Journal of Pipeline Systems Engineering and Practice

The problem of corrosion in oil and gas pipelines is one of the major factors affecting the process safety and efficient sustainability development of the oil and gas industry. To gain a better understanding of global research trends and dynamics in the field of oil and gas pipeline corrosion and to advance the development of corrosion control technology, we conducted a literature review using a sample of 1,745 papers from the Web of Science (WOS) database published from 2002 to 2022. We employed a bibliometric analysis approach employed to investigate the distribution of publications over time, geographic regions, major organizations, major authors, journal cocitation, and literature cocitation, and to identify research hotspots and frontiers. The results revealed an exponential growth in the overall number of papers, with the most rapid increase occurring in the last 4 years. China, the US, Canada, the United Kingdom, and Brazil emerged as the most active countries in oil and natural gas pipeline corrosion research, and Mexico, Canada, and Australia also exhibited significant influence in the field. The journals Engineering Failure Analysis, Corrosion, and Corrosion Science had the highest number of publications and impact in this domain. Notably, Corrosion Science stood out as the most influential and highly regarded journal in the corrosion field. The fundamental theories and research framework in the realm of oil and natural gas pipeline corrosion have been primarily established, and a large number of research directions and frontier branches are emerging. The impact of flow parameters on corrosion, pipeline reliability assessment, and analysis of corrosion defects and failures are identified as the three main development paths in this field. In terms of research methodologies, machine learning techniques are becoming increasingly prevalent, with a growing number of studies adopting various machine learning methods. Among these methods, explainable deep learning is at the forefront of development in the field of oil and natural gas pipeline corrosion.

Abstract

Buried medium-density polyethylene pipes are conveniently used in gas distribution systems, particularly in areas subjected to ground movements, due to their flexible property to accommodate large deformation. The pipelines experiencing large ground movements require assessments for the fitness-for-services. Conventionally, the beam-on-spring idealization is used to evaluate pipelines exposed to ground movements. However, it is challenging for the beam-on-spring idealization to identify the spring parameters for representing soil–pipe interaction appropriately, which may vary from problem to problem. In the current study, three-dimensional (3D) finite element (FE) modeling was employed to understand soil–pipe interaction near the connection of a branch to a main pipe subjected to lateral movement. The FE model was developed through validation with full-scale test results. The study revealed that the conventional elastic–perfectly plastic model with a stress-dependent modulus of elasticity for soil could be reasonably used to simulate pipe–soil interaction observed during the tests. The FE analysis effectively simulated the mechanisms observed during the tests. Similar to the observations in the tests, the analysis calculated a lower pulling force yet higher strains for shallow buried pipes than for deeply buried pipes, confirming lower resistance to bending of the shallow buried pipes. The calculated contact pressures were nonuniform along the pipe length, indicating nonuniform axial and lateral soil resistances to the pipe. Thus, the spring forces recommended in the design guidelines should be revisited to account for the variation of contact pressure to model the pipe behavior using the conventional beam-on-spring analysis.

Practical Applications

The outcomes of this research would apply to the integrity assessment of MDPE pipes in areas prone to ground movements. Pipes in a distribution network can experience additional stresses/strains due to ground movements, which should be evaluated for fitness-for-service assessment. This paper presents an assessment of pipe wall strains near a connection of a network branch. The understanding from this research would be useful for assessing pipes in the field under a similar ground movement scenario.

Under the fair open-access operation mode of gas pipeline network facilities, the accurate evaluation of the remaining capacity of a gas pipeline network can help to grasp the load rate of the pipelines, and the remaining capacity can be sold to the market to increase the utilization rate of the pipelines. In this paper, the indices of the remaining capacity of injection nodes, delivery nodes, and the pipeline system are proposed, and the theoretical as well as actual remaining capacity under different gas allocation principles are analyzed. Mathematical optimization models considering the physical, legal, and gas allocation constraints are established to evaluate the remaining capacity of nodes and pipeline systems. All the mathematical optimization models established are nonlinear programming models, which are solved using the interior-point method. The indices and mathematical models proposed for the remaining capacity are verified using a gun-barrel and branch-structured pipeline system, meanwhile, the results of the theoretical and actual remaining capacity of nodes as well as pipeline systems under different gas allocation rules are compared and analyzed in detail.

The distribution of internal defects in large-diameter steel pipes can be complicated. Due to the harsh working environments involved, the manual grinding method for correcting such defects is inaccurate, inefficient, and risky. Therefore, in this study, we designed a new internal pipe grinding robot with five degrees of freedom. The grinding trajectory was optimized, and dynamic and kinematic models of the robot were established for the internal space of a pipe with a diameter between 800 and 1,200 mm. The feasibility of the robot was verified by simulation. A trajectory optimization method that focuses on the time, energy, and impact of the trajectory of the grinding robot is proposed. The method uses modified multiobjective particle swarm optimization (MMOPSO) in the robot’s trajectory planning module to obtain a Pareto solution set. The analytic hierarchy process is used with the minimum fuzzy entropy (AHP-MFE) method in the decision-making module to select the most appropriate trajectory according to the relevant working conditions. The experimental results validated the space accessibility and trajectory optimization performance. Compared with the original trajectory, operation time was shortened by 15.4%, energy consumption was reduced by 17.5%, and impact was reduced by 12.4%. The experimental error was less than 2.8%. The experimental results showed that the pipe grinding robot is safe and efficient.

Abstract

Piping is an essential component of processing plants that transfers process fluids from one piece of equipment to another. To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or corrective maintenance activities, usually replacements, should be done. Therefore, it is necessary to develop an optimal maintenance strategy to replace piping systems to reduce the overall maintenance expenditure and avert the effects of pipe failure. Most of the literature in this field concentrates on maintenance activities executed during decision-making. This paper investigates creating a system and tool to assist decision-makers in evaluating and selecting the optimal moment to replace a deteriorating pipe based on the current state of the piping system. Considering that preventative replacement of the pipeline is done during a scheduled shutdown, also referred to as a turnaround, this paper investigates making a preventative replacement ruling two turnaround cycles ahead. We account for the varying thickness at different monitoring locations of two piping systems in a refinery by applying a nonstationary gamma process with random effects. We employ the Markov decision process (MDP) and the value iteration algorithm to determine the most effective maintenance replacement policy. The outcome of our analysis is an optimal maintenance policy that minimizes the cost of maintenance.

Practical Applications

This paper’s practical use is to aid in maintenance planning in refineries, midstream or pipelines, chemical plants, and gas facilities by planning piping replacement in two shut-down cycles ahead of time. The pipe wall is classified into states ranging from the present thickness reading to the retirement thickness. The likelihood of transiting from one state to another is calculated based on the corrosion rate. Given that the pipe wall thickness is classified into states, the current wall thickness, transitional probabilities, and preventative and corrective maintenance costs, including lost profit opportunity, can be inputted into the model to help decision-makers decide if it is optimal to replace the piping in the subsequent two shut-down cycles. This policy will assist in reducing the risk of a pipe failure, which has financial, safety, and environmental consequences. It is also critical not to replace a still-in-good-shape pipe. This model may be customized to pipe systems in the various petrochemical sectors with known retirement thicknesses to estimate the risk of operating near the retirement thickness.

