Journal of Pipeline Science and Engineering最新文献_第6页

Calculation for critical gas velocity of liquid accumulation in inclined pipelines: A method based on physics-informed neural network 倾斜管道积液临界气速计算：一种基于物理信息神经网络的方法

IF 4.9 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.jpse.2025.100257

Xinru Zhang , Lei Hou , Xin Wang , Jiaquan Liu , Zuoliang Zhu

Liquid accumulation in gas pipelines will reduce transportation efficiency, increase corrosion rates, and induce severe slug flow. Calculation for critical gas velocity v_cg of liquid accumulation in inclined pipelines is important for the prevention of liquid accumulation. Due to the complexity of multiphase flow, the mechanism of liquid accumulation is still controversial. Many models have been proposed based on different liquid accumulation theories, but most of these models are complex and inaccurate. It is difficult to compare the calculation results of different theories in a unified standard. To simplify the calculation and improve the accuracy, a new physics-informed neural network (PINN) for calculating v_cg is proposed. PINN is trained only by the physical constraints of gas-liquid two-phase flow (GLF) and does not require any training data. In the same computational framework, PINN can calculate the v_cg corresponding to minimum pressure gradient (MPG), minimum gas-liquid interface shear stress (MIS), and zero liquid-wall shear stress (ZLS), respectively. In addition, the same two empirical equations are introduced for each calculation procedure, which ensures objectivity in the evaluation of different liquid accumulation theories. With 89 collected public experimental data, PINN is compared with 3 models based on different theories, and the changing law of v_cg are analyzed. The results show that PINN is applicable to a range of operating conditions with liquid superficial velocity from 0.001 to 0.100 m/s, pipe inclination from 2° to 20°, and pipe diameters from 50 to 200 mm. PINN are better than other models, and different theories have different sensitivities to each factor. This study provides a new computational method for the research of GLF and provides guidance for the prevention of liquid accumulation in gas pipelines.

天然气管道中的液体积聚会降低输送效率，增加腐蚀速率，并诱发严重的段塞流。倾斜管道积液临界气速vcg的计算对于防止积液具有重要意义。由于多相流的复杂性，液体积聚的机理仍存在争议。基于不同的液体积累理论，人们提出了许多模型，但这些模型大多复杂且不准确。用一个统一的标准比较不同理论的计算结果是困难的。为了简化计算，提高计算精度，提出了一种新的物理信息神经网络（PINN）来计算vcg。PINN仅受气液两相流（GLF）的物理约束进行训练，不需要任何训练数据。在相同的计算框架下，PINN可以分别计算最小压力梯度（MPG）、最小气液界面剪应力（MIS）和零液壁剪应力（ZLS）对应的vcg。此外，每个计算过程都引入了相同的两个经验方程，保证了不同液体积累理论评价的客观性。利用收集到的89个公开实验数据，将PINN与基于不同理论的3种模型进行了比较，分析了vcg的变化规律。结果表明，该方法适用于液体表面流速为0.001 ~ 0.100 m/s、管道倾角为2°~ 20°、管径为50 ~ 200mm的工况范围。PINN比其他模型更好，不同的理论对每个因素的敏感性不同。该研究为GLF的研究提供了一种新的计算方法，为防止天然气管道积液提供了指导。

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引用次数: 0

A review of computer vision applications for asset inspection in the oil and gas industry 计算机视觉在油气行业资产检测中的应用综述

IF 4.9 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.jpse.2024.100246

Edmundo Casas , Leo Thomas Ramos , Cristian Romero , Francklin Rivas-Echeverría

This review explores the current application of computer vision (CV) technologies in the inspection of pipelines within the oil and gas industry, highlighting the methodologies, challenges, and advancements in this critical area. Through a systematic analysis of key articles, our study emphasizes CV’s role in addressing crucial issues such as corrosion, leaks, oil spills, and mechanical damage, areas identified as critical through our literature review. Predominant CV techniques like object detection and image segmentation, particularly using advanced frameworks like You Only Look Once (YOLO), Mask Region-based Convolutional Neural Network (R-CNN), and U-Net, showcase the field’s robust response to asset inspection challenges. Additionally, our findings reveal a significant reliance on in-house or directly acquired datasets, primarily through RGB and thermal imaging or increasingly via internet and satellite resources, underscoring the urgent need for standardized, accessible datasets to advance CV research. Despite these advancements, a gap in real-world testing remains, indicating a pressing need for field validation to ensure the operational viability of CV applications in asset inspection. In conclusion, this study reaffirms the transformative potential of CV technologies in enhancing asset integrity and operational safety across the oil and gas industry. However, the findings also highlight critical challenges, such as the scarcity of standardized datasets and the need for more comprehensive field testing. Looking ahead, future research should focus on expanding the application of CV, fostering collaborative dataset development, and ensuring that these technologies can bridge the gap between theoretical research and practical implementation, ultimately contributing to more reliable and efficient asset inspection.

