Natural Gas Gathering Pipeline Research Articles

Numerical simulation of sulfur particle agglomeration at bends of high sulfur natural gas gathering pipelines based on Euler–PBM coupling

Sulfur deposition can result in an increase in the wall thickness of high-sulfur natural gas gathering pipelines, leading to issues like unstable pipeline flow. It is crucial to reveal the aggregation of sulfur particles at key locations of high-sulfur natural gas gathering pipelines to predict the location and amount of sulfur deposition in the pipelines. In this paper, the Euler–PBM (Population balance model) coupling is used to establish a numerical simulation model of gas–solid two-phase pipe flow accompanied by sulfur particle agglomeration in the pipe bends, focusing on the influence of sulfur particle volume fraction, pipe inclination angle and inlet flow velocity on sulfur particles agglomeration behavior. The results show that the sulfur particles have a significant agglomeration effect at the bend of the collecting pipeline, and the agglomeration growth occurs to different degrees throughout the bend, and the main area of sulfur particles agglomeration is near the top wall of the pipeline, followed by other areas near the wall of the pipeline. When the inlet volume fraction of sulfur particles was increased from 0.05 to 0.25%, and the inclination angle of the pipe was increased from 30° to 90°, the distribution range of sulfur particle size after agglomeration became wider, and the maximum size of sulfur particles was 187.56 μm, and the effect of sulfur particle agglomeration was enhanced; the inlet flow rate was increased from 3.0 to 9.0 m/s, and the reduction range of sulfur particle size after agglomeration was 5.68–9.87 μm. The maximum particle size of sulfur particles also decreased, and the effect of sulfur particle agglomeration was weakened.

A novel optimization model for tackling capacity challenges in natural gas gathering systems

In the layout optimization of natural gas gathering pipeline network, a lot of work has been done, but few scholars focus on the capacity optimization of gas gathering station (short for station). The structural relationship parameters of dendritic pipeline network and the flow parameters of pipeline network are complicated, which makes it difficult to establish the model, and at the same time, the existence of nonlinear term causes the difficulty of solving the problem. In the current two stage star-dendritic natural gas gathering pipeline network, pipeline sizes and station capacity are not well taken into account for the optimization of pipeline network layout at the same time. In this paper, a consideration station capacity and pipeline sizes: Two-stage star-dendritic network layout optimization model (SCPSTSDLO-Model) is constructed by integrating the factors of site discrete characteristics, station capacity and pipeline sizes, and considering the hydraulic pressure drop conditions of the network, and a solution framework based on the nonlinear processing is proposed, which can obtain the optimization results of optimal affiliation, station type, station site and pipeline sizes at the same time. Five cases are used to verify the validity and accuracy of the model and algorithm, compare the sensitivity analysis and solution algorithm, compare the hydraulic pressure drop conditions, and analyze the application in different forms of pipeline networks. The results show that the SCPSTSDLO-Model and the solution method established in this paper can obtain the optimal solutions under different topologies according to the application requirements, taking into account the capacity of stations and pipeline sizes.

Investigation into flow-induced internal corrosion direct assessment in small-diameter dry gas fluctuating pipeline

Small-diameter undulating natural gas pipelines with low gas transmission capacity do not have internal inspection conditions to grasp the internal corrosion status of the pipeline. This study focuses on a small-diameter undulating dry natural gas gathering and transportation pipeline, the internal corrosion mechanism of the pipeline is studied through corrosion products, and the specific location of the internal corrosion sensitive pipe segment is determined based on multiphase flow simulation and PMDT(pipeline non-contact magnetic anomaly detection); the excavation inspection is ultimately used to evaluate the internal status of the pipeline. The results demonstrate that the internal corrosion mechanism of ZL (ZI Lai) pipeline is O2 and CO2 synergistic corrosion in which O2 plays a dominant role, while the increasing flow rate will aggravate corrosion; The maximum general corrosion rate and critical inclination along the pipeline are 0.0218 mm/a and 18.4°, respectively. Furthermore, twelve corrosion sensitive segments have been identified, including two II-level and six III-level magnetic anomaly segments. The internal corrosion defects are localized at the 3–9o’clock position of the pipeline, with a maximum general corrosion rate not exceeding 0.0228 mm/a. Furthermore, the pipeline failure pressure of 12.27 MPa surpasses the maximum allowable working pressure of 8.84 MPa, ensuring its continued operational safety. The corrosion protection measures of reducing the water content of natural gas, adding vapor corrosion inhibitor and regularly detecting magnetic anomaly pipeline are developed.

Leak Detection for Natural Gas Gathering Pipelines Under Multiple Operating Conditions Using RP-1dConvLSTM-AE and Multimodel Decision

Leak Detection for Natural Gas Gathering Pipelines Under Multiple Operating Conditions Using RP-1dConvLSTM-AE and Multimodel Decision

A self-supervised leak detection method for natural gas gathering pipelines considering unlabeled multi-class non-leak data

Detecting leaks in natural gas gathering pipelines is paramount for the safe and reliable operation of the gas and oil industry. Due to the lack of leak data and the changes in leak features, semi-supervised leak detection methods that use normal data for health model learning have attracted much attention. However, these approaches usually consider one-class normal samples as health data, which may fail to fit the reality of unlabeled multi-class non-leak data under variable operating conditions. In addition, existing semi-supervised methods often suffer from insufficient representation learning as they employ step-by-step training or rely on the low-level reconstruction of autoencoders. To address the above two key challenges, this paper proposes a novel end-to-end self-supervised leak detection method, self-supervised multi-sphere support vector data description. Specifically, it utilizes the presented multi-sphere support vector data description to model unlabeled multi-class non-leak data and the introduced self-supervised learning strategy to boost the representation learning of the end-to-end semi-supervised model. Moreover, the categories of unlabeled multi-class non-leak data are learned in an unsupervised way through alternating feature clustering and pseudo-label-based classification. A robust leak score calculation method is also designed to improve the performance of the proposed method. Finally, the experimental results on the field data collected from pipelines demonstrate the effectiveness of the proposed method.

Analysis of sensitivity to hydrate blockage risk in natural gas gathering pipeline

During the operational process of natural gas gathering and transmission pipelines, the formation of hydrates is highly probable, leading to uncontrolled movement and aggregation of hydrates. The continuous migration and accumulation of hydrates further contribute to the obstruction of natural gas pipelines, resulting in production reduction, shutdowns, and pressure build-ups. Consequently, a cascade of risks is prone to occur. To address this issue, this study focuses on the operational process of natural gas gathering and transmission pipelines, where a comprehensive framework is established. This framework includes theoretical models for pipeline temperature distribution, pipeline pressure distribution, multiphase flow within the pipeline, hydrate blockage, and numerical solution methods. By analyzing the influence of inlet temperature, inlet pressure, and terminal pressure on hydrate formation within the pipeline, the sensitivity patterns of hydrate blockage risks are derived. The research indicates that reducing inlet pressure and terminal pressure could lead to a decreased maximum hydrate formation rate, potentially mitigating pipeline blockage during natural gas transportation. Furthermore, an increase in inlet temperature and terminal pressure, and a decrease in inlet pressure, results in a displacement of the most probable location for hydrate blockage towards the terminal station. However, it is crucial to note that operating under low-pressure conditions significantly elevates energy consumption within the gathering system, contradicting the operational goal of energy efficiency and reduction of energy consumption. Consequently, for high-pressure gathering pipelines, measures such as raising the inlet temperature or employing inhibitors, electrical heat tracing, and thermal insulation should be adopted to prevent hydrate formation during natural gas transportation. Moreover, considering abnormal conditions such as gas well production and pipeline network shutdowns, which could potentially trigger hydrate formation, the installation of methanol injection connectors remains necessary to ensure production safety.

An unsupervised leak detection method with aggregating prediction and reconstruction along projection pathway for natural gas gathering pipelines

Early detection of leaks in natural gas gathering pipelines makes proactive maintenance and corrective measures take place more effective, thus enhancing the safe and reliable operation of pipelines and mitigating serious threats to the environment and human life. The data-driven leak detection methods have become a natural choice owing to the extensive deployment of sensors in the pipeline network. However, the supervised and semi-supervised leak detection methods need to utilize a certain amount of leak data to train reliable classifiers, and labeled data usually difficult to obtain in real-world applications especially for the gas industry. Besides, most of data-driven methods today only consider the long-term or short-term patterns hidden in the multivariate time series rather than both, which may reduce their effectiveness since temporal data generated in the real-world applications often relates to a mixture of these two patterns. To settle this challenge, we propose a novel unsupervised leak detection method by utilizing a one-dimensional convolutional autoencoder (1D-CAE) to learn short-term dependency patterns from local time series and a long short-term memory (LSTM) to discover evolving trends about long-term patterns from entire temporal data. The proposed method not only calculates the difference between actual samples and their predictions but also compares the inputs and their reconstructions, where comparisons of reconstruction are further extended from input space to hidden spaces. The obtained deviations are then integrated through the developed integration strategy and employed to calculate their global leak scores. Specially, considering that the magnitudes of deviations integrated together are diverse or there may exist correlation among these deviations caused by correlated neurons across layers, the minimum covariance determinant (MCD) method is employed to scale deviations and eliminate correlations along the projection pathway. The effectiveness of the proposed method is verified on real datasets of natural gas gathering pipeline. In addition, analysis of experimental results demonstrates that the proposed method has good accuracy, and is conducive to operating in the context of uncontrolled long-term monitoring.

