Pipeline Corrosion Research Articles

Assessment of the influence of glycol and alcohol on the morphology and protective properties of iron carbonate in carbon dioxide corrosion of gas pipelines

The potential effect of hydrate formation inhibitors (glycols, alcohols) — components of the liquid phase of gas pipeline operating media — on carbon dioxide corrosion during the transport of aggressive gas has not been sufficiently studied. The paper presents the results of a study of the corrosive effect of glycol (alcohol) present in the liquid phase on the composition and properties of corrosion products on steel when modeling the main aggressive factors under conditions of transport of CO2-containing gas. On a corrosion stand, the aggressive conditions of alternating wetting of gas pipeline walls with water were reproduced. The fluid circulation characteristic of a partially filled pipeline can have an effect on preventing the formation or destruction of films of corrosion products (iron carbonate, siderite) on the steel surface. In places of cracking and peeling of siderite formed in the presence of CO2, conditions will be created for the formation of general and local corrosion damage. During simulation tests, monoethylene glycol and isopropanol, as well as their aqueous solutions of different concentrations, were used. For the first time, data on the formation and composition of non-stoichiometric siderite in water-glycol and water-alcohol media at CO2 partial pressures and temperature were obtained. By processing diffraction patterns obtained by X-ray diffraction, the degree of substitution of iron ions in non-stoichiometric siderites by other cations was determined. The dependence of the degree of substitution on the concentration of alcohol (glycol) in their aqueous solutions was analyzed. It was revealed that with an increase in glycol concentration, a decrease in the rate of local corrosion is observed. With a high glycol content, internal corrosion is completely suppressed in the presence of CO2. It has been established that in aqueous-alcoholic media at elevated temperatures, local carbon dioxide corrosion does not occur. This is apparently due to the uneven distribution of water and alcohol in the mixture. The results obtained can be used in assessing internal carbon dioxide corrosion in the presence of glycol (alcohol), used at gas facilities as an inhibitor of hydrate formation.

ნიადაგით გამოწვეული ელექტროქიმიური კოროზია ტენიან, ნაკლებ მჟავიან, ნეიტრალურ და ტუტოვან გარემოში

The article analyzes the anodic, transient and cathodic processes of a galvanic cell. Electrochemical corrosion caused by soil in a moist, less acidic, neutral and alkaline environment is discussed. The scheme of corrosion of underground pipelines under various conditions of soil aeration is given. The cases of corrosion process control for various soil corrosion conditions are considered: soil corrosion with prevailing cathodic control; corrosion in loose, dry soils (anodic control); corrosion over long stretches (prevailing ohmic control).

Research on Through-Flame Imaging Using Mid-Wave Infrared Camera Based on Flame Filter.

High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to the interference caused by flames generated from fuel combustion. To address this issue, in this study, a through-flame infrared imager is developed based on the mid-wave infrared (MWIR) radiation characteristics of the flame. The imager incorporates a narrowband filter that operates within the wavelength range of 3.80 μm to 4.05 μm, which is integrated into conventional thermal imagers to perform flame filtering. This configuration enables the radiation from the background to pass through the flame and reach the detector, thereby allowing the infrared imager to visualize objects obscured by the flame and measure their temperatures directly. Our experimental findings indicate that the imager is capable of through-flame imaging; specifically, when the temperature of the target exceeds 50 °C, the imager can effectively penetrate the outer flame of an alcohol lamp and distinctly capture the target's outline. Importantly, as the temperature of the target increases, the clarity of the target's contour in the images improves. The MWIR through-flame imager presents considerable potential for the real-time monitoring and preventive maintenance of high-temperature furnaces and similar equipment, such as detecting the degradation of refractory materials and damage to pipelines.

