The electrolyte renewal effect on the corrosion mechanisms of API X65 carbon steel under sweet and sour environments

Santos, Bernardo Augusto Farah; Serenário, Maria Eduarda Dias; Souza, Rhuan Costa; Oliveira, Jefferson Rodrigues de; Vaz, Gustavo L.; Gomes, José Orlando; Bueno, Alysson Helton Santos

doi:10.1016/j.petrol.2021.108347

Cited by 15 publications

(11 citation statements)

References 49 publications

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“…In the corrosion dataset, a total of 100 valid data were collected. Among them, 40 data are from literature (Yin et al, 2007;Liu et al, 2014a;Li et al, 2012;Liu et al, 2017;Santos et al, 2021), and the other 60 data are from corrosion simulation experiments accumulated in our laboratory over the years. As shown in Table 1, all the materials in the statistics are pipeline steels with a small amount of alloying elements,and each complete data sample is composed of 13 material features (i.e., C, Si, Mn, P, S, Cr, Ni, Cu, Mo, Ti, Nb, Al, V), eight solution features (i.e., Vs, Sal., Cl − , Ca 2+ , Mg 2+ , Na + , SO 4 2-), four environmental features (i.e., T, H 2 S, CO 2 , CO 2 /H 2 S), immersion time (i.e., t) and pitting information.…”

Section: Establishing the Datasetmentioning

confidence: 99%

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Tang²,

Wang

et al. 2021

Front. Mater.

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Pitting corrosion seriously harms the service life of oil field gathering and transportation pipelines, which is an important subject of corrosion prevention. In this study, we collected the corrosion data of pipeline steel immersion experiment and established a pitting judgment model based on machine learning algorithm. Feature reduction methods, including feature importance calculation and pearson correlation analysis, were first adopted to find the important factors affecting pitting. Then, the best input feature set for pitting judgment was constructed by combining feature combination and feature creation. Through receiver operating characteristic (ROC) curve and area under curve (AUC) calculation, random forest algorithm was selected as the modeling algorithm. As a result, the pitting judgment model based on machine learning and high dimensional feature parameters (i.e., material factors, solution factors, environment factors) showed good prediction accuracy. This study provided an effective means for processing high-dimensional and complex corrosion data, and proved the feasibility of machine learning in solving material corrosion problems.

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Section: Establishing the Datasetmentioning

confidence: 99%

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Tang²,

Wang

et al. 2021

Front. Mater.

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“…This assembly was adopted based on the work developed by Souza et al [8] because, given the aggressiveness of the evaluated environments, the tests would hardly provide reliable and reproducible responses with the use of conventional reference electrodes. Some researchers have used the same assembly and have also observed a good correlation with results obtained with a conventional assembly [26–29].…”

Section: Methodsmentioning

confidence: 84%

The role of acetic acid in FeCO₃ scale deposition on CO₂ corrosion of API X65 carbon steel under high temperatures

Santos

Souza

Serenário

et al. 2021

Corrosion Engineering, Science and Technology

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The combined effects of high temperatures and different acetic acid (HAc) concentrations on corrosion and deposition of FeCO 3 scales on API X65 carbon steel in CO 2 -saturated brines were investigated. Conditions with the absence and presence of 100 and 600 ppm of HAc were evaluated under high pressure at 90°C and 120°C. Electrochemical impedance spectroscopy, linear polarisation resistance, weight loss and characterisations through scanning electron microscopy, energy-dispersive spectroscopy and X-ray diffraction were conducted. Conditions with the absence of acid presented a huge deposition of sweet corrosion products and low corrosion rates. The addition of 100 ppm of acid at 90°C did not increase significantly corrosion rates; however, it retarded the precipitation of FeCO 3 . Higher temperatures favoured iron carbonate nucleation even in the presence of HAc. The highest corrosion rates were observed for the environment with 600 ppm of HAc at 120°C.

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“…This gas is extremely toxic, and it is responsible for increasing the medium aggressiveness acting as an extra source of H +, which in turn reduces pH. Furthermore, the H 2 S corrosion mechanism, called sour corrosion, is known to form corrosion products when in contact with ferrous ions that, in favorable conditions, may cover the steel and contribute to the diminishing of corrosive processes 30,49,50 .…”

Section: Critical Analyses Of Methods To Evaluate Soil Corrosivity Ba...mentioning

confidence: 99%

Proposal of a Novel Criteria for Soil Corrosivity Evaluation and the Development of New Soil Synthetic Solutions for Laboratory Investigations

et al. 2022

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A novel method to classify the aggressiveness of soil considering its physicochemical content and the development of new synthetic solutions for lab uses is proposed. The results showed that the main criteria existing in the literature for soil corrosivity classification might cause misunderstandings about the real aggressiveness. The synthetic solutions proposed aim to cover a wide range of soil found worldwide in order to refine the assessment of their characteristics hence their corrosivity. For the lab experiments, an API 5L X65 pipeline carbon steel was used. The solutions presented great reliability, and they seemed to be adequate to simulate soils with the presence of sulfate-reducing bacteria (SRB), chloride, and high pH.

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The electrolyte renewal effect on the corrosion mechanisms of API X65 carbon steel under sweet and sour environments

Cited by 15 publications

References 49 publications

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

The role of acetic acid in FeCO₃ scale deposition on CO₂ corrosion of API X65 carbon steel under high temperatures

Proposal of a Novel Criteria for Soil Corrosivity Evaluation and the Development of New Soil Synthetic Solutions for Laboratory Investigations

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The electrolyte renewal effect on the corrosion mechanisms of API X65 carbon steel under sweet and sour environments

Cited by 15 publications

References 49 publications

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

The role of acetic acid in FeCO3 scale deposition on CO2 corrosion of API X65 carbon steel under high temperatures

Proposal of a Novel Criteria for Soil Corrosivity Evaluation and the Development of New Soil Synthetic Solutions for Laboratory Investigations

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The role of acetic acid in FeCO₃ scale deposition on CO₂ corrosion of API X65 carbon steel under high temperatures