本文探讨了当前计算机视觉（CV）技术在油气行业管道检测中的应用，重点介绍了这一关键领域的方法、挑战和进展。通过对关键文章的系统分析，我们的研究强调了CV在解决腐蚀、泄漏、漏油和机械损伤等关键问题方面的作用，这些领域在我们的文献综述中被确定为关键领域。主要的CV技术，如目标检测和图像分割，特别是使用先进的框架，如You Only Look Once （YOLO）、基于掩模区域的卷积神经网络（R-CNN）和U-Net，展示了该领域对资产检查挑战的强大响应。此外，我们的研究结果揭示了对内部或直接获取的数据集的严重依赖，主要是通过RGB和热成像，或者越来越多地通过互联网和卫星资源，强调了对标准化、可访问的数据集的迫切需要，以推进CV研究。尽管取得了这些进步，但在实际测试中仍然存在差距，这表明迫切需要进行现场验证，以确保CV应用在资产检查中的操作可行性。总之，这项研究重申了CV技术在提高油气行业资产完整性和操作安全性方面的变革潜力。然而，这些发现也强调了一些关键的挑战，例如标准化数据集的稀缺以及需要更全面的实地测试。展望未来，未来的研究应侧重于扩大CV的应用，促进协同数据集开发，并确保这些技术能够弥合理论研究和实际实施之间的差距，最终为更可靠和高效的资产检查做出贡献。

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引用次数: 0

An algorithm for third-party intrusion action detection in oil and gas pipelines based on fine-grained feature enhancement 基于细粒度特征增强的油气管道第三方入侵行为检测算法

IF 4.9 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.jpse.2025.100261

Shaocan Dong , Yuxing Li , Qihui Hu , Wuchang Wang , Ruijia Zhang , Yundong Yuan , Chengming An

The extensive distribution of oil and gas pipeline networks across China results in frequent third-party intrusions in high-consequence areas, significantly increasing the risk of pipeline failures and posing serious threats to pipeline safety. Current detection methods mainly rely on manual inspections and video surveillance. However, traditional manual inspections suffer from high workloads, low efficiency, poor effectiveness, and considerable safety risks. Existing video surveillance technologies can only identify abnormal objects within pipeline protection zones, failing to recognize abnormal behaviors effectively. These limitations lead to high false alarm rates and poor recognition capabilities. To address these issues, this study designs a multi-scale network feature extraction structure based on the SlowFast algorithm framework. The design captures fine-grained features of small targets across various scales in complex oil and gas pipeline scenes. The proposed approach enhances spatiotemporal representation by leveraging features across different temporal scales. Corresponding feature fusion methods are also designed for these improvements to develop a third-party intrusion abnormal action recognition technology. This enhances the ability to identify third-party intrusions in oil and gas pipelines and provides support for the intelligent development of pipeline infrastructure.

中国油气管网分布广泛，导致第三方入侵频繁发生在高影响区域，大大增加了管道故障的风险，对管道安全构成严重威胁。目前的检测方法主要依靠人工检测和视频监控。但传统的人工巡检工作量大、效率低、效果差，且存在较大的安全隐患。现有的视频监控技术只能识别管道保护区内的异常物体，无法有效识别异常行为。这些限制导致了高虚警率和较差的识别能力。针对这些问题，本研究设计了一种基于SlowFast算法框架的多尺度网络特征提取结构。该设计可捕获复杂油气管道场景中各种尺度小目标的细粒度特征。该方法通过利用不同时间尺度的特征来增强时空表征。针对这些改进设计了相应的特征融合方法，开发了第三方入侵异常动作识别技术。这增强了识别油气管道中第三方入侵的能力，并为管道基础设施的智能化发展提供了支持。