The Microbiologically Influenced Corrosion and Protection of Pipelines: A Detailed Review

Microbial corrosion is the deterioration of materials associated with microorganisms in environments, especially in oil- and gas-dominated sectors. It has been widely reported to cause great losses to industrial facilities such as drainage systems, sewage structures, food-processing equipment, and oil and gas facilities. Generally, bacteria, viruses, and other microorganisms are the most important microorganisms associated with microbial corrosion. The destructive nature of these microorganisms differs based on the kind of bacteria involved in the corrosion mechanism. Amongst the microorganisms related to microbial corrosion, sulfate-reducing bacteria (SRB) is reported to be the most common harmful bacteria. The detailed mechanistic explanations relating to the corrosion of pipelines by sulfate-reducing bacteria are discussed. The mechanism of microbial corrosion in pipelines showing the formation of pitting corrosion and cathodic depolarization is also reported. The current review provides theoretical information for the control and protection of pipelines caused by microbial corrosion and how new eco-friendly protection methods could be explored.

Internal inspection method for crack defects in ferromagnetic pipelines under remanent magnetization

Magnetic flux leakage (MFL) testing is effective in detecting corrosion in ferromagnetic pipelines. However, the MFL generated at the crack is highly weak and is easily drowned in the strong background magnetic field and difficult to detect. Therefore, this paper creatively proposes a remanent magnetic flux leakage (RMFL) detection method. This method utilizes the hysteresis state of the pipeline after MFL detection and identifies crack defects by collecting the remanent magnetic flux density near the inner wall surface of the pipeline. The remanent magnetic field distribution near the pipeline surface is examined at different crack sizes, magnetization speeds, and lift-off values via theoretical analysis, finite element simulation, and experimental verification. In addition, experiments have demonstrated that in terms of crack detection capability, RMFL is superior to MFL. Therefore, adding a RMFL measurement module to the conventional MFL detector has important practical value. It can realize the targeted collaborative application of these two technologies, namely the synchronous detection of corrosion and crack defects, which greatly improves the detection coverage and detection efficiency.

Anions exacerbate microbial corrosion of X80 pipeline steel induced by sulfate-reducing bacteria in the sea mud environment

Sulfate-reducing bacteria (SRB) present in sea mud pose a challenge to the safe operation of subsea pipelines. Chlorides and sulfates in sea mud promote the microbial corrosion of subsea pipelines by SRB. This study investigates the effects of sulfate and chloride on the microbial corrosion of X80 pipeline steel in the sea mud environment. The results indicate that sulfate serves as an electron acceptor, promoting the growth of SRB, while chloride regulates the osmotic pressure of SRB cells, affecting their bioactivity. SRB exhibit more aggressive corrosion behavior in sea mud with high concentrations of both chloride and sulfate.

Corrosion prediction model for long-distance pipelines based on NLFE-NGO-ELM

Abstract To improve the accuracy of buried pipeline corrosion rate prediction, we developed a Northern Goshawk Optimization-based Extreme Learning Machine (NLFE-NGO-ELM) model enhanced by Non-linear Feature Expansion. Feature selection was used to expand the small dataset and reduce the dimensionality of factors affecting pipeline corrosion. The Northern Goshawk algorithm was used, and 106 sets of pipeline external corrosion detection data were simulated in MATLAB as a case study. The ELM optimizer was analyzed using Grey Wolf Optimization (GWO-ELM), Northern Goshawk Optimization (NGO-ELM), and BP optimization as comparison models. Using NLFE-NGO-ELM significantly improved the model's prediction accuracy and speed. The NGO-ELM model was applied to predict the corrosion rates of 21 pipelines, achieving an absolute error of only 2.40% and an R value of 0.97255, surpassing the predictive performance of NGO-BP and GWO-ELM models. These results indicate that the current model exhibits superior learning performance and stronger fitting capabilities.

Multi-physics coupled simulation and experimental investigation of alternating stray current corrosion of buried gas pipeline adjacent to rail transit system

As the gradual emergence of alternating current (AC) electrified rail transit system in urban areas, buried gas pipeline adjacent to the system will be seriously corroded by induced alternating stray current. These buried gas pipelines are at serious risk of electrochemical corrosion, which leads to safety and environmental threaten. In order to study the distribution of alternating stray current corrosion on pipeline surface on a larger spatial scale, this paper conducted numerical simulation and experimental validation of alternating stray current corrosion of buried gas pipeline. In the numerical simulation model, coupling between different physical fields are realized through the relationship between current density of pipe-to-soil interface, electrolyte, and electrodes. Proposed numerical method based on coupled multi-physics in this paper are in good agreement with experimental results under different influencing factors. A novel evaluation index was proposed to assess the corrosion risk within different zones on the pipeline surface. Results show that corrosion distribution is greatly influenced by spatial interaction between buried pipeline and rail transit system including crossing angle and parallel distance. Besides, alternating stray current corrosion on buried gas pipeline are proved to be both affected by dynamic characteristics due to AC fluctuation and operation mode of locomotive.