Abstract

In engineering applications, the simultaneous suspension and corrosion of pipelines under unstable geological conditions present significant challenges, leading to large deformations, stress concentrations, and potentially catastrophic failures. Previous research often overlooked the simultaneous impact of these factors, resulting in inaccuracy in the failure analysis and further maintenance strategies. To fill the gap, this paper employs numerical analysis to create and simulate a series of pipe–soil coupling models uniquely considering internal corrosion for a more precise investigation of mechanical and failure behaviors in high-strength spanning pipelines. This research pioneers a comprehensive parametric analysis, exploring key factors such as operating conditions and geometric features on deformation, stress, strain, and stress concentration factor (SCF), all vital for failure determination. The results offer crucial insights, showing that maximum stresses in corroded spanning pipelines occur within the corrosion area, unlike in intact pipelines, where they typically appear at the span end. Increases in spanning length, corrosion dimensions, and internal pressure lead to upward trends in the maximum longitudinal stress, strain, and vertical displacement. The axial location of the internal corrosion also significantly affects the pipeline’s mechanical state. The longitudinal strain escalates along the axial location, being 1.97 times smaller at $2/10l_s$ compared with $5/10l_s$. This study diverges from standard analyses by innovatively spotlighting the significant impact of corrosion width on spanning pipeline safety, particularly affecting longitudinal SCF. Maximum SCF increases by 36.1% with the corrosion width expanding from 5° to 50°, showcasing a more pronounced effect than that of corrosion length. This study advances pipeline integrity knowledge and lays the groundwork for future research, offering crucial tools for life span prediction and failure prevention in spanning pipelines.

Practical Applications

Our study reveals a more accurate way to simulate and predict when and where pipelines might fail due to the combination of internal corrosion and spanning. Traditionally, it was thought that the ends of suspended pipelines were most at risk, but our research shows the weak spots are within the corrosion area. The obtained stress concentration factor can be further used to estimate the fatigue life of a spanning pipeline. The simulation results also suggest that corrosion width is vital for spanning pipelines’ mechanical behaviors.

Hydraulic transient analysis through numerical simulations is a widely used tool for predicting and mitigating potential severe pressure oscillations due to abrupt changes in flow velocity. In practice, water hammer events typically are induced by valve operations used to control the flow rate. One of the methods to enhance the throttling capabilities of a gate valve is to install a V-shaped notch (V-port) in its opening. This provides a more linear relationship between the position of the gate and the flow rate discharging through the valve. The exact range of the flow control is determined by the V-port opening angle. Because valve-induced pressure oscillations depend on the valve characteristics, in order to accurately simulate the transient event, the specific valve closing function is required. The primary objective of this paper was to develop valve closing functions of V-port gates for all possible opening angles. Two cases were considered: the linear closure of a knife (rectangular) and a circular gate fitted with a V-port. For this purpose, the analytical formulas for calculating the relative opening area of V-shaped gate valves were derived. On this basis, valve closing functions, in the form of a power function with two variables, were developed. To analyze the influence of the V-port angle on a simulated transient event, a case study with a simple reservoir–pipeline–valve system was investigated. One-dimensional unsteady flow equations were solved using the method of characteristics. The numerical calculations conducted indicate that installing a V-notch port can result in a smoother reduction of the flow rate as the gate closes, which may dampen both maximum and minimum pressure peaks and attenuate valve-induced pressure head oscillations. The developed valve closing functions offer practical utility for practitioners engaged in hydraulic system design and operation, providing a tool for predicting hammer events induced by valve gates fitted with V-ports.

Abstract

Pressurized irrigation network systems constitute an important water-saving approach, but their relatively high costs limit their promotion. Pipe diameter optimization is the most direct way to reduce irrigation costs and can bring significant economic benefits. In recent years, material technology developments have improved networks’ impact resistance, resulting in traditional flow velocity constraints no longer applying to current network designs, and appropriately increasing the velocity has become an engineering requirement. In addition, with the update of engineering construction standards, the location of pipe diameter changes is no longer limited to pipe section connections and is allowed to change between standard pipes. Therefore, this paper removes the constraint of the upper limit of flow velocity, defines it as a factor related to the maintenance cost of the network, and establishes a pipe section optimization model. Further, this paper changes the minimum optimization unit from pipe sections to standard pipes and establishes a standard pipe optimization model. The results show that compared with traditional optimization methods, the pipe section optimization model that deletes the upper velocity limit can reduce costs by 5.1%, in which the velocity will not increase infinitely but will be stable within a reasonable range. The standard pipe optimization model can reduce irrigation costs by 12.1%. It is worth noting that the two models were optimized to balance the head loss; their energy costs did not change significantly with the reduction in pipe diameter. In addition, this paper changes the structure of the traditional algorithm and designs a structure of double selection and parallel variation, achieving a faster and more accurate solution than the classic genetic algorithm.