Primary Growth Behavior of Sulfur Particles through the Throttle Valve in the Transmission System of High Sulfur Content Natural Gas

The deposition of sulfur particles in gathering and transportation pipeline system can cause serious safety problems and economic losses. When the high sulfur content natural gas (HSCNG) flows through the throttle valve of the gathering and transportation system, it will cause the supersaturation of elemental sulfur in the gas phase, and then the sulfur crystal nuclei and sulfur particles will appear in the pipeline system. Studying the initial growth behavior of sulfur crystal nuclei and sulfur particles can lay a necessary prerequisite for the accurate prediction of sulfur particle deposition in high sulfur content natural gas gathering and transportation (HSCNGGT) pipelines. Based on the homogeneous nucleation theory in crystallization kinetics, a mathematical model of elemental sulfur nucleation was established. Taking the throttling condition in the process of HSCNGGT as an example, the effects of temperature, pressure and H2S concentration in HSCNG on the critical nucleation radius and nucleation rate of elemental sulfur were explored. The results show that: (1) after the supersaturation of elemental sulfur, sulfur crystal nuclei with nanoscale radius will be precipitated. The temperature and pressure after throttling have great influence on the nucleation radius, and the influence of H2S concentration on the nucleation radius is more complex. (2) The temperature, pressure and H2S concentration after throttling also have great influence on the nucleation rate. By solving the growth kinetics model of sulfur particles based on Brownian condensation, it is found that the nano-sized sulfur crystal nuclei can grow into micron-sized sulfur particles in a very short time.

Experiment and numerical simulation of distribution law of water-based corrosion inhibitor in natural gas gathering and transportation pipeline

The transmission medium of natural gas gathering and transportation pipelines usually contains corrosive gases, which will cause serious corrosion on the inner wall of the pipelines when they coexist with water. Therefore, it is necessary to add corrosion inhibitor to form a protective film to protect the pipeline. The distribution of corrosion inhibitors in a gathering and transportation pipeline in Moxi gas field was studied by combining experiment and simulation. The Pearson function was used to calculate the experimental and simulation results, and the correlation was more than 80%, indicating a high degree of agreement. The simulation results show that: ① The larger the pipe angle, filling speed and gas flow rate, the smaller the particle size, the better the distribution of corrosion inhibitor particles in the pipe. The filling amount will affect the concentration, but the distribution trend is unchanged; ② A method to determine the filling mode based on the loss was proposed, and for this pipeline, the loss of corrosion inhibitor was determined to be 5.31 × 10−3 kg/s, and the filling amount was recommended to be adjusted to 20 L/h, which has certain guiding significance for the actual filling strategy of pipeline corrosion inhibitor.

Automatic detection of internal corrosion defect in a natural gas gathering pipeline using improved YOLOv5 model

The detection of internal corrosion of the very long gas pipeline is a fundamental task for the prevention of possible failures, also one of the major challenges facing gas companies. Pipeline endoscopy based on the corrosion inspection method provides direct visual observation of defects, but the post-video analysis is time-consuming and not practical. In this work, we propose an improved YOLOv5 model-based software approach to automatically detect inner corrosion defects in nature gas-gathering pipelines. Video streaming of pipe interiors is provided by an endoscope robot. Sample augmentation strategies such as affine transformation and defect segmentation with background fusion are used to generate defect images and expand the data set. The region-based recursive localization method can be effectively optimized to improve the localization of corrosion regions.

Methane Emissions from Natural Gas Gathering Pipelines in the Permian Basin.

The rapid reduction of methane emissions, especially from oil and gas (O&G) operations, is a critical part of slowing global warming. However, few studies have attempted to specifically characterize emissions from natural gas gathering pipelines, which tend to be more difficult to monitor on the ground than other forms of O&G infrastructure. In this study, we use methane emission measurements collected from four recent aerial campaigns in the Permian Basin, the most prolific O&G basin in the United States, to estimate a methane emission factor for gathering lines. From each campaign, we calculate an emission factor between 2.7 (+1.9/-1.8, 95% confidence interval) and 10.0 (+6.4/-6.2) Mg of CH4 year-1 km-1, 14-52 times higher than the U.S. Environmental Protection Agency's national estimate for gathering lines and 4-13 times higher than the highest estimate derived from a published ground-based survey of gathering lines. Using Monte Carlo techniques, we demonstrate that aerial data collection allows for a greater sample size than ground-based data collection and therefore more comprehensive identification of emission sources that comprise the heavy tail of methane emissions distributions. Our results suggest that pipeline emissions are underestimated in current inventories and highlight the importance of a large sample size when calculating basinwide pipeline emission factors.

Research on Internal Parameter Filtering of Natural Gas Pipeline under Duty Cycle Transmission Mechanism

In recent years, with the continuous growth of China's long-distance oil and gas pipeline network, wireless communication network applications have become more and more extensive with the continuous promotion of intelligent oilfield construction, while the energy-saving problem in the process of information transmission has become increasingly prominent. In order to ensure the sustainable development of China's oil and gas industry, low-power wireless transmission has become a basic requirement for the current intelligent oil and gas pipeline communication construction. For the monitoring of pipeline operating conditions, pressure and flow rate values are important parameters. If the accuracy and comprehensiveness of pipeline operation data can be ensured simultaneously under an energy-efficient transmission mechanism, it will provide strong support for the green and safe operation of pipelines. Therefore, in this paper, a recursive filtering study is conducted for natural gas gathering pipeline flow system under duty cycle transmission mechanism to complete the reliable monitoring of pipeline fluid transportation status.

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.

Natural Gas Gathering and Transmission Pipelines and Social Vulnerability in the United States.

Midstream oil and gas infrastructure comprises vast networks of gathering and transmission pipelines that connect upstream extraction to downstream consumption. In the United States (US), public policies and corporate decisions have prompted a wave of proposals for new gathering and transmission pipelines in recent years, raising the question: Who bears the burdens associated with the existing pipeline infrastructure in the US? With this in mind, we examined the density of natural gas gathering and transmission pipelines in the US, together with county‐level data on social vulnerability. For the 2,261 US counties containing natural gas pipelines, we found a positive correlation between county‐level pipeline density and an index of social vulnerability. In general, counties with more socially vulnerable populations have significantly higher pipeline densities than counties with less socially vulnerable populations. In particular, counties in the top quartile of social vulnerability tend to have pipeline densities that are much higher than pipeline densities for counties in the bottom quartile of social vulnerability. The difference grows larger for counties at the upper extremes of pipeline density within each group. We discuss some of the implications for the indigenous communities and others affected by recent expansions of oil and gas infrastructure. We offer recommendations aimed at improving ways in which decision‐makers identify and address the societal impacts and environmental justice implications of midstream pipeline infrastructure.

General Models for Optimal Design of Star–Star Gathering Pipeline Network

AbstractThe arrangement of gas gathering stations (GGS) and central processing facilities (CPF) in the natural gas gathering pipeline network really counts. Their reasonable number and locations ca...

In situ mathematically simulation for CO2 internal corrosion in wet natural gas gathering pipelines system by HYSYS

The rate of corrosion due to the presence of carbon dioxide in a carbon steel pipe is an important design consideration for gathering pipelines, particularly in the transport of wet natural gas. To evaluate the corrosion rate in a gathering pipeline, in situ simulation study using HYSYS program employed to predict CO2 corrosion in natural gas gathering pipelines system. The effects of operational conditions, inhibitors, pipe parameters and flow regime on the CO2 corrosion are studied in this paper. Increasing operating pressure leads to increase the pH and decrease the CO2 corrosion rate when the operating pressure over 10 MPa and the solution contents 0.85kmol/m3 iron carbonate. The CO2 corrosion rate does not always increase with temperature, and which will reach a maximum value at 45 °C. A high gas velocity will lead to a high corrosion rate. Less free water present in pipeline may lead to more serious corrosion than that of a large amount of free water. The corrosion rate may be inhibited by liquid hydrocarbon and glycol, and the glycol is not an economic inhibitor, especially when there is a large amount of free water in pipeline. The large diameter and flat pipe are useful for decreasing CO2 corrosion rate. The shear stress and corrosion rate will increase sharply when the slug flow forms in pipeline. Through comparing the data of simulation with field corrosion rate data, feasibility of this numerical simulation method is proved.