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引用次数: 0

CRA clad pipes: Do their benefits justify sole selection? CRA包覆管道：它们的优点是否证明了选择单一管道的合理性？

IF 4.9 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.jpse.2024.100245

Ahmed Reda , Mohammad Zam Zam Noor , Ali Karrech

This article presents a case study of a gas leak at a girth weld on a subsea spool. The leak occurred 14 days after the pipeline’s start-up. The pipeline in question was a 36-inch corrosion-resistant alloy (CRA) clad pipeline, with a carrier pipe made of X65 steel and a 3 mm layer of alloy 825. The girth weld was performed using alloy 625. The leak occurred at the girth weld between the riser and the tie-in subsea spool. The failure was attributed to excessive misalignment, which was unavoidable and evident in radiographic testing (RT). To pass the girth weld in terms of quality control, the welder ground the area around the root where the highest misalignment was located, inadvertently grinding through the CRA clad layer and exposing the carbon steel directly to sour gas. The pipeline had been installed and left in a preservation mode using treated inhibited seawater for several years. It was later dewatered, conditioned, and purged before introducing the wet sour gas. Fourteen days after the start-up, the pipeline ruptured, as discussed in this article.

本文介绍了一个海底阀芯环焊缝气体泄漏的案例研究。泄漏发生在管道启动14天后。该管道是一条36英寸的耐腐蚀合金（CRA）包覆管道，其载体管由X65钢制成，并有一层3毫米的825合金层。围焊采用625合金。泄漏发生在隔水管和连接的海底阀芯之间的环焊缝处。失败的原因是过度的不对准，这在放射检查（RT）中是不可避免的和明显的。为了在质量控制方面通过环焊缝，焊工在焊缝根部附近最错位的地方进行研磨，无意中磨穿了CRA包覆层，使碳钢直接暴露在酸性气体中。这条管道已经安装好，并使用经过处理的抑制海水保存了好几年。然后在引入湿酸气之前对其进行脱水、调节和净化。启动14天后，管道破裂，如本文所述。

引用次数: 0

A mechanistic model for flow accelerated corrosion prediction of a 90° carbon steel elbow in CO2 environments 90°碳钢弯头在CO2环境中流动加速腐蚀的机理模型

IF 4.9 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.jpse.2025.100258

Dongrun Li , Zheng Fang , Tai Ma , Zengqiang Li , Jun Shi , Li Zeng , Hanxin Chen

A mechanistic model for the prediction of CO₂ flow accelerated corrosion (FAC) at a 90° carbon steel elbow is developed. Homogenous chemical reactions, electrochemical reactions at the metal/solution interface and mass transfer of corrosion species are covered in present proposed model. The distribution of corrosion rate at different positions of a three-dimensional 90°elbow in single-phase CO₂-contained oilfield formation water, which are consistent with flow field, are numerically modeled. The numerical prediction of corrosion rate distribution at the elbow exhibits good accordance with experimental data. The developed model provides theoretical foundation for the understanding of FAC mechanisms and corrosion prevention, thus guaranteeing security and reliability levels during oil/gas production and transportation.

建立了90°碳钢弯头CO2流动加速腐蚀（FAC）机理预测模型。该模型涵盖了均相化学反应、金属/溶液界面的电化学反应和腐蚀物质的传质。对单相含co2油田地层水中三维90°弯头不同位置的腐蚀速率分布进行了数值模拟，结果与流场一致。弯管腐蚀速率分布的数值预测与实验数据吻合较好。所建立的模型为理解FAC机理和防腐提供了理论基础，从而保证了油气生产和运输过程中的安全性和可靠性水平。

引用次数: 0

Experimental study on overpressure and flame characteristics of hydrogen-methane mixture 氢-甲烷混合物超压及火焰特性实验研究

IF 4.9 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.jpse.2024.100242

Weibin Wang

In order to evaluate the consequences of hydrogen-methane mixture explosion accidents in open space and confined space, large-scale (8 m³) open space and confined space (55 m³) hydrogen-methane mixture explosion experiments were carried out to study the effects of hydrogen-doped ratio and equivalent ratio on hydrogen-methane mixture explosion characteristics. In the open space experiments, within the equivalence ratio range of 0.9 to 1.3, the overpressure measured at an equivalence ratio of 1.1 is the highest. When the equivalent ratio is 1.1, the higher the hydrogen blending ratio, the higher the overpressure peak and the farther the action distance. The overall overpressure increased significantly when the hydrogen blending ratio increased from 20% to 30%. When the hydrogen ratio is 30%, the flame propagation speed is as high as 20 m/s, which is about twice the flame propagation peak speed when the hydrogen ratio is 10%–20%. Therefore, from the angle of overpressure and flame development, the step point of overpressure harm can be obtained by a hydrogen mixing ratio of 20%. The overall trend of external overpressure change in confined and open spaces is consistent; both decrease with the increase in distance. However, the external overpressure in confined space is more significant than in open space, and the flame propagation speed is much higher than in open space. Compared with open space, the harm range of confined space is more extensive. This study provides an experimental basis for the safety assessment of hydrogen-methane mixture and the formulation of safety protection measures.

为了评价开放空间和密闭空间氢气-甲烷混合爆炸事故的后果，开展了大型（8 m3）开放空间和密闭空间（55 m3）氢气-甲烷混合爆炸实验，研究掺氢比和当量比对氢气-甲烷混合爆炸特性的影响。在开放空间实验中，在等效比为0.9 ~ 1.3范围内，当等效比为1.1时测得的超压最高。当当量比为1.1时，掺氢比越大，超压峰值越高，作用距离越远。当掺氢比从20%增加到30%时，总超压显著增加。氢气比为30%时，火焰传播速度高达20 m/s，约为氢气比为10% ~ 20%时火焰传播峰值速度的2倍。因此，从超压和火焰发展的角度来看，当掺氢比为20%时，可以得到超压危害的阶跃点。封闭空间和开放空间外超压变化总体趋势一致；两者都随着距离的增加而减小。但密闭空间的外超压比开放空间更为显著，火焰传播速度也远高于开放空间。与开放空间相比，密闭空间的危害范围更为广泛。本研究为氢甲烷混合物的安全性评价和安全防护措施的制定提供了实验依据。

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引用次数: 0

Mechanical behavior of pipeline steel after strain aging – Part I: Experiment 管道钢应变时效后的力学性能。第1部分：试验

IF 4.9 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-09-01 DOI: 10.1016/j.jpse.2025.100259

Radmir E. Mukhamejanov , Igor Yu. Pyshmintsev , Vladimir M. Khatkevich , Alexander A. Frantsuzov , Ivan V. Sergeichev

Low plastic straining combined with aging leads to critical changes in the mechanical properties of steels used in submarine pipelines constructed using the pipe reeling method. This includes alterations in the mechanical behavior of steel at the initial stages of plastic strain, which can be critical for the long-term operation of pipelines due to localized plastic deformation in the form of buckling. In this study, the effects of plastic preliminary strain of 3% through monotonic tension and aging at 250 °C for 1 h on the mechanical behavior of low-carbon steel in various microstructural states were investigated. These states differed in terms of the volumetric fraction of structurally free ferrite after quenching or normalizing. It was shown that the overall yield strength increase, as well as the contributions of strain hardening and bake hardening, depend on the volumetric fraction of ferrite and the heat treatment method. A direct proportional relationship between uniform elongation and the strain hardening exponent, both before and after strain aging, was confirmed. The results have been used for numerical modeling of the plastic strain of steel, aiming to predict and prevent the buckling of deep-water pipelines, as presented in Part II of the article.

低塑性应变和老化导致钢管在卷绕法海底管道中的力学性能发生了重大变化。这包括钢在塑性应变初始阶段的力学行为变化，由于屈曲形式的局部塑性变形，这对于管道的长期运行至关重要。在本研究中，通过单调拉伸和250℃时效1 h，研究了塑性初应变为3%对低碳钢在不同组织状态下力学行为的影响。这些状态在淬火或正火后结构自由铁素体的体积分数方面有所不同。结果表明，铁素体的体积分数和热处理方式对整体屈服强度的提高以及应变硬化和烘烤硬化的贡献有较大的影响。结果表明，在应变时效前后，均匀伸长率与应变硬化指数成正比关系。研究结果已用于钢塑性应变的数值模拟，旨在预测和防止深水管道的屈曲，如文章的第二部分所述。

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引用次数: 0

A multi-level classification model for corrosion defects in oil and gas pipelines using meta-learner ensemble (MLE) techniques 基于元学习器集成（MLE）技术的油气管道腐蚀缺陷多级分类模型

IF 4.8 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-06-01 DOI: 10.1016/j.jpse.2024.100244

Adamu Abubakar Sani , Mohamed Mubarak Abdul Wahab , Nasir Shafiq , Kamaludden Usman Danyaro , Nasir Khan , Adamu Tafida , Arsalaan Khan Yousafzai

Maintaining the integrity of oil and gas pipelines is necessary for the efficient and safe transport of hydrocarbons. Corrosion defects can lead to decreased operational efficiency, leaks, a reduction in operational efficiency, and even catastrophic pipeline failures. Machine learning techniques are useful in detecting corrosion defects, ensemble methods that combine multiple classifiers offer better predictive accuracy. The aim of this work is to develop multi-level classification model an efficient ensemble technique capable of predicting the level of corrosion defects and addressing class imbalances in oil and gas pipeline data. The study uses a two-level stacking ensemble learning method that enhances corrosion defect prediction called the meta-learner ensemble (MLE). The model classifies corrosion defects into three categories: high, medium, and low. Prediction accuracy was improved by using a stacking classifier that combines multiple basic classifiers with a logistic regression meta-learner. Findings show that most corrosion defects fall within the low-risk category, with a significant number falling into the medium-to-high-risk range, highlighting the necessity for targeted maintenance. Considering the challenges of dataset imbalance, the stacking classifier achieved 94% accuracy, showing balanced performance across all risk categories. The stacking model outperformed the random forest and logistic regression models in terms of F1-scores, precision, and recall. This study demonstrates the application of stacking ensemble techniques in predicting corrosion risks and optimizing pipeline maintenance strategies. It provides vital information for improving pipeline safety and optimizing predictive maintenance practices by providing an in-depth assessment of various machine learning models, especially when real-time monitoring systems are integrated.

保持油气管道的完整性对于碳氢化合物的高效和安全运输是必要的。腐蚀缺陷会导致作业效率下降、泄漏、作业效率降低，甚至导致灾难性的管道故障。机器学习技术在检测腐蚀缺陷方面很有用，组合多个分类器的集成方法提供了更好的预测准确性。这项工作的目的是开发多级分类模型，一种有效的集成技术，能够预测腐蚀缺陷的水平，并解决石油和天然气管道数据中的类别不平衡问题。该研究使用了一种叫做元学习集成（MLE）的两级堆叠集成学习方法来增强腐蚀缺陷预测。该模型将腐蚀缺陷分为高、中、低三类。通过将多个基本分类器与逻辑回归元学习器相结合的堆叠分类器提高预测精度。研究结果表明，大多数腐蚀缺陷属于低风险类别，有相当数量的腐蚀缺陷属于中高风险范围，这突出了有针对性维护的必要性。考虑到数据不平衡的挑战，叠加分类器达到了94%的准确率，在所有风险类别中表现出平衡的性能。在f1得分、精度和召回率方面，叠加模型优于随机森林和逻辑回归模型。本研究展示了堆叠集成技术在预测管道腐蚀风险和优化管道维护策略中的应用。通过对各种机器学习模型进行深入评估，特别是在集成实时监控系统时，它为提高管道安全性和优化预测性维护实践提供了重要信息。

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引用次数: 0

A burst capacity model for pipelines containing pinhole-in-corrosion defects 含腐蚀针孔缺陷管道的突发容量模型

IF 4.8 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-06-01 DOI: 10.1016/j.jpse.2024.100243

Y. Shen, W. Zhou

This study develops a practical burst capacity model for oil and gas pipelines containing pinhole-in-corrosion (PIC) defects based on results of extensive parametric three-dimensional finite element analyses (FEA) that are validated by full-scale burst tests of corroded pipe specimens reported in the literature. The PIC defect considered in this study contains a cylindrical-shaped pinhole located inside a cuboidal-shaped general corrosion. The proposed model takes the form of the well-known PCORRC model for the burst capacity of the general corrosion plus a correction term taking into account the impact of the pinhole, whereby the correction term is evaluated as a function of the depth and length of the general corrosion as well as the depth and relative position of the pinhole using the Gaussian process regression based on the results of parametric FEA. The accuracy of the proposed PIC model is demonstrated using 16 FEA cases and shown to be higher than that of the well-known RSTRENG model and a burst capacity model for complex-shaped corrosion defects reported in the recent literature.