Practical Applications

Smart irrigation is an irrigation method based on advanced technology and data analysis that can manage water resources more accurately and efficiently. As an essential part of smart irrigation, pressure networks have the characteristics of saving water resources, improving the water utilization coefficient, and facilitating management and control. Their promotion can promote the development of agriculture. However, high network costs limit the popularity of smart network irrigation. Therefore, this study is based on the latest material technology, cancels the upper limit of flow velocity, and establishes a pipe section optimization model. Furthermore, this paper changes the minimum optimization unit from pipe sections to standard pipes and establishes a standard pipe optimization model. The research results show that the standard pipe optimization model can significantly reduce irrigation costs. This has practical significance for increasing farmers’ income and promoting the development of smart pipeline irrigation. In addition, this paper changes the structure of the traditional algorithm and designs a structure of double selection and parallel variation, achieving a faster and more accurate solution than the classic genetic algorithm.

Abstract

This study assessed the vulnerability of groundwater resources to the failure of the urban fuel distribution network under an earthquake. A case study of the Tehran, Iran, gas distribution network and the Tehran–Karaj Plain aquifer was conducted. To assess the seismic vulnerability of buried fuel pipelines in Tehran based on the fuel distribution network components, three possible earthquake scenarios were studied. To assess damage to the pipeline, a comprehensive model was developed using machine learning (ML). This model can assess and predict damage to a fuel pipeline and its type (i.e., leakage or full breakage). Moreover, aquifer contamination was assessed using the DRASTIC model. It was found that the ML-based pipeline seismic vulnerability assessment model had good performance in predicting seismic damage to the fuel distribution network, with a RMS error (RMSE) and a correlation coefficient ($R$) of 0.004 and 0.99, respectively. The results showed that the presented model had an acceptable efficiency in assessing the probability of seismic vulnerability of the buried pipeline and analyzing the pollution of the aquifer based on different earthquake scenarios. The developed groundwater seismic vulnerability assessment model can be used for further analysis in future research.

Practical Applications

Earthquakes are one of the most important natural disasters, and have caused widespread financial, human, and environmental losses in different regions of the world, especially in seismic areas. The existence of faults and the possible deterioration of buried pipes makes earthquake crisis and its serious damage to humans and the environment more severe. One of the most important threats in this situation is the contamination of underground water with hydrocarbon substances due to leakage from the fuel transmission network. In this research, to evaluate the pollution of the aquifer due to the damage to the fuel transmission network, we developed a model using the machine learning method to analyze the vulnerability of the buried pipeline. The DRASTIC model also was used to evaluate aquifer pollution. To evaluate the presented model, the fuel transmission network and aquifer of Tehran, Iran, were studied. The results indicated acceptable performance of the proposed model for assessing the seismic vulnerability of groundwater. The presented model can be used for other areas.

Abstract

Damage to pipeline welds during service can result in substantial human and economic losses. Hence, early and accurate quality diagnosis of in-service natural gas pipeline welds is of utmost importance. However, the current diagnostic methods are plagued by low accuracy and excessive resource consumption. Therefore, a fusion model for diagnosing the quality of in-service natural gas pipeline welds is constructed based on gradient boosting decision tree (GBDT), $k\text{-}\text{nearest}$ neighbor (KNN), and random forest classifier (RFC). Initially, heterogeneous raw data sets undergo data cleaning and quality enhancement processes. Subsequently, the predictions from GBDT, KNN, and RFC are fused using voting as the fusion strategy. The processed data set is then utilized to train and test the fusion model (MIX). After verifying the reliability of the model through cross-validation, the performance of the model’s prediction results is assessed, and a comprehensive analysis of the model’s characteristics is conducted by combining theory and empirical evidence. By analyzing the experimental results, the fusion model performed better than the single model, with an accuracy rate of 96%. It can effectively diagnose the quality of natural gas pipeline welds in service, providing a new reference for existing diagnostic methods.

