Real Natural Gas Research Articles

A methodology to determine target gas supply reliability of natural gas pipeline system based on cost-benefit analysis

In this study, a methodology to determine the target gas supply reliability of the natural gas pipeline system based on cost-benefit analysis is proposed. In the methodology, an optimization model is developed firstly, and the objective function is constructed to maximize the difference between the benefits and investment when improving system reliability. Secondly, the investment and the benefits of reduced unreliability cost due to the implementation of the reliability improvement measures are considered in the optimization model. Thirdly, the virtual age model is employed to quantify the impact of reliability improvement measures on the unit's reliability, and the gas supply reliability evaluation method of the natural gas pipeline system is developed. Finally, the optimization model used to determine the target gas supply reliability is solved by a heuristic genetic algorithm. Furthermore, a real natural gas pipeline system located in China is applied to confirm the feasibility of the methodology, and the target gas supply reliability is determined, with a value of 0.998608. At last, the effect of system operational age on the target gas supply reliability is investigated through sensitivity analysis. The results show that the target gas supply reliability decreases with the increase of the operational age, which is consistent with the engineering practice.

The premixed flame structure and combustion mechanism of clean hydrogen mixed with multi-component natural gas

Hydrogen is widely taken for the most potential clean energy for the moment. Currently, pipeline transport of natural gas mixed with hydrogen is one of the primary approaches of hydrogen transportation. However, compared to pure natural gas (CH4), the explosion and combustion of natural gas mixed with hydrogen may lead to more severe consequences, and its reliability needs to be considered. Existing research of combustion and explosion using methane instead of natural gas have yet to be considered for application to real multi-component natural gas. To address this issue, real multi-component natural gas is configured, and the premixed flame and combustion characterization parameters of natural gas/H2/air are obtained through experiment. The chemical reaction kinetics of natural gas/H2/air mixtures are analyzed using Chemkin Pro software. These results prove that the laminar burning (LB) velocity varies between 0.33 m/s and 2.34 m/s by adding H2. The primitive reactions R1 (H2 + OH → H + H2O) and R3 (H + O2 → O + OH) predominantly contribute to enhancing the LB velocity. The Markstein lengths rang from −0.19 mm to −0.049 mm, most lewis numbers are less than 1, the flame thicknesses and critical destabilization radii are reduced. Additionally, hydrodynamic instability and thermal-diffusion instability are strengthened by adding the hydrogen. As for smaller hydrogen content, the average size of flame cell from about 8 mm program first rises and then falls cyclic fluctuations, fluctuation amplitude gradually reduces, fluctuation frequency accelerates. In larger hydrogen content, this fluctuation amplitude is reduced and the average size of flame cell decreases. In addition, H2 enhances the explosive strength of natural gas. Finally, methane instead of natural gas may exaggerate the velocity of laminar burning and exaggerate or weaken the ignition time delay of natural gas with different hydrogen contents. The current research might provide crucial information for the safe transport of hydrogen mixed with natural gas.

Multi-objective optimization of a hydrogen production process for polymer membrane coupled with electrochemical hydrogen pump from hydrogen-doped natural gas by NSGA-II

In this work, a novel process of polymer membrane coupled with electrochemical hydrogen pump (EHP) was proposed to achieve efficient hydrogen production from hydrogen-doped natural gas (HDNG), and was simulated by UniSim Design. A hydrogen separation unit of two-stage polymer membrane was used for achieving hydrogen enrichment from low H2 concentration HDNG. Due to the hydrogen upgrade, EHP was operated at a H2 concentration much higher than HDNG, resulting in low energy consumption. Benefiting from the synergy between hydrogen upgrade and purification, the coupling process brought substantial effect while improving the hydrogen purity and recovery rate and reducing the hydrogen separation cost. The real natural gas components were considered into HDNG to ensure the effectiveness of process simulation. An optimization strategy considering both product yield and production cost was proposed to achieve the trade-off between hydrogen recovery rate and separation cost. However, the impact of process parameters on the objectives is nonlinear and difficult to describe with mathematical expressions. Therefore, an agent model of the proposed process was established based on back propagation neural network (BPNN), and multi-objective optimization of H2 recovery rate and total annual cost (TAC) of hydrogen separation unit was achieved using non-dominated sorting genetic algorithm-II (NSGA-II). Investigations with HDNG under various operating conditions (feed pressure of 8–40 bar and feed H2 concentration of 5–25 mol%) were performed, which proved that 90 % overall H2 recovery rate and 99.99 mol% H2 purity of hydrogen product can be achieved at low cost, with the lowest hydrogen separation cost of 1.00 $/kg H2 achievable at 40 bar feed pressure and 25 mol% feed H2 concentration. In summary, this coupling process can provide engineering solutions for efficient hydrogen production from HDNG and promote the development of green hydrogen.

An approach to coupling the gas flow dynamics and thermodynamics of hydrogen solubility in L485ME steel grade for estimation of fracture toughness

The paper presents the problem of coupling the gas flow dynamics in pipelines with the thermodynamics of hydrogen solubility in steel for the estimation of the fracture toughness. In particular, the influence of hydrogen blended natural gas transmission on hydrogen solubility and, consequently, on fracture toughness is investigated with a focus on the L485ME low-alloy steel grade. Hydraulic simulations are conducted to obtain the pressure and temperature conditions in the pipeline. The hydrogen content is calculated from Sievert’s law and, as a consequence, the fracture toughness of the base metal and heat-affected zone is estimated. Experimental data is used to define hydrogen-assisted crack size propagation in steel as well as to a plane strain fracture toughness. The simulations are conducted for a real natural gas transmission system and compared against the threshold stress intensity factor. The results showed that the computed fracture toughness for the heat-affected zone significantly decreases for all natural gas and hydrogen blends. The applied methodology allows for identification of the hydrogen-induced embrittlement susceptibility of pipelines constructed from thermomechanically rolled tubes worldwide most commonly used for gas transmission networks in the last few decades.

Use of near-infrared spectroscopy for the online monitoring of natural gas composition (hydrocarbons, water and CO2 content) at high pressure

The natural gas production from Brazilian pre-salt fields imposed new challenges for petrochemical industry. Actual treatment facilities are not adequate for this new scenario and studies have been conducted to apply new adsorbents materials and membranes for water and CO2 removal from natural gas at high pressures. To better develop such investigations, sensors for on-line monitoring of natural gas properties like CO2 and water content are important, since their presence affects the quality of the final product. Near infrared (NIR) spectroscopy associated to chemometric models (partial least squares) were employed for on-line monitoring of representative natural gas systems (methane/CO2, methane/water and methane/CO2/water) and a real natural gas at temperature and pressure ranges from 20 to 60 °C and 10 to 200 bar, respectively, water content up to gas saturation and CO2 content up to 50 wt%. Water solubility values used as reference for NIR Spectrometer calibration in model systems were taken from the literature and for real natural gas calculated with Cubic Plus Association (CPA) Equation of State (EoS), while CO2 content was experimentally controlled aiming to calibrate the chemometric models in the full range of pressure and temperature. Several strategies were adopted for the chemometric model’s development to obtain the best correlation between NIR spectra and experimental data. Results indicate good correlation in both calibration and validation steps attaining linear correlation coefficients (R2) higher than 0.96 for all systems investigated. The proposed methodology is a potential tool for on-line monitoring of natural gas composition, including CO2 and water content, at high-pressures and can be applied at petrochemical industries or in laboratories, dispensing sampling or any sample preparation.

A CFD model of natural gas steam reforming in a catalytic membrane reactor: Effect of various operating parameters on the performance of CMR

The use of hydrogen as an energy carrier has increased for reasons related to its environmentally friendly characteristics. The most common and low-cost method of producing hydrogen from natural gas (or CH4) is the steam reforming process. The 2-D axisymmetric catalytic membrane reactor (CMR) model is developed for pure hydrogen production from real natural gas (NG) using a Pd–Ru membrane with a Ni/Al2O3 catalyst. In this paper, a CFD model is prepared to investigate the performance of a catalytic membrane reactor through natural gas conversion and hydrogen production under various operation parameters, i.e., temperature of reaction in the range of 600, 700, 800, 900, and 1000 K, gas hourly space velocity GHSV with a range of 500, 700, 1000, 2000, and 3000 h−1, and sweep gas flow rate (Re No.) 1, 50, 100, 150, and 250. The results of the simulation show that the CMR exhibits a high permeation rate of hydrogen on the permeate side (tube side) and achieves a high methane conversion rate of approximately 99.9 % at 1000 K on the retentate side. The hydrogen flux exhibits a decline as the temperature increases from 900 to 1000 K, despite the achievement of complete (NG) conversion. Hence, it can be concluded that the performance of the catalytic membrane reactor (CMR) process exhibits a pattern of increase when the other parameters, such as (GHSV) and (Re.No.), are increased.

High-pressure ignition behaviors of methane/ethane/propane-n-heptane mixtures representing natural gas-diesel dual fuel

Considering that natural gas is the key transition fuel towards the carbon-neutral future, the objective of the present study is to gain insight into evolution features of natural gas-diesel dual fuel during ignition process. Firstly, new experimental data of ignition delay times for the stoichiometric methane/ethane/propane-n-heptane mixtures, which is of significance for validating and optimizing chemical kinetic models of the dual fuel at engine-relevant conditions, were acquired through a shock-tube facility at pressure of 40 atmopshere within temperature range of 1200-1600 K, and quantitative influences of components of the fuel mixtures on ignition were determined. Then importance of species including typical radicals and alkenes during ignition processes were identified. Besides, stage characteristics of the fuel mixtures during ignition processes were analyzed. The result shows that the ignition of real natural gas which contains some ethane and propane is greatly different from that of methane. It can be seen that the C2 substances are significant to control ignition of the mixtures. For methane-n-heptane and methane/ethane-n-heptane mixtures, the whole ignition process can be divided into decomposition and oxidation stages. While for the fuel mixtures containing propane and n-heptane, it seems to be more reasonable to divide the whole ignition process into three-stages, i.e., decomposition, mixed and oxidation stages.

Energy price shocks and current account balances: Evidence from emerging market and developing economies

This paper investigates the effects of real energy price shocks on current account balances of 45 emerging market and developing economies. The investigation is based on country-specific structural vector autoregression models, where alternative specifications and identification schemes are considered for robustness purposes. The empirical results suggest that 1 % of a positive real oil price shock results in up to 0.11 (0.08) percentage points of a cumulative improvement (deterioration) in current account balances of oil exporters (importers) after five years, whereas 1 % of a positive real natural gas price shock results in up to 0.06 (0.04) percentage points of a cumulative improvement (deterioration) in current account balances of natural gas exporters (importers) after five years. Real coal price shocks result in higher current account balances of oil exporters and natural gas exporters, suggesting substitution of coal with oil and natural gas in such cases. When contributions of alternative real energy prices to the variance of current account balances are compared, real oil price shocks dominate those of real natural gas and real coal prices. The empirical results investigating the effects of oil demand versus oil supply shocks on current account balances suggest that oil demand shocks (rather than oil supply shocks) result in similar reactions of current account balances to real oil price shocks, supporting the view that the effects of oil demand shocks are different from those of oil supply shocks. The results are robust to the consideration of country-specific changes in real GDP and real effective exchange rates.

A methodology to determine the target reliability of natural gas pipeline systems based on risk acceptance criteria of pipelines

The reliable operation of the natural gas pipeline system is directly related to natural gas supply security, and it is vital to determine the target reliability of the natural gas pipeline system when assessing whether the system is reliable. In this study, a methodology to determine the target reliability of natural gas pipeline system based on the risk acceptance criteria of pipelines is proposed, and the methodology consists of three parts. Firstly, the risk acceptance criteria of the natural gas pipelines are determined based on the existing standards. Secondly, the failure consequence model of natural gas pipelines considering the pipeline properties and surroundings is developed, and the corresponding allowable pipeline failure probabilities are calculated according to the risk theory. Finally, the evaluation model of the gas supply reliability for natural gas pipeline systems considering the unit’s failure and hydraulic characteristics is developed, and the target reliability of natural gas pipeline systems is calculated by employing the allowable pipeline failure probabilities. Furthermore, a real natural gas pipeline system is employed to determine its target reliability, and the target value is compared with the actual value of the gas supply reliability. Based on the compared results, the corresponding measure to improve the gas supply reliability is proposed.

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

Despite the availability of pipeline bending strain detection technologies based on inertial measurement unit, there is a lack of intelligent and efficient methods for accurately identifying pipeline features by bending strain. Therefore, this paper proposes a novel method for identifying features in natural gas pipelines based on shapelet and blending fusion model. Specifically, the shape features of the bending strain data are extracted and transformed by shapelet. Then a blending fusion model with SVM, Decision Tree and Gradient Boosting as base learners and Random Forest as meta-learner is constructed. Finally, the extracted features are fed into the blending fusion model for pipeline feature recognition. The model is trained with bending strain data obtained from a real natural gas pipeline, the results indicate that the recognition accuracy of the proposed method is 97.17%. Compared with other models, the superiority of the proposed model is verified, and it is proved that the proposed method has better accuracy than the existing models (over 1.3%). Overall, the method proposed in this paper can be effectively combined with the in-line inspection system to provide a reference for pipeline companies to carry out pipeline integrity management.

