Pipeline Network Research Articles

Unraveling mechanisms and failures of stray current corrosion in urban Subway adjacent pipeline networks through statistical insights and 3D simulation

The escalating expansion of urban subway systems in recent years has accentuated the issue of stray current corrosion within pipeline networks, emerging as a critical concern for urban safety. This paper delves into the intricate interplay between these phenomena, employing data-driven statistical analyses to elucidate the coupling characteristics between subway lines and the occurrence of failures in adjacent buried pipelines. An advanced three-dimensional finite element model was developed for stray current corrosion in pipelines, seamlessly integrating empirical data and physics-based modeling, which is to uncover the spatial nuances and multifaceted impacts on subway pipeline corrosion from both macro and micro perspectives under varying influencing factors. The study unveils a pronounced geographical and functional affinity between urban subway networks and metallic pipeline networks. The coupling attributes between subway systems and pipelines, such as distance, angle, and pipeline-specific characteristics including material and age, assume pivotal roles. The results further emphasize the hierarchical order of influence, with stray current intensity holding the greatest sway, followed by the distance between subway and pipelines, the angle between them, and soil resistivity. This paper offers a comprehensive investigation of the interrelationships and influential factors between subway systems and adjacent pipelines. It contributes to the mitigation and management of stray current corrosion in pipelines induced by nearby rail transit, thereby enhancing the resilience of both subway and pipeline networks within urban areas.

Thermal Hydraulic Analysis of Turbulent Flow of Superheated Steam Through Multiple Configurations of 3D Angular Piping Bend in Power Plant Installations

Abstract One of the most vulnerable parts in high-pressure and temperature pipeline networks in steam turbine power plant installations is pipe bends, due to intense fluctuation of velocity and pressure of the superheated steam flowing through the piping bend. On the other hand, because of unique chemical and physical properties, the transportation of superheated steam can adversely effect on the integrity of the pipe bends in the piping network. Therefore, the potential risk of pipeline failure becomes more probable at bends. Therefore, to ensure the survivability of the piping network, thorough investigations of the superheated steam flowing through pipe bends are of great significance in better understanding its flow behavior. However, to predict flow behavior at piping bend, intensive research by direct experiment at high temperature and pressure might not be possible to some extent. In that case, computer codes and models can help us analyze flow behavior of superheated steam at pipe bends. The current work is a computational fluid dynamics investigation, where numerical examination is carried out by commercial code ansys fluent for thermal hydraulic analysis of flow behavior of superheated steam through multiple configurations of 3D angular piping bend: 90 deg, 120 deg, and 135 deg. Validation of credibility of the computational approach is done against Sudo et al.'s experiment. With relatively good agreement between the experiment and numerical result using air flowing through 90 deg elbow, single-phase turbulent flow of superheated steam is examined by the application of realizable turbulence model (k–ɛ). The final assessment of the computed results has been done using qualitative and quantitative analyses. The study reveals that unlike the pressure profiles, the radial velocity profiles at the bend exit are noticeably different from those at the bend inlet, since they exhibit more rounded peaks and gentler velocity gradients. The study also shows that the radial pressure and velocity profiles do not display any sign of the existence of a pair of vortices.

Hybrid Power System Design and Dynamic Modeling for Enhanced Reliability in Remote Natural Gas Pipeline Control Stations

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator.

Robust pressure prediction of oil and gas pipeline networks based on equipment embedding neural network

Currently, extensive pipeline networks are developed in response to the demands of the oil and gas industry. The accurate estimation of the hydraulic condition of pipeline networks holds significant importance in the fields of pipeline design and safety management. Nevertheless, predicting the pressure of oil and gas pipeline networks with different equipment and structures remains challenging. To meet this challenge, a novel pressure prediction model for the oil and gas pipeline networks based on the equipment embedding neural network (EENN) is proposed in this study. The proposed model embeds different equipment models into the neural network model. The neural network in this model is used to focus on learning the connection characteristics of the pipeline network to achieve higher prediction accuracy. The present study first explores different embedding combinations of the EENN model to estimate the pressure in an oil pipeline network system that involves a non-isothermal batch transportation process. Then, the trained model is applied to predict the pressure in a gas pipeline network. The optimal EENN exhibits an average prediction error of 18.5% for oil pipelines and 0.36% for gas pipelines, which is lower than 20.8% and 3.57% under the neural network. The findings of this study demonstrate the efficacy of the proposed EENN in accurately forecasting pressures in diverse oil and gas pipeline networks by reducing the complexity of the learning process.

A network design problem for upgrading decentrally produced biogas into biomethane

In this paper, we explore the possibility of connecting decentralized biogas plants via a pipeline network to terminals that upgrade biogas into biomethane. We present a mixed-integer linear program that forms subnetworks of such plants, decides on suitable terminal locations, and establishes pipeline connections to maximize profit. We apply this model to a real-world scenario in Northern Germany. The results show a much higher total profit for the optimized network compared to the benchmark solutions where each plant upgrades biogas into biomethane on its own. Therefore, plants can increase their profitability by collaborating with other (neighboring) plants. However, the collaboration requires a fair profit-sharing model as network participation is not individually profitable for all plants, especially small ones.

Recovery of oil viscosity values according to its additive parameters

Considering the complexity of mixtures formed within extensive and intricate pipeline networks, determining the viscosity of mixtures through a computational method that utilizes parameters conducive to additivity is advantageous. These parameters may include the density and component composition, as well as corresponding derivatives, such as the content of paraffins, resins, asphaltenes, and fractional composition. This study explored the relationship between the viscosity and these parameters. A linear regression model for estimating the logarithm of the viscosity was developed, where significant regression coefficients were identified, and the prediction error was assessed through cross-validation. A similar approach was adopted for the second-order regression analysis. This analysis incorporated linear regression including parameters pertaining to the fractional composition. Formulas for calculating the viscosity based on the identified properties were derived and the respective errors were evaluated. Keywords: oil; viscosity; Arrhenius formula; cross-validation; paraffin; resins; asphaltenes; database.

A multi-step attack path prediction method for oil & gas intelligence pipeline cyber physics system based on CPNE

Owing to the integration of the 5th Generation Mobile Communication Technology (5 G) and industrial internet construction, Oil & Gas intelligent pipeline network has witnessed accelerated construction and development. The Oil & Gas intelligent pipeline network proposed by China National Petroleum Corporation exhibits the advantages such as high integration, data unification, and intelligent decision-making, which helps to greatly reduce manual management costs, optimize risk decision-making by managers, and avoid risks such as human misuse. However, the highly integrated Cyber-physical system of the Internet and the Internet of Things faces various cyber security threats, and traditional intrusion detection systems exhibit drawbacks in the face of complex cyber-attack behaviors. In response to incomplete multi-step attack path prediction in traditional prediction methods, this paper proposes a multi-step attack path prediction method for Oil & Gas intelligence pipeline cyber physics system (CPS) based on Colored Petri nets and mixed strategy Nash equilibrium (CPNE). Taking into account the importance of assets and the dependency relationships between assets within the cyber-physical system of Oil & Gas intelligent pipeline network, CPNE constructs a node dependency model for the cyber-physical system of Oil & Gas intelligent pipeline network, simulates multi-step attack and defense games, calculates the probability of node failure triggering and transfer using mixed strategy Nash equilibrium, and extracts the risk of multi-step attack paths for different attack strategies. With the filtration and separation unit of a certain natural gas station as an example, this paper establishes a CPNE model to rank the risk of the first multi-step attack single attack source path and predict the risk of subsequent multi-step attack source paths, thus predicting multi-step attack paths. Taking three rounds of attacks as an example, compared to the paths predicted by traditional probability attack graph models, the proposed CPNE model is more advantageous in multi attack source path prediction, with a 2.33-fold increase in the relative coverage rate of path predictions.

Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies

Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.

Simulation Analysis of Influence of Accident Conditions on Hydraulic Reliability of Gas Pipe Network

Taking reliability as the starting point, based on Pipeline Studio software and combined with the actual situation of medium-pressure gas pipe network in Weidu District, Xuchang City, the operation conditions of gas pipe network under design conditions and accident conditions were simulated, and the accident conditions were assumed to be the shutdown of important pipe segments for reasons or the failure of the pipe segment with the maximum flow rate. According to the simulated hydraulic calculation results, the overall gas supply capacity of the pipeline network is comprehensively evaluated and analyzed, and the conclusion is reached that the pipe network can meet the requirements of normal and accident conditions by properly enlarging the pipe diameter closer to the gas source point.

A new optimization model for carbon capture utilization and storage (CCUS) layout based on high-resolution geological variability

Carbon capture utilization and storage (CCUS) is expected to play a pivotal role in achieving carbon-neutral pledge. However, the neglect of geological volatility in existing models will lead to obvious bias in the CCUS layout optimization results. Therefore, a new optimization model for CCUS layout is developed based on high-resolution geological variability. Finally, the model is validated by solving the optimal CCUS layout of coal-fired power plants (CFPPs) in a case region till 2060. The obtained results show that (i) To accomplish the same emission reduction targets, the total length of the pipeline network by 2060 in the scenario considering high-resolution geological variability (HGV) will decrease by 12.5% compared with the scenario without geological variability (NGV); (ii) The total crude oil production can be enhanced by about 19.0% in HGV scenario compared with NGV due to the optimized CO2 source-sink matching; (iii) Comparing with the NGV, the CCUS layout under the HGV can decrease 26.1 and 32.7 CNY/tCO2 in capital costs and O&M costs respectively, and ultimately improve the total profit by 149.4 CNY/tCO2. In summary, the proposed new model can solve the optimal CCUS layout and operation more practically and accurately.

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.

Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network

Linear Array Pushbroom (LAP) imaging technology is widely used in the realm of remote sensing. However, images acquired through LAP always suffer from distortion and blur because of camera jitter. Traditional methods for restoring LAP images, such as algorithms estimating the point spread function (PSF), exhibit limited performance. To tackle this issue, we propose a Jitter-Aware Restoration Network (JARNet), to remove the distortion and blur in two stages. In the first stage, we formulate an Optical Flow Correction (OFC) block to refine the optical flow of the degraded LAP images, resulting in pre-corrected images where most of the distortions are alleviated. In the second stage, for further enhancement of the pre-corrected images, we integrate two jitter-aware techniques within the Spatial and Frequency Residual (SFRes) block: 1) introducing Coordinate Attention (CoA) to the SFRes block in order to capture the jitter state in orthogonal direction; 2) manipulating image features in both spatial and frequency domains to leverage local and global priors. Additionally, we develop a data synthesis pipeline, which applies Continue Dynamic Shooting Model (CDSM) to simulate realistic degradation in LAP images. Both the proposed JARNet and LAP image synthesis pipeline establish a foundation for addressing this intricate challenge. Extensive experiments demonstrate that the proposed two-stage method outperforms state-of-the-art image restoration models. Code is available at https://github.com/JHW2000/JARNet.

Algerian gas to strengthen energy security of the European Union: Policy, capacity and strategy

The last decade has seen relative stability in the global gas market, with European countries benefiting from access to abundant and inexpensive gas supplies, primarily from Russia. However, the onset of the Russo-Ukrainian war has exposed Europe's energy vulnerabilities, given its heavy reliance on Russian gas. In response, Algeria has emerged as a key alternative, drawing increased diplomatic engagement from the European Union. Algeria's significance stems not only from its considerable gas reserves and potential for production expansion but also from its geographical proximity to Europe and its robust gas infrastructure, including LNG tankers and a comprehensive pipeline network. This paper seeks to delve into Algeria's potential to bolster the European Union's energy security. Through a detailed examination of Algeria's resource capabilities and insights drawn from the experiences of Spain and Italy—countries that have navigated political tensions and competition with Russian gas respectively—the study aims to provide a nuanced analysis of how Algeria can play a pivotal role in ensuring a stable and secure energy supply for Europe.

Leakage detection method of underground heating pipeline based on improved wavelet threshold function

For urban heating pipeline network, the intelligent and precise detection is an important guarantee for urban heating infrastructure of the safe and low-carbon operation. Heating pipe network was directly buried in the soil and difficult to detect the leakage point due to lack of non-excavation detection method for rapid repair. An improved noise reduction algorithm of the wavelet threshold function coupled with the acoustic method was used for the directly buried pipe leakage detection. Large-scale leakage experimental tests were used to validate the reliability. The influences of temperature, pressure, flowrates and leakage distance on the frequency and time domains were then investigated. Results show that the improved threshold function could well detect the leakage for directly buried hot water heating pipes. When the temperature increases, the leakage frequency domain spreads from 200 - 800 Hz to 50–1500 Hz. The pressure, flow rate, and leak point location could affect the amplitude feature. The accuracy ranges of the improved method in the different location, temperature and pressure and flowrates are separately 0.11–2.36%, 0.11–1.96%,0.11–3.49% and 0.16–1.55%, respectively.

New Approach to SCADA System Screen Configuration Based on the Model of Oil and Gas Pipeline Network

INTRODUCTION： With the continuous progress of science and technology, the monitoring and control of oil and gas pipeline networks have become more and more critical; SCADA systems, as a kind of technology widely used in industrial control, play a key role. The screen configuration of the SCADA system is the core part of its user interface, which is directly related to the operator's mastery of the status of the pipeline network. In order to improve the monitoring efficiency and reduce the operation risk, this study is devoted to exploring a new method of SCADA system screen configuration based on the oil and gas pipeline network model.PURPOSE: The purpose of this study is to develop an innovative SCADA system screen configuration method to present the operating status of the oil and gas pipeline network more intuitively and efficiently. The design based on the pipeline network model aims to enhance the operators' understanding of essential information, such as pipeline network topology, fluid flow, etc., so as to make monitoring and control more intelligent.METHODS: The study adopts a new method of SCADA system screen configuration based on the oil and gas pipeline network model. First, the topology, sensor data, and control nodes of the oil and gas pipeline network are comprehensively modelled. Then, through the design principle of human-computer interaction, the modelling results are integrated into the screen configuration of the SCADA system to realize the intuitive presentation of information. At the same time, advanced visualization technology is introduced so that the operators can understand the real-time changes in the pipe network status more clearly.RESULTS: After experimental verification, the new method shows significant advantages in oil and gas pipeline network monitoring. The operators can recognize the abnormalities of the pipeline network more quickly and accurately through the SCADA system screen configuration, which improves the efficiency of troubleshooting and treatment. The visualized interface design makes the operation more intuitive and reduces the possibility of operating errors, thus improving the safety and reliability of the pipeline network.CONCLUSION: The new method of SCADA system screen configuration based on the oil and gas pipeline network model has achieved significant results in improving monitoring efficiency and reducing operational risks. Through a more intuitive and intelligent interface design, operators can have a more comprehensive understanding of the operating status of the pipeline network, which provides practical support for rapid response and decision-making. This approach introduces new ideas to the field of oil and gas pipeline network monitoring, which is of positive significance for improving the overall performance of the system. Future work can be carried out to optimize the interface design further and expand the applicable scenarios.

OPC UA Application Study in Oil and Gas Pipeline Network Monitoring Data Forwarding

INTRODUCTION： With the continuous development of oil and gas pipeline network monitoring and control technology, the need for data transmission and communication is becoming more and more prominent. In this context, OPC UA has attracted wide attention. This study aims to explore the application of OPC UA in data forwarding for oil and gas pipeline network monitoring in order to improve the efficiency, reliability and security of data transmission.PURPOSE: The purpose of this study is to evaluate the applicability of OPC UA in oil and gas pipeline network monitoring and to verify its performance in data forwarding through empirical studies. By gaining an in-depth understanding of the characteristics of OPC UA, it aims to provide a more advanced and efficient monitoring data transfer solution for the oil and gas industry.METHOD: The study adopts a combination of field monitoring and laboratory simulation. First, the essential characteristics and requirements of monitoring data in oil and gas pipeline networks were collected. Subsequently, a monitoring system with OPC UA as the communication protocol was established and field tested. In the laboratory environment, data transmission scenarios under different working conditions were simulated, and the performance of OPC UA under different conditions was analyzed.RESULT: The field monitoring results show that the data transmission efficiency is significantly improved by using OPC UA as the communication protocol for data forwarding in oil and gas pipeline network monitoring. Meanwhile, the system performs well in different environments with high reliability and security. The laboratory simulation results further verify the stability and adaptability of OPC UA under complex working conditions.CONCLUSION: OPC UA is an effective communication protocol that can meet the data transmission requirements for oil and gas pipeline network monitoring. Its efficient, reliable, and secure characteristics make it an ideal choice for improving the communication performance of monitoring systems in the oil and gas industry. The empirical results of this study provide reliable technical support for the oil and gas industry in the field of data transmission and a vital reference for the optimization and upgrading of monitoring systems in the future.