本研究基于广泛的参数三维有限元分析（FEA）结果，开发了一种适用于含腐蚀针孔（PIC）缺陷的油气管道的爆裂能力模型，并通过文献中报道的腐蚀管道样品的全尺寸爆破试验进行了验证。在本研究中考虑的PIC缺陷包含一个圆柱形针孔位于一个立方体形状的一般腐蚀。该模型采用PCORRC一般腐蚀爆发能力模型的形式加上考虑针孔影响的修正项，在参数化有限元分析结果的基础上，利用高斯过程回归将修正项作为一般腐蚀深度和长度以及针孔深度和相对位置的函数进行评估。通过16个FEA案例证明了所提出的PIC模型的准确性，并表明其高于最近文献中报道的著名的RSTRENG模型和复杂形状腐蚀缺陷的突发容量模型。

引用次数: 0

Inhibition and co-condensation behaviour of 2-mercaptoethanol in top-of-line CO2 corrosion environments 2-巯基乙醇在顶级CO2腐蚀环境中的缓蚀和共缩聚行为

IF 4.8 Q2 ENERGY & FUELS

Journal of Pipeline Science and Engineering

Pub Date : 2025-03-01 DOI: 10.1016/j.jpse.2024.100224

Mariana C. Folena , Joshua Owen , Iain W. Manfield , Hanan Farhat , J.A.C. Ponciano , Richard Barker

Top of line corrosion (TLC) is a significant problem in oil and gas transportation pipelines, leading to both economic and production loss. Conventional organic corrosion inhibitors typically fail to provide effective protection for this particular type of corrosion. As such, the chemical inhibition of TLC relies on volatile compounds which can partition from the aqueous to the condensate formed at the top of the pipeline. Studies have shown that thiol compounds, through their high affinity for metallic surfaces, are providing effective inhibition in such environments, yet their inhibition mechanism and co-condensation characteristics are yet to be fully determined. This work studies the efficiency, adsorption mechanism and condensation behaviour of 2-mercaptoethanol (2-ME) as a volatile corrosion inhibitor in CO₂-containing TLC environments through a novel direct assessment of condensate chemistry and real-time TLC measurements. Experimental analysis of condensate partitioning is performed through the implementation of a biochemical technique which targets sulphydryl groups, coupled with a miniature electrode configuration for real time, in-situ electrochemical TLC measurements. The proposed assay results in a rapid, cost-effective screening technique that can monitor thiol-based chemistries that condense in conjunction with the water phase. The new developed biochemical methodology identified that from 20 ppm of 2-ME added to the bulk solution, only around 12 ppm was present within the condensate. Additionally, 2-ME addition into the system resulted in a corrosion inhibitor efficiency of 93.8% where the chemical act as a mixed-type corrosion inhibitor. The corrosion and condensation experiments are complemented with surface characterization via XPS and STEM-EDX techniques. The surface characterization analysis showed a compact inner layer containing sulphur which is related to adsorption of the thiol.

管线顶部腐蚀（TLC）是油气输送管道中存在的一个重要问题，它不仅会造成经济损失，还会造成生产损失。传统的有机缓蚀剂通常不能为这种特殊类型的腐蚀提供有效的保护。因此，TLC的化学抑制作用依赖于挥发性化合物，这些化合物可以从水溶液中分离到管道顶部形成的凝析液。研究表明，巯基化合物通过其对金属表面的高亲和力，在这种环境中提供了有效的抑制作用，但其抑制机制和共缩聚特性尚未完全确定。本研究通过一种新的直接评估冷凝化学和实时TLC测量方法，研究了2-巯基乙醇（2-ME）作为挥发性缓蚀剂在含co2的TLC环境中的效率、吸附机理和冷凝行为。通过实施以巯基为目标的生化技术，再加上用于实时、原位电化学TLC测量的微型电极配置，对冷凝物分配进行了实验分析。所提出的分析结果是一种快速、经济有效的筛选技术，可以监测与水相结合的硫醇基化学物质。新开发的生化方法确定，从20 ppm的2-ME添加到散装溶液中，只有12 ppm左右存在于冷凝液中。此外，在体系中加入2-ME后，缓蚀剂的效率达到93.8%，其中该化学物质作为混合型缓蚀剂。腐蚀和冷凝实验辅以XPS和STEM-EDX技术的表面表征。表面表征分析表明，其内层致密，含硫，这与硫醇的吸附有关。

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引用次数: 0