CO2 mole percent, and PH values. The result from this study established that both type I and type II hydrates could form at the operating conditions of 5OC and 60 bar. Rate of corrosion decreases and increases with increase in PH and temperature respectively to a certain temperature of approximately 78 OC, then a dip in rate of corrosion. Corrosion-temperature relationship for varying PH and CO2 mole percent shows a decrease in corrosion rate with an in increase in PH, and an increase with increase in CO2 mole percent, with a rise as high as 5,7 mm/year at a 3 mole percent. This value and trend portray a bad omen for the affected pipeline. This study recommends that natural gas to be transported by pipeline should be sweetened and processed to remove H2S, CO2 and mercaptans if present and also to satisfy maximum dew point specification.

Identification of gas-liquid two-phase flow patterns based on flexible ultrasound array and machine learning

Ultrasound technology has been recognized as the mainstream approach for the identification of gas-liquid two-phase flow patterns, which holds great value in engineering domain. However, commercial rigid probes are bulky, limiting their adaptability to curved surfaces. Here, we propose a strategy for autonomous identification of flow patterns based on flexible ultrasound array and machine learning. The array features high-performance 1–3 piezoelectric composite material, stretchable serpentine wires, soft Eco-flex layers and a polydimethylsiloxane (PDMS) adhesive layer. The resulting ultrasound array exhibits excellent electromechanical characteristics and offers a large stretchability for an intimate interfacial contact to curved surface without the need of ultrasound coupling agents. We demonstrated that the flexible ultrasound array combined with machine learning can accurately identify gas-liquid two-phase flow patterns, in a circular pipeline. This work presents an effective tool for recognizing gas-liquid two-phase flow patterns, offering engineering opportunities in petroleum extraction and natural gas transportation.

Prediction of wellbore gas hydrate generation based on temperature-pressure coupling model in deepwater testing

With the advancement of oil and gas exploration and development technology, the development of the world's oil and gas resources is gradually advancing from land and shallow waters to deep waters. During the development of gas wells in deepwater regions, the temperature of the wellbore decreases from the bottom of the well to the wellhead, and therefore, common well conditions of high pressure, low temperature, and the presence of free water are encountered, which can lead to gas hydrate plugging of the pipeline and affect the transportation of natural gas. Simulation of temperature and pressure along the wellbore and identification of gas hydrate-generating zones in the wellbore can be used to take timely preventive measures and minimize losses. In this study, a wellbore gas hydrate generation and prediction model is developed by combining the mass, momentum, and energy conservation equations, and the model is solved by the fourth-order Runge-Kutta method, taking into account the temperature-pressure coupling in the wellbore. The effects of gas production, specific heat capacity, thermal conductivity, tubing size, test duration, and ground temperature gradient on the location of gas hydrate generation and the amount of inhibitor used were analyzed in a case study well in the South China Sea. Some suggestions for practical engineering operations, such as increasing the gas production in moderation and choosing smaller tubing sizes within the acceptable range, were given. The study's results can provide a theoretical basis for flow assurance for hydrocarbon production in the wellbore of deepwater gas wells.

Issues of the Russian liquefied natural gas market in the context of sanctions pressure and ways to solve them

Liquefied natural gas export is currently a rather promising sector of the Russian fuel and energy complex. Gas transportation by special gas carriers makes it possible to supply the resource independently of the pipeline system. In recent years, the liquefied natural gas market role has been significant for the Russian economy. This industry has not been subject to significant export restrictions by other countries, unlike pipeline gas exports. However, under the conditions of sanctions imposed by “unfriendly” countries on the supply of technologies and equipment required for liquefied natural gas production and transportation, not only the industry development, but also implementation of the Energy Strategy of the Russian Federation for the period up to 2035 is threatened, especially major projects implementation on the Arctic coast of the country. All of the above speaks for the relevance of the study, the objectives of which are to assess the Russian market of liquefied natural gas in the context of the introduced technological restrictions, to identify the key problems arising as a result of this unfavorable situation and to determine the directions for the industry further development.

Modeling and assessment of hydrogen-blended natural gas releases from buried pipeline

Blending hydrogen into natural gas pipelines for transportation reduces costs but increases safety risks. The main objective of this work is to explore the evolution of leakage hazards in hydrogen-blended natural gas pipelines and quantify these risks. To achieve this, multi-stage coupling numerical models are developed to explore the leakage hazards in hydrogen-blended natural gas transportation. The results show that the proposed model accurately predicts leakage rates of hydrogen-blended natural gas pipelines in various soil types. The gas diffusion pattern shifts from spherical to hemispherical, with the hazardous volume increasing linearly overall. The influence of pressure on leakage is amplified in the soil, while the impact of the leakage aperture is diminished. Although increasing the hydrogen blending ratio reduces the leakage rate, it also enhances the hazard evolution. Burial depth primarily affects alarm time, while an increase in leakage angle within 90–180° significantly delays the hazard evolution. Moreover, higher soil resistance not only mitigates the leakage diffusion but also reduces the pressure differential. The leakage aperture exhibits the highest correlation with the hazard evolution.

Investigation into the Simulation and Mechanisms of Metal-Organic Framework Membrane for Natural Gas Dehydration.

Natural gas dehydration is a critical process in natural gas extraction and transportation, and the membrane separation method is the most suitable technology for gas dehydration. In this paper, based on molecular dynamics theory, we investigate the performance of a metal-organic composite membrane (ZIF-90 membrane) in natural gas dehydration. The paper elucidates the adsorption, diffusion, permeation, and separation mechanisms of water and methane with the ZIF-90 membrane, and clarifies the influence of temperature on gas separation. The results show that (1) the diffusion energy barrier and pore size are the primary factors in achieving the separation of water and methane. The diffusion energy barriers for the two molecules (CH4 and H2O) are ΔE(CH4) = 155.5 meV and ΔE(H2O) = 50.1 meV, respectively. (2) The ZIF-90 is more selective of H2O, which is mainly due to the strong interaction between the H2O molecule and the polar functional groups (such as aldehyde groups) within the ZIF-90. (3) A higher temperature accelerates the gas separation process. The higher the temperature is, the faster the separation process is. (4) The pore radius is identified as the intrinsic mechanism enabling the separation of water and methane in ZIF-90 membranes.

Prediction of hydrate formation boundaries in pure water and salt/alcohol containing systems based on prior knowledge and artificial intelligence

As an efficient and clean energy source, natural gas plays a crucial role in optimizing the global energy consumption structure. However, the formation and accumulation of hydrates in pipelines during the exploitation and transportation of natural gas greatly jeopardize the production safety. Accurately predicting hydrate formation boundaries remains challenging due to the complex interation among various influencing factors. Although mechanistic models provide insights, they often fall short in accuracy. Conversely, machine learning models, while promising, may face limitations related to small sample sizes and a lack of physical interpretability. To overcome these limitations, this study proposed a physically guided neural network (PGNN) based on the Chen-Guo model, which integrated thermodynamic mechanisms with a neural network framework. This proposed model utilized pressure calculated from the Chen-Guo model as an input variable and incorporates physical inconsistencies into its loss function. A comparative analysis using literature data that PGNN achieved superior prediction accuracy, with an R2 value of 0.9768 for gas hydrate formation pressure prediction. The integration of thermodynamic mechanisms enhanced the prediction accuracy, as evidenced by an increase in the R2 value by 0.036, and a reduction in the MSE value by 5.56. Furthermore, the PGNN maintained a high level of prediction accuracy, ranging from 0.96 to 0.98, even with limited sample sizes, thus confirming its applicability and stability. This study validated the feasibility of PGNN for predicting gas hydrate formation conditions and offered insights into hydrate-based applications and hydrate management strategies.

The Impact of Retrofitting Natural Gas-Fired Power Plants on Carbon Footprint: Converting from Open-Cycle Gas Turbine to Combined-Cycle Gas Turbine

Since retrofitting existing natural gas-fired (NGF) power plants is an essential strategy for enhancing their efficiency and controlling greenhouse gas emissions, this paper compares the carbon footprint of natural gas-fired power generation from an NGF power plant in Brazil (BR-NGF) with and without retrofitting. The former scenario entails retrofitting the BR-NGF power plant with combined-cycle gas turbine (CCGT) technology. In contrast, the latter involves continuing the BR-NGF power plant operation with open-cycle gas turbine (OCGT) technology. Our analysis considers the BR-NGF power plant’s life cycle (construction, operation, and decommissioning) and the natural gas’ life cycle (natural gas extraction and processing, liquefaction, liquefied natural gas transportation, regasification, and combustion). Moreover, it is based on data from primary and secondary sources, mainly the Ecoinvent database and the ReCiPe 2016 method. For OCGT, the results showed that the BR-NGF power plant and the natural gas life cycles are responsible for 620.87 gCO2eq./kWh and 178.58 gCO2eq./kWh, respectively. For CCGT, these values are 450.04 gCO2eq./kWh and 129.30 gCO2eq./kWh. Our findings highlight the relevance of the natural gas’ life cycle, signaling additional opportunities for reducing the overall carbon footprint of natural gas-fired power generation.