ASSESSMENT OF METHODS FOR DETERMINING THE NATURAL GAS HEAT OF COMBUSTION

The shortage of fuel and energy resources, the increase in natural gas prices and the continuous increase in gas consumption indicate the relevance of the issues of measuring the natural gas amount and assessing its quality indicators. Ukraine is implementing the European methodology for calculating gas consumption based on its quality and efficiency. The problem of determining the gas quality in Ukraine requires a thorough assessment and research.
 The main physical and chemical indicator of the natural gas quality, which characterizes its energy value, is the specific volumetric gas heat of combustion. The value of this parameter differs for different gas fields. The gas heat of combustion determines the efficiency of its use both in everyday life and in industry.
 The aim of the work is to analyze the main methods for determining the calorific value of natural gas.
 The gas heat of combustion is determined by calorimetric, chromatographic and correlation methods.
 The calorimetric method is characterized by the complexity of the measurement process, complete combustion of the test gas, and long term process. This ensures the objectivity of the results, which is based on taking into account the moisture content of the gas and the presence of non-combustible components in it.
 The basis of the chromatographic method is the separation of the components of a gas sample in a chromatographic column. The result is a chromatogram that shows the percentage composition of hydrocarbons in the gas. The characteristic properties of the method are the identification of a wide range of components and the repeatability of the results.
 When using correlation methods for determining the heating value of natural gas, various physicochemical parameters of samples are measured (such as total hydrocarbon concentration, heat capacity, acoustic wave propagation velocity, carbon dioxide concentration, thermal conductivity, dielectric constant, dynamic viscosity, and others).
 Correlation dependence is based on the choice and association of such parameters. This dependence is studied on different test gas mixtures and allows us to determine the empirical dependence. The mixtures correspond to gases with different calorific values. The obtained empirical dependence is used to register the heat of combustion of real natural gas. The paper evaluates the features of these methods application and instruments based on them.

Data-driven digital twin method for leak detection in natural gas pipelines

Leak detection in natural gas pipelines is an extremely important and persistent problem in the oil and gas industry. The construction of accurate physical models of pipelines is limited by the high complexity, unavailable closed-form solution, and strict experienced personnel requirements. Moreover, industrial automation has the common problems of large amount of data with little information. This paper proposes a data-driven digital twin (DT) method for leak detection as a new paradigm solution to these challenges. From the perspective of knowledge-based data-driven, a DT pipeline learning and updating scheme based on normal data directly from operational data during the entity pipelines life-cycle is proposed to enhance DT adaptability. A DT-driven leak detection method is proposed, making effective use of data interaction and fusion of DT. The effectiveness and performance of the proposed approach is illustrated by deploying the DT pipeline in a simulated leak scenario of a real running natural gas pipeline. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd

A methodology to evaluate gas supply reliability of natural gas pipeline network considering the effects of natural gas resources

In this study, a methodology to evaluate gas supply reliability of the natural gas pipeline network is proposed, and the uncertainty of the gas supply capacity of the natural gas resources is considered. The methodology is composed of four parts. Firstly, the gas supply capacity calculation model of natural gas resources considering its stochastic and time-dependent characteristics is described. Secondly, the gas demand prediction model is developed based on the demand-side analysis. Thirdly, the gas supply calculation model of natural gas pipeline network is proposed considering units failure and hydraulic characteristics. Finally, according to the established gas supply reliability index system, the gas supply reliability indicators are calculated based on N (N is a large number) times of Monte Carlo simulation. Furthermore, a real natural gas pipeline network located in China is applied to confirm the feasibility of the methodology, and the gas supply reliability is evaluated based on quantity and time dimensions, and the results are reduced by about 3.9% and 4.1% compared with the results calculated by the method which ignored the uncertainty of resources. At last, the significance of the natural gas resources in the gas supply reliability is investigated by the sensitivity analysis.

Leak Detection in Natural Gas Pipelines Based on Unsupervised Reconstruction of Healthy Flow Data

Summary Timely detection of leak accidents plays an essential role in the safe operation and risk assessment of natural gas pipelines. However, the scarce leak data and complex operating conditions lead to small samples, data imbalance, and problems with confusing operating conditions. The reliance on leak data limits the recognition performance of the artificial intelligence classification method for leakage operating conditions. A leak detection method based on the unsupervised reconstruction of healthy flow data is established to address these problems. First, an unsupervised neural network is established to reconstruct healthy flow data from real natural gas pipelines. And a model update strategy based on active learning is designed to improve the model’s adaptability for time-varying pipelines. Next, a dynamic alarm threshold strategy that accounts for the knowledge of the experience and statistical characteristics of the data segments is suggested to prevent false alarms caused by ambiguous operating conditions. Finally, unlike most recent work that only considers simulated data or laboratory data, this paper conducts a leak case study on an actual natural gas pipeline in service to improve the robustness of the proposed method in the actual operating environment. The findings of this paper can be used as a reference to analyze pipeline behavior analysis based on pipeline flow trend characteristics and early alarm management.

Determination of Natural Gas Consumption and Carbon Emission in Natural Gas Supplying Countries in Asia Pacific

This study investigates determination of natural gas consumption and carbon emission in the middle income group of natural gas supplying countries in Asia Pacific, including China, Indonesia, Malaysia, Myanmar and Vietnam during the period 2000-2021 by applying the simultaneous equation analysis. The results of this study are that carbon emission increase due to the real exchange rate, population and natural gas production, but carbon emission will reduce natural gas consumption. Furthermore, natural gas consumption increases due to natural gas production, GDP per capita and energy intensity. This study recommends the government to develop clean and efficient alternative energy to reduce carbon emission in response to natural gas demand for domestic and foreign market needs.

Shock tube study of natural gas oxidation at propulsion relevant conditions

Global climate change and rising crude oil prices have motivated search of clean, sustainable and economic fuel. Methane, having the highest hydrogen to carbon ratio is ideally the best option towards carbon neutral emissions and reduce greenhouse gas emissions. However, there the economic benefits are minimal when using pure methane because of purification costs. Natural gas primarily consists of methane but with appreciable amounts of C2C4 alkanes which significantly impact oxidation performance, especially when compared to pure methane at high pressures and temperatures, studies of natural gas have been scarce and there is new interest in studying natural gas for its application to propulsion devices. This single pulse shock tube study investigates the effect of natural gas composition on species yields as a function of temperature at various stoichiometric conditions (φ ≈ 0.5, 1.0, 2.0) and high pressure (240 atm) over a nominal reaction time of 2.5 ms. The experimentally measured speciation results for one real natural gas sample from Boise, ID (USA) and a reference natural gas sample are compared to species yield predictions using well established chemical kinetic mechanisms, to check the feasibility of using these mechanisms in the design of propulsion devices. The chemical kinetic simulations are carried out using the constant pressure approach as well as the changing pressure approach which considers the exact measured pressure change in the shock tube. The experiments provide a large speciation data set for optimizing chemical kinetic mechanisms for natural gas applications and to understand the effect of composition on natural gas chemical kinetics. The predictions vary significantly between models tested and effect of composition variation was evident. Aramco Mech 3.0 could predict the experiments almost perfectly, especially at fuel rich conditions.

Leakage detection in a buried gas pipeline based on distributed optical fiber time-domain acoustic wave signal

In long-term operation, buried pipelines are prone to corrosion, deformation, and fracture, resulting in leakage. It will cause serious environmental pollution and economic loss. Distributed temperature sensing (DTS) and location method based on temperature signals have been gradually applied to the pipe network. However, the DTS is difficult to comprehensively monitor pipeline leakage and has blind monitoring areas. In addition, fiber optics are difficult to capture continuous low-temperature changes because of the heat dissipation of soil porous media. Thus, distributed acoustic sensing (DAS) is needed to assist the comprehensive monitoring. The acquisition of acoustic signals caused by the leakage field is of great significance to the study of pipeline leakage monitoring methods. In this paper, the attenuation model of leakage acoustic amplitude is established, and the law of vibration acoustic signal in porous soil is studied. Based on the real natural gas leakage test, the characteristic parameters of the time-domain acoustic wave signal are studied. It is proved that the vibration acoustic signal is a kind of acoustic wave caused by the transient mass loss at the beginning of leakage. The main conclusions are as follows: the time-domain signal amplitude is large and the vibration is obvious when pipeline leakage occurs. Acoustic wave leakage occurs in a very short time and acoustic vibration amplitude reaches its peak. Subsequently, the pipeline is in a stable leakage state, and the waveform continues to expand. When there is no natural gas leakage, the vibration signal intensity is less than 3000, and the amplitude changes little. The difference is that when the pipeline leaks, the vibration test curve signal has obvious bulge and fluctuation, and the vibration signal intensity is basically above 4000. The amplitude of the vibration signal increases with the increase of the leakage hole. The research results provide a theoretical basis for the field application of distributed optical fiber acoustic detection.

