The Experts below are selected from a list of 13179 Experts worldwide ranked by ideXlab platform
Ningsheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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pitting Corrosion and microstructure of j55 carbon steel exposed to co2 crude oil brine solution under 2 15 mpa at 30 80 c
Materials, 2018Co-Authors: Yongqing Wang, Ningsheng ZhangAbstract:This study aimed to evaluate the Corrosion properties of J55 carbon steel immersed in CO2/crude oil/brine mixtures present in the wellbores of CO2-flooded production wells. The main corroded position of wellbore was determined and wellbore Corrosion law was provided. Corrosion tests were performed in 30% crude oil/brine solution under the simulated temperature (30–80 °C) and pressure (2–15 MPa) conditions of different well Depths (0–1500 m). The Corrosion behavior of J55 carbon steel was evaluated through weight-loss measurements and surface analytical techniques, including scanning electron microscopy, energy dispersive spectrometer, X-ray diffraction analysis, and optical digital microscopy. Corrosion rate initially increased and then decreased with increasing well Depth, which reached the maximum value of 1050 m. At this well Depth, pressure and temperature reached 11 MPa and 65 °C, respectively. Under these conditions, FeCO3 and CaCO3 localized on sample surfaces. Microscopy was performed to investigate Corrosion Depth distribution on the surfaces of the samples.
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effect of co2 partial pressure on the Corrosion behavior of j55 carbon steel in 30 crude oil brine mixture
Materials, 2018Co-Authors: Haitao Bai, Yongqing Wang, Qingbo Zhang, Ningsheng ZhangAbstract:The influence of CO2 partial pressure on the Corrosion properties, including Corrosion rate, morphology, chemical composition, and Corrosion Depth, of J55 carbon steel in 30% crude oil/brine at 65 °C was investigated. A Corrosion mechanism was then proposed based on the understanding of the formation of localized Corrosion. Results showed that localized Corrosion occurred in 30% crude oil/brine with CO2. The Corrosion rate sharply increased as the CO2 partial pressure (P co 2 ) was increased from 0 to 1.5 MPa, decreased from P co 2 = 1.5 MPa to P co 2 = 5.0 MPa, increased again at P co 2 = 5.0 MPa, and then reached a constant value after P co 2 = 9.0 MPa. The system pH initially decreased, rapidly increased, and then stabilized as CO2 partial pressure was increased. In the initial period, the surface of J55 carbon steel in the CO2/30% crude oil/brine mixtures showed intense Corrosion. In conclusion, CO2 partial pressure affects the protection performance of FeCO3 by changing the formation of Corrosion scale and further affecting the Corrosion rate.
Yongqing Wang - One of the best experts on this subject based on the ideXlab platform.
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pitting Corrosion and microstructure of j55 carbon steel exposed to co2 crude oil brine solution under 2 15 mpa at 30 80 c
Materials, 2018Co-Authors: Yongqing Wang, Ningsheng ZhangAbstract:This study aimed to evaluate the Corrosion properties of J55 carbon steel immersed in CO2/crude oil/brine mixtures present in the wellbores of CO2-flooded production wells. The main corroded position of wellbore was determined and wellbore Corrosion law was provided. Corrosion tests were performed in 30% crude oil/brine solution under the simulated temperature (30–80 °C) and pressure (2–15 MPa) conditions of different well Depths (0–1500 m). The Corrosion behavior of J55 carbon steel was evaluated through weight-loss measurements and surface analytical techniques, including scanning electron microscopy, energy dispersive spectrometer, X-ray diffraction analysis, and optical digital microscopy. Corrosion rate initially increased and then decreased with increasing well Depth, which reached the maximum value of 1050 m. At this well Depth, pressure and temperature reached 11 MPa and 65 °C, respectively. Under these conditions, FeCO3 and CaCO3 localized on sample surfaces. Microscopy was performed to investigate Corrosion Depth distribution on the surfaces of the samples.
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effect of co2 partial pressure on the Corrosion behavior of j55 carbon steel in 30 crude oil brine mixture
Materials, 2018Co-Authors: Haitao Bai, Yongqing Wang, Qingbo Zhang, Ningsheng ZhangAbstract:The influence of CO2 partial pressure on the Corrosion properties, including Corrosion rate, morphology, chemical composition, and Corrosion Depth, of J55 carbon steel in 30% crude oil/brine at 65 °C was investigated. A Corrosion mechanism was then proposed based on the understanding of the formation of localized Corrosion. Results showed that localized Corrosion occurred in 30% crude oil/brine with CO2. The Corrosion rate sharply increased as the CO2 partial pressure (P co 2 ) was increased from 0 to 1.5 MPa, decreased from P co 2 = 1.5 MPa to P co 2 = 5.0 MPa, increased again at P co 2 = 5.0 MPa, and then reached a constant value after P co 2 = 9.0 MPa. The system pH initially decreased, rapidly increased, and then stabilized as CO2 partial pressure was increased. In the initial period, the surface of J55 carbon steel in the CO2/30% crude oil/brine mixtures showed intense Corrosion. In conclusion, CO2 partial pressure affects the protection performance of FeCO3 by changing the formation of Corrosion scale and further affecting the Corrosion rate.
José M. Hallen - One of the best experts on this subject based on the ideXlab platform.
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markov chain model helps predict pitting Corrosion Depth and rate in underground pipelines
2010 8th International Pipeline Conference Volume 4, 2010Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, José M. Hallen, A EsquivelamezcuaAbstract:A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting Corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the transition probability function in a discrete space of pit Depths. The transition probability function can be