Sulfide stress corrosion cracking in L360QS pipelines: A comprehensive failure analysis and implications for natural gas transportation safety

This research investigates the severe weld failures in L360QS pipelines, which are integral to the infrastructure of high-pressure natural gas transportation systems. Even with strict compliance to engineering standards, two pivotal welds experienced sulfide stress corrosion cracking (SSCC) due to continuous exposure to hydrogen sulfide and stresses from construction activities. A comprehensive material analysis and stress simulation has been utilized, revealing a maximum stress concentration of 544.9 MPa that surpasses the yield strength of the pipeline material. This discovery prompts a reevaluation of current safety margins and underscores the urgent need for enhanced pipeline integrity management. Our findings, corroborated by stress distribution simulations, not only illuminate the complex interplay of material properties and environmental factors in SSCC but also provide a new perspective for the safe service of pipeline.

Micro–Nano 3D CT Scanning to Assess the Impact of Microparameters of Volcanic Reservoirs on Gas Migration

Volcanic rock reservoirs for oil and gas are known worldwide for their considerable heterogeneity. Micropores and fractures play vital roles in the storage and transportation of natural gas. Samples from volcanic reservoirs in Songliao Basin, CS1 and W21, belonging to the Changling fault depression and the Wangfu fault depression, respectively, have similar lithology. This study employs micro–nano CT scanning technology to systematically identify the key parameters and transport capacities of natural gas within volcanic reservoirs. Using Avizo 2020.1software, a 3D digital representation of rock core was reconstructed to model pore distribution, connectivity, pore–throat networks, and fractures. These models are then analyzed to evaluate pore/throat structures and fractures alongside microscopic parameters. The relationship between micropore–throat structure parameters and permeability was investigated by microscale gas flow simulations and Pearson correlation analyses. The results showed that the CS1 sample significantly exceeded the W21 sample in terms of pore connectivity and permeability, with connected pore volume, throat count, and specific surface area being more than double that of the W21 sample. Pore–throat parameters are decisive for natural gas storage and transport. Additionally, based on seepage simulation and the pore–throat model, the specific influence of pore–throat structure parameters on permeability in volcanic reservoirs was quantified. In areas with well–developed fractures, gas seepage pathways mainly follow fractures, significantly improving gas flow efficiency. In areas with fewer fractures, throat radius has the most significant impact on permeability, followed by pore radius and throat length.

Decoupling and predicting natural gas deviation factor using machine learning methods

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

Action plan proposal to obtain triple certification in a natural gas transportation company

Goal: for large gas transportation companies, it is of strategic relevance to be compliant with ISO 9001, ISO 45001, and ISO 14001 certifications, acknowledging their Integrated Management System (IMS). Based on this context, the purpose of this study was to propose an action plan to obtain triple certification for a Brazilian natural gas transportation company. Design / Methodology / Approach: the methodology comprised research of applied nature, exploratory and descriptive objectives, qualitative approach, with a single case study investigation strategy, and data collected through triangulation, combining documents, interviews, and field observation. Results: First, an action plan, which enabled the company to achieve triple certification in ISO 9001, ISO 45001, and ISO 14001 standards. Second, a table correlating seventeen IMS elements with technical and academic literature, and guidance documents of the studied company, making it easier to visualize interdependencies. Limitations of the investigation: The main one is the generalization limit of the conclusions, models or theories developed from a single case study, used in this work. These limitations do not reduce the importance of the results found, since the proposed action plan enabled the natural gas transportation company to obtain the desired triple certification. Practical implications: the proposed action plan may support other companies to achieve triple certification. Originality / Value: no research has been identified in the international literature that deals with triple certification for companies operating in the natural gas transportation market, specifically, the correlation of IMS elements with literature references and guidance documents of the studied company.

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.