A semi-supervised leakage detection method driven by multivariate time series for natural gas gathering pipeline

Real-time and accurate leakage detection of natural gas gathering pipelines is critical to the safe and reliable operation of the gas and oil industry. Modern data-driven fault detection and diagnosis techniques have recently gained increasing attention because model-based techniques are practically prohibitive. However, most existing data-driven leakage detection approaches are obtained through supervised learning, which requires a substantial set of labelled data. Especially in real-world scenarios, leakage samples are rare. To reduce the dependence of the leakage detection method on leakage data and make full use of numerous normal datasets generated under normal working conditions, we propose a semi-supervised leakage detection method which consists of two components: an improved long short term memory autoencoder (LSTM-AE) network and one-class support vector machine (OCSVM). The LSTM-AE is first trained to learn the intrinsic features of a normal dataset of pipeline parameters which are multivariate time series. The OCSVM is then applied to calculate the score which is used to infer leak existence. The performance of the proposed method is evaluated on the real natural gas gathering pipelines, and the results confirm that the proposed method achieved 98% accuracy and 99% AUC (Area Under Curve) in a real-life dataset.

RSM Modeling and Optimization of CO2 Separation from High CO2 Feed Concentration over Functionalized Membrane.

The challenges in developing high CO2 gas fields are governed by several factors such as reservoir condition, feed gas composition, operational pressure and temperature, and selection of appropriate technologies for bulk CO2 separation. Thus, in this work, we report an optimization study on the separation of CO2 from CH4 at high CO2 feed concentration over a functionalized mixed matrix membrane using a statistical tool, response surface methodology (RSM) statistical coupled with central composite design (CCD). The functionalized mixed matrix membrane containing NH2-MIL-125 (Ti) and 6FDA-durene, fabricated in our previous study, was used to perform the separation performance under three operational parameters, namely, feed pressure, temperature, and CO2 feed concentration, ranging from 3.5–12.5 bar, 30.0–50.0 °C and 15–70 mol%, respectively. The CO2 permeability and CO2/CH4 separation factor obtained from the experimental work were varied from 293.2–794.4 Barrer and 5.3–13.0, respectively. In addition, the optimum operational parameters were found at a feed pressure of 12.5 bar, a temperature of 34.7 °C, and a CO2 feed concentration of 70 mol%, which yielded the highest CO2 permeability of 609.3 Barrer and a CO2/CH4 separation factor of 11.6. The average errors between the experimental data and data predicted by the model for CO2 permeability and CO2/CH4 separation factor were 5.1% and 3.3%, respectively, confirming the validity of the proposed model. Overall, the findings of this work provide insights into the future utilization of NH2-MIL-125 (Ti)/6FDA-based mixed matrix membranes in real natural gas purification applications.

Recent Advances of Polymeric Membranes in Tackling Plasticization and Aging for Practical Industrial CO2/CH4 Applications-A Review.

Membranes are a promising technology for bulk CO2 separation from natural gas mixtures due to their numerous advantages. Despite the numerous fundamental studies on creating better quality membrane efficiency, scaling up the research work for field testing requires huge efforts. The challenge is to ensure the stability of the membrane throughout the operation while maintaining its high performance. This review addresses the key challenges in the application of polymeric technology for CO2 separation, focusing on plasticization and aging. A brief introduction to the properties and limitations of the current commercial polymeric membrane is first deliberated. The effect of each plasticizer component in natural gas towards membrane performance and the relationship between operating conditions and the membrane efficiency are discussed in this review. The recent technological advancements and techniques to overcome the plasticization and aging issues covering polymer modification, high free-volume polymers, polymer blending and facilitated transport membranes (FTMs) have been highlighted. We also give our perspectives on a few main features of research related to polymeric membranes and the way forwards. Upcoming research must emphasize mixed gas with CO2 including minor condensable contaminants as per real natural gas, to determine the competitive sorption effect on CO2 permeability and membrane selectivity. The effects of pore blocking, plasticization and aging should be given particular attention to cater for large-scale applications.