Practical Applications

The fusion modeling method proposed in this study can be applied to the diagnosis of welding quality of in-service natural gas pipelines in order to solve the problem of early quality diagnosis of natural gas pipelines and improve the diagnosis of welding quality. The method can effectively overcome the problem of small size and poor quality of engineering data sets. First, the original data set is cleaned and downscaled, and then the synthetic minority oversampling technique (SMOTE) combined with edited nearest neighborhood (ENN) (i.e., SMOTEENN) is balanced to improve the quality of the data set. Then, the data set is predicted by three models, and the three predictions are fused using a voting fusion strategy to effectively improve the diagnostic ability of the models. Finally, the importance of the output features of the fused models is utilized for empirical and theoretical information mining. The experimental results show that the diagnostic accuracy of the method can reach 96% with high reliability and stability, which can provide technical support and theoretical basis for the diagnosis of the welding quality of in-service natural gas pipelines. Mining feature information can also provide more reference information for the pipeline welding construction stage, improve the initial welding quality, and reduce welding defects.

Abstract

Rapid urban, economic, and population growth has led to increased water demand and reduced availability. At the same time, the condition of water distribution systems has deteriorated, and water leakage has increased. Although previous research has developed models to simulate leaks in water distribution systems, these models do not consider the distribution of varying leak types and their distribution in different pipe materials, and little is known about this topic. Therefore, this study aimed to identify the most effective pipe failure distribution for analyzing failure types in various pipe materials by using the maximum likelihood approach. The study used data on water pipe failures gathered from Auckland’s water network over six years (2017–2022) to accomplish its goal. The results show that the log-normal distribution is the best distribution for longitudinal, circumferential, and round holes, whereas the normal distribution is the best for blowout holes. Finally, the reported plus unreported failures were derived from the fitted distribution for estimated data. The study’s findings provide valuable insights into the distribution of leak types and their occurrence in different pipe materials, which can help in developing effective leak management strategies. The discussion of the study highlights the limitations and implications of the research and suggests directions for future research to improve the accuracy of the models used to simulate leaks in water distribution systems. Overall, this study contributes to the understanding of the complex issue of water leakage and provides important information for water authorities and policymakers to develop effective strategies to minimize water loss and ensure a sustainable water supply.

Practical Applications

This study offers initial insights into the type and dimensions of pipe failures occurring in different pipe materials based on photographic records of pipe repairs conducted in Auckland’s water distribution system in New Zealand. An extensive literature review of pipe failure studies revealed no previous work describing the dimensions of leaks discovered in water distribution systems. This information is critical for better understanding pipe failure types and mechanisms and the development of more realistic stochastic leakage models for water distribution systems. Such stochastic models will allow the impact of leaks on system flow rates and pressures to be investigated and provide more realistic scenarios for network simulation and evaluation of leak detection algorithms. The research only had access to a limited number of failures, and it recommends more research on the types and dimensions of leaks in water distribution systems. A better understanding of pipe failure patterns will assist water utility managers and engineers in making more informed decisions on material selection, maintenance, and repair strategies, ultimately reducing water loss and costs associated with pipe failures.