Water Separation and Formation of Cells with Differential Aeration as Factors Controlling Corrosion of Steel Pipelines in a Crude Oil Storage Facility

The mechanism causing the dramatic intensification of the corrosion deterioration of carbon steel pipes in a crude oil storage facility has been investigated. This study considers a number of factors affecting corrosion in crude oil, such as the water content, the corrosivity of the aqueous phase, the kinetics of water–oil separation, the effect of dissolved oxygen, the effect of the crude oil quality, the degree of stagnancy inside of the pipes, the possible contribution of microbially induced corrosion (MIC) and the presence of deposits. The key root of the corrosion intensification was the separation of the water phase, supported by stagnancy, which eventually led to the formation of stable shallow pits surrounded by cathodic areas.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Effect of flavin adenine dinucleotide (FAD) on Desulfovibrio desulfuricans corrosion of pipeline welded joint

The impact of Flavin adenine dinucleotide (FAD) on sulfate-reducing bacteria (SRB) corrosion of a pipeline welded joint (WJ) was investigated under anaerobic condition in this paper. The results showed that the thickness of the corrosion product on heat affected zone (HAZ) was lower than that on base metal (BM) and welded zone (WZ), and the FAD addition enhanced the development of the protruding microbial tubercles on the WJ. The local corrosion degrees of the BM and WZ coupons were significantly higher than that of the HAZ coupon. Besides, the FAD addition simultaneously promoted local corrosion of all three zones of the WJ in the SRB inoculated environment, and the promotion role was much more pronounced on the WZ coupons. The selective promotion effect of FAD on SRB corrosion in the WJ was attributed to the special structure of the WZ, the selected SRB attachment and the FAD/FADH2 redox feedback cycle.

Enhanced prediction of corrosion rates of pipeline steels using simulated annealing-optimized ANFIS models

Accurate prediction of corrosion rates is crucial for preventing infrastructure failures, reducing maintenance costs, and ensuring operational safety. Traditional models often struggle to account for the complex, non-linear interactions between environmental factors and material properties. This study presents a novel approach integrating Simulated Annealing (SA) with an Adaptive Network-based Fuzzy Inference System (ANFIS) to improve corrosion rate predictions for pipeline steels. The SA-ANFIS model features six input neurons representing temperature, H₂S pressure, CO₂ pressure, salinity, moisture content, and material type. These factors influence corrosion rates, represented by a single output neuron. The SA algorithm optimizes the ANFIS model's parameters, enhancing its ability to handle non-linear relationships. Historical corrosion data for P110SS, L80, and 2205 Duplex steel were used, incorporating environmental variables such as temperature, pH, and gas pressures. The SA-ANFIS model achieved superior accuracy, with a maximum error of 2.8424 % and an average error of 1.2536 %, outperforming the GA-ANFIS model and conventional ANFIS and SVR models. The SA-ANFIS model offers a robust, optimized tool for predicting corrosion in petroleum pipelines, significantly improving prediction accuracy under harsh conditions.