Constraining natural gas pipeline emissions in San Juan Basin using mobile sampling

Quantifying methane (CH4) leaks of pipeline systems is critical to ensure accurate emission factors in regional and global atmospheric models. The previous emission factors in the United States Environmental Protection Agency (EPA) Greenhouse Gas Inventory (GHGI) are from 1996 and do not reflect the modern gathering pipeline system. Additional data from different basins across the United States are urgently needed to improve the emission factors. The National Energy Technology Laboratory conducted a ground-based vehicle survey at Carson National Forest in the San Juan Basin, New Mexico, in September 2019. 187 km of natural gas gathering pipeline systems were surveyed. The mobile CH4 survey system was efficient in identifying CH4 plumes and pinpointing the leak sources. Gaussian dispersion modeling suggested our survey system had a minimum detection limit of 1.5 LPM. No leaks were found from the pipelines while a leak of 7.1 +/− 0.2 LPM was on a pig launcher door and another leak of 0.7 +/− 0.1 LPM on a block valve. Limited access to the gathering pipeline system prevented us from quantifying all potential leaks detected by the CH4 sensors. The low leak frequency phenomenon was also observed in the sole existing study of natural gas gathering pipelines in the Fayetteville Shale.

Corrosion failure analysis of the 45-degree elbow in a natural gas gathering pipeline by experimental and numerical simulation

A bursting incident occurred in a 45-degree elbow of a natural gas gathering pipeline in an oilfield. The failure analysis was performed by means of corrosion morphology observation, corrosion products analysis, and computational fluid dynamics (CFD) simulations. The results showed that a radical change in the fluid state at the 45-degree elbow due to its climbing structure and formation a region of low flow velocity, high pressure and high turbulence kinetic energy, which promoted the condensation of water vapor containing CO2. Then the corrosive water droplets flowed back under the action of gravity and accumulated at the junction between the elbow and horizontal pipeline. In such a corrosive medium, electrochemical corrosion was preferred for 20 steel. In addition, the junction of the horizontal pipe and the inclined pipe exhibits higher structural stress concentration, resulting in increased corrosion thinning. Finally, the failure of the pipeline can be attributed to the synergistic effect of electrochemical corrosion and stress accelerated corrosion.

Gathering Pipeline Methane Emissions in Utica Shale Using an Unmanned Aerial Vehicle and Ground-Based Mobile Sampling

The United States Environmental Protection Agency Greenhouse Gas Inventory only recently updated the emission factors of natural gas gathering pipelines in April 2019 from the previous estimates based on a 1990s study of distribution pipelines. Additional measurements are needed from different basins for more accurate assessments of methane emissions from natural gas midstream industries and hence the overall climate implications of natural gas as the interim major energy source for the next decade. We conducted an unmanned aerial vehicle (UAV) survey and a ground-based vehicle sampling campaign targeting gathering pipeline systems in the Utica Shale from March to April in 2019. Out of 73 km of pipeline systems surveyed, we found no leaks on pipelines and two leaks on an accessory block valve with leak rates of 3.8 ± 0.4 and 7.6 ± 0.8 mg/s. The low leak frequency phenomenon was also observed in the only existing gathering pipeline study in Fayetteville Shale. The UAV sampling system facilitated ease of access, broadened the availability of pipelines for leak detection, and was estimated to detect methane leaks down to 0.07 g/s using Gaussian dispersion modeling. For future UAV surveys adopting similar instrument setup and dispersion models, we recommend arranging controlled release experiments first to understand the system’s detection limit and choosing sampling days with steady and low wind speeds (2 m/s).

Double Marginalization and the Paths of Natural Gas Gathering Pipelines

Efficient natural gas recovery requires the development of an extensive pipeline network of gathering lines, connecting individual gas wells with processing stations and major transmission lines. Networks of gathering lines are likely to face the economic problem of double marginalization, whereby it is more expensive to run a pipe of a given length through a geographic area that is owned by multiple parties than it would be if the area were owned by a single party, ceteris paribus. Due to the economics of double marginalization, gathering lines may be placed so as to avoid paths with multiple property owners. I formalize this using a simple game theoretic model, and explore this theory empirically in Bradford County, Pennsylvania, a jurisdiction that has recently experienced a dramatic increase in natural gas production due to recent innovations in drilling. In Bradford, I observe the locations of recently drilled gas wells and, importantly, gathering lines for a single cross-section of the county taken in 2012. I combine these data with data on tax parcel boundaries in order to investigate whether the paths taken by gathering lines are influenced by parcel ownership patterns in ways consistent with the economic theory of double marginalization.