Abstract

Optimal design for pipelines layout is gaining further importance because of the increase in demand on oil and gas products. Due to the crucial importance of pipelines design, the need for optimization of pipelines layout and pipeline networks has acquired the attention of operators and practitioners. Shortest path optimization schemes of pipelines need new assessment to find the optimal solution for real-world topologies. In this paper, three optimal path planning techniques—breadth-first search (BFS), A-star (${\mathrm{A}}^{*}$), and artificial potential field (APF)—are compared and introduced to the field of choosing pipelines layout. Although these three techniques are successfully used in other applications, such as robotics and self-driving vehicles, a benchmark study is required to determine the most suitable technique out of these three techniques for pipelines placement application. In this work, a comparison between the three techniques was applied on three types of maps represented different degrees of difficulties, simple, medium, and complex, to choose the optimal technique. Also, these techniques were applied on a real-world problem considering real topologies and complex obstacles. Results of this work show the superiority of the APF algorithm with respect to finding the more realistic, shortest path and taking the lowest computational time. However, the application on real-world problems shows some limitation of this technique due to local minimum problem. The test of the ${\mathrm{A}}^{*}$ algorithm shows moderate computational time and moderate generated path with regard to length.

Practical Applications

Shortest path planning is usually related to minimizing cost of construction and operation of pipelines. In this work, the objective was to provide practitioners and field engineers with a tool to choose the shortest path for pipelines. Usually, pipelines are designed without considering the shortest path but to prevent geographical obstacles. This work used three path planning techniques, ${\mathrm{A}}^{*}$, BFS and APF algorithms, to create pipeline paths on various geographical difficulty-level maps. These techniques were appraised in other fields of research but were not compared to solve path planning problems. The results of this work are used to complete the path planning from oil and gas wells to the production and test manifolds and from manifolds to the early production facility (EPF). Further work is currently being done to obtain a software that performs this design and the cost calculations to obtain a realistic pipeline path on all type of maps.

The seismic landslide-pipe problem was investigated numerically using finite-difference code and a bounding surface soil constitutive model [Simple ANIsotropic SAND constitutive model (SANISAND)]. The SANISAND model was calibrated using triaxial monotonic and cyclic tests at the element level and shaking-table test results at the boundary value level. The results show that the calibrated parameters of the SANISAND model can predict monotonic and cyclic triaxial test results and slope displacement response properly. After the verification process, the dynamic response of a slope with the presence of buried pipes under sinusoidal input acceleration was evaluated in terms of slope displacement and the pipe axial strain. The results show that the presence of buried pipes in the slope can reduce slope surface displacement by 50%, especially for shallower burial depths of pipe (i.e., 1–1.5 m). The results of the axial strain of the pipe for changes in the burial depth and location indicate that for pipes buried in the downslope and upslope sections, deeper and shallower burial depths, respectively, lead to less axial strain being imposed on the pipe under landslide actions. The variations of slope geometric parameters (slope width and inclination angle) on slope displacement response and pipe strain patterns show that with increasing slope width and inclination angle, the displacement of sliding mass increases, and the depth of the slope failure wedge decreases. Moreover, the maximum strain of the pipe increases by 150% as the width-to-height ratio ($W/H$) of the slope increases from 1 to 4. With the increase in soil density, the pipe axial strain increases. The results of dynamic analysis under earthquake records showed that the axial strain of the pipe has a high correlation with the cumulative absolute velocity of seismic input.

This paper presents findings from an experiment examining a novel curve-shape sandwich composite made of fiber-reinforced polymer (FRP) facesheets and flexible cores under transverse compressive loading. The curve-shape sandwich composites aim to enhance strength and stiffness while minimizing material use, particularly as liner for rehabilitation of large buried infrastructure like pipes and culverts. The study involved fabricating and testing 24 circular liners with various facesheet–core combinations. Results include deflection measurements, load data, and tensile strain values at different points on the liners. The stiffness of each sandwich specimen was compared to theoretical predictions based on composite facesheet behavior. Notably, bulkermat cores demonstrated superior stiffness and strength compared to three-dimensional (3D) woven fabric cores, exhibiting higher composite action. In contrast, solid-wall liners exhibited greater deformations than sandwich liners. To predict these significant deformations, an iterative analytical model was developed, accounting for geometric nonlinearity. This model accurately predicted test data prior to any material nonlinearity, such as facesheet or core failure. Additionally, the model was used to perform a parametric analysis, exploring various liner characteristics, including diameter, FRP layers, core thickness, and liner shape.