Synergistic effects of mixed microorganisms on the corrosion of X65 carbon steel in actual reinjection water

Complex microbiological interactions in reinjection water can lead to severe corrosion of pipelines. In this study, X65 specimen was used for corrosion simulation experiments to explore the microbiologically influenced corrosion (MIC) and mechanism in actual oilfield reinjection water. The results of scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) showed that the X65 carbon steel was severely corroded by microorganisms, with the maximum localized corrosion pit depth of 15.04 μm after 28 days of immersion. Electrochemical analysis and weight loss measurement further confirmed that the mixed microorganisms caused serious corrosion of X65 carbon steel, and the maximum corrosion rate was 0.249 mm/y. Furthermore, microbial community structure determination showed that mixed microorganisms in the actual reinjection water formed a corrosive biofilm on the surface of X65 steel, inducing severe pitting corrosion, which was mainly attributed to the synergistic effect of Pseudomonas, sulfur-oxidizing bacteria (SOB), sulfate-reducing bacteria (SRB) and methanogenic archaea. The results of this study have important guiding significance for the identification of MIC process for actual water samples of the oilfield and the timely adjustment of MIC control measures.

Research of the anti-static and corrosion resistance of S-ZnO/m-SWCNTs/ FC composite coating

Abstract In order to solve the hazards caused by corrosion and charge accumulation in aircraft, electronic equipment, oil and gas pipelines, the corrosion resistance and anti-static performance of coatings need to be noted. In this study, monodisperse single-walled carbon nanotubes functional slurry (m-SWCNTs) was obtained by the combination of high-energy ultrasonic dispersion and non-covalent amphiphilic dispersant (PVP). In this way, the uniform dispersion of low-content m-SWCNTs in fluorocarbon composite coatings was achieved, and the micronano structured surface of the fluorocarbon composite coating was constructed by using the modified-ZnO in order to achieve superhydrophobicity. The results showed that the corrosion resistance of the fluorocarbon composite coating was further improved and the antistatic property of the composite coating met the use requirements, which was achieved through the synergistic effect of the m-SWCNTs network and the modified-ZnO. The resistivity of S-ZnO/m-SWCNTs/FC coating is 3.15 Ωm, acting as anti-static coating to reduce safety hazards, its corrosion current is 9.66×10−8 A/cm2, which is lower than bare steel by 3 orders of magnitude. Meanwhile, the composite coating owns the self-cleaning, superhydrophobic and organic solvent affinity, acting as oil-water separation coating. This research provides a new idea for the fabrication of multifunctional coating, elucidating the significant potential of its applications.

The identification and analysis of pivotal factors influencing the corrosion of natural gas pipelines using fuzzy cognitive map

Corrosion presents the gas industry with the formidable challenge of sustaining natural gas energy resources. Given the intricate nature of corrosion, it is imperative to devise a comprehensive model and formulate cognitive sets to effectively manage the risks associated with corrosion in the gas industry. Therefore, the present research was conducted with the objective of identifying and presenting a fuzzy cognitive map delineating the critical factors of corrosion. The statistical population of the research comprises corrosion experts. The statistical sample for this study encompasses 30 experts possessing requisite knowledge and experience, with a minimum of 10 years of expertise in engineering disciplines such as materials science and metallurgy. The findings revealed that the cathodic protection factor exerts the most significant influence on corrosion. The fuzzy cognitive mapping structure of the study comprises 19 edges. The factors “CO2″, ”H2S“, and ”Pipeline Age“ exhibit the highest susceptibility with input degrees of 0.895 and 0.6, respectively. Additionally, ”Cathodic Protection“ ranks highest in terms of influence with a degree of 0.895, whereas ”Working Temperature“ has the least impact with an output degree of − 0.895. The reliability of expert opinions using the Z-number theory has been assessed, yielding fuzzy numbers (0.47, 0.7, 0.93), indicating a reliable and highly certain relationship. The identified factors furnish essential insights to bolster evidence-based policy formulation, facilitating effective monitoring and evaluation of policies within the gas industry.

Probabilistic assessment of complex corrosion in pipelines considering River-Bottom Profile information