A universal parallel simulation framework for energy pipeline networks on high-performance computers

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.

Modeling Gas Compressor Station Operation to Minimize Fuel Costs for Surge Zone Protection

Gas transportation through the pipeline network requires high pressure; otherwise, movement will be impossible. Due to friction with the walls of the pipes, the gas loses both speed and pressure. Therefore, it is necessary to increase the pressure in the main pipeline, which can extend for thousands of kilometers. For this purpose, compressor stations are located along the main network, facilitating the gas transport process by raising it to the required pressure. Compressor stations mainly consume the transported gas, with approximately 5% expended on gas compression. This article presents a study on the fuel cost for compressor stations, using mathematical models to optimize the operation of gas compressor units. The objective is to minimize gas consumption as a fuel and provide an optimization method with control over blower revolutions and valve installations to ensure safe transportation. Mathematical modeling of the described process detected the technically unjustified fuel gas consumption during the operation of gas compressor units at a specific compressor station under consideration. The paper proposes a method for minimizing costs, which can serve as the basis for providing recommendations to engineers-dispatchers. Compressor stations of the type under study lack measuring instruments for mass flow; therefore, the methods such as PID controllers, MPC, or neural networks are not applicable here.

Application of Unsteady Fluid Flow Simulation in the Process of Regulating an Industrial Hydraulic Network

In this article, an analytical solution to a hydraulic network with a wide range of pipe lengths (up to 10 km) is proposed. The Finite-Difference Time-Domain (FDTD) method was applied with the aim of creating a regulation model for controlling both the flow rate of water from one of the two sources and the discharge pressure in the system. The system inertia requires an understanding of boundary conditions in the operation of pipeline networks, which must be known in order to regulate the required parameters with only minor deviations. The proposed model was compared to experimental data, while the mean absolute deviations in the individual system branches ranged from 1 to 5.19%. The created regulation model was subsequently tested by applying linear, sine and stochastic changes in the output load, while the ability to control the discharge pressure and the selected water flow rate was analysed. The effect of coefficient ε, which multiplies the effect of the difference between the measured and the predicted value of the discharge pressure on the boundary conditions of the discharge pressure in the system, was analysed in this paper. With the use of the proposed unsteady simulation of the fluid flow in the hydraulic system arranged in parallel and in series, the maximum deviation of the regulated pressure was 1.2% and the maximum deviation of the regulated flow rate was 5.3%.

GIS-BASED REAL-TIME GAS LEAK DETECTION SYSTEM AT BASKENT NATURAL GAS DISTRIBUTION COMPANY

Abstract. Natural gas has been used for heating and production purposes in Turkey since 1987, and every passing year, the natural gas pipeline network continues to expand with investments. The distribution of natural gas within urban areas is achieved through a complex network structure formed by the joining of Polyethylene (PE) and Steel (ST) pipes using various connection elements. This complex structure is susceptible to deformation over time due to various reasons such as corrosion in steel lines, manufacturing errors in connection elements in PE lines, and other factors. As a result, natural gas leaks occasionally occur in these pipelines. The leakage control process in natural gas pipelines is carried out using specially designed equipment and vehicles equipped for this purpose. However, while these systems may be sufficient for collecting the necessary data for leak detection, the complex structure of the distribution network and the necessary data for leak detection, the complex structure of the distribution network and the necessity of performing this process with multiple teams bring about many challenges in terms of tracking, management, and reliability of the operation. At this point, the advancement of GIS (Geographic Information System), mobile, and communication technologies has made it possible to overcome these challenges. In this study, a real-time GIS-supported leak detection system developed using these technologies for Başkent Natural Gas Distrubition Inc. (Başkentgaz), which provides natural gas distribution services in Ankara, has been described. The study discusses the benefits brought by this system to the natural gas leak detection process.