One of the important causes of failure and incidents in pipelines is corrosion. Due to the severe consequences and impact in several social, economic, and environmental areas, pipelines must be regularly inspected. The possibilities for pipeline assessment are standard equations and numerical simulations. While the first is simple and fast, it tends to be conservative and the second is expensive. The finite element method (FEM) has been successfully used to predict the failure pressure of pipelines under different conditions. Commonly assessment methods overlook the uncertainties for failure predictions, often relying on safety factors. However, it is imperative to consider uncertainties to ascertain the reliability and risk of corroded pipes. While the reliability analysis of pipelines with idealized defects has been extensively explored in the literature, studies considering complex corrosion profiles, are still demanding. This paper addresses this gap considering the traditional Monte Carlo (MC) and first-order reliability (FORM) methods. The assessment is based on the Effective Area Method (EAM) within the failure function. As well know, MC can be expensive due to the high number of function evaluations. To mitigate this, FORM is employed as it requires fewer function evaluations. However, function gradients are required as it relies on an iterative optimization algorithm. Particularly challenges arise when computing the gradient using EAM for burst pressure estimation. The EAM is an iterative procedure, and small perturbations can yield incorrect sensitivity values, causing problems for FORM convergence. To address this, we propose a novel procedure for efficiently and less conservatively obtaining the gradients required by FORM using EAM as a failure function. The modified FORM method achieved convergence within 15 iterations for all test cases. This alternative approach offers a faster, more precise and less conservative method for Level-2 probabilistic failure analysis of complex-shaped corrosion defects compared to traditional Level-1 methods.

Assessing the reliability of natural gas pipeline system in the presence of corrosion using fuzzy fault tree

The corrosion encountered within natural gas pipelines can precipitate significant ramifications. Numerous internal and external factors contribute to incidents of gas leakage. A multitude of factors influencing pipeline failure underscore the imperative need for a safety system intricately linked to the operation process. The absence of this systematic process for detecting factors contributing to failure detrimentally impacts the efficiency of pipelines. In this context, this paper has devised a risk assessment and warning model utilizing multi-factor monitoring data, employing the fuzzy fault tree analysis (FFTA). The fuzzy fault tree is constructed to assess the impact of individual factors and parameters influencing corrosion on the reliability of the grid. Moreover, a performance diagram illustrating this mechanism is crafted, elucidating the system's reliability relationship based on each component's reliability. The findings showed that 14 basic and 10 intermediate events play a role in the corrosion of natural gas pipelines. The failure probability of the top event was identified to be 22%. Employing the proposed method, the system's reliability was estimated with lower and upper limits of [0.6825, 0.8859]. The obtained results demonstrate the effectiveness and efficiency of the proposed method in addressing the uncertainty inherent in the input data.

Identification of defects on the inner wall of water supply pipes based on the optimized residual network

ABSTRACT The urban water supply network is part of the infrastructure that sustains the economic and social functions of cities and regions. Timely inspection and maintenance of the network can effectively reduce resource wastage and prevent accidents. Traditional manual detection methods are inefficient and can be based on subjective judgments. A classification model based on an improved ResNet34 network has been proposed to classify and detect various types of corrosion on the inner walls of pipes under challenging conditions. The introduction of the attention mechanism and multi-scale feature fusion modules further improved the model's effectiveness in classifying defects within the inner walls of pipes. The model can detect various types of pipeline corrosion with a detection accuracy of 98.61%. This accuracy is significantly superior to that achieved with traditional models such as ResNet34, AlexNet, MobileNet, and VGGNet.

Towards proactive corrosion management: A predictive modeling approach in pipeline industrial applications

Corrosion is the most significant factor affecting pipeline integrity management. It may lead to facility damage, production interruption, environmental pollution and even explosion. Corrosion management developed in recent years is a critical means to ensure the safety and reliability of pipeline systems. However, developing proactive corrosion management is difficult due to limited availability of data on target pipelines and corrosion factors. The motivation of this study is to develop a proactive corrosion management approach based on a predictive framework. The proposed framework integrates multiple corrosion factors, diverse working conditions, multi-phase degradation, and probabilistic analysis to offer an efficient analysis tool. A numerical analysis of corrosion prediction is conducted using the corrosion data of the training pipeline and the target pipeline, and the corrosion rate, corrosion depth, and remaining useful life (RUL) of the target pipeline are evaluated. Based on the RUL information, the maintenance time of the pipeline is further analyzed to maximize the benefits of maintenance. This study offers a comprehensive approach to pipeline corrosion prediction, facilitating proactive maintenance strategies and improving asset management in the pipeline industry.

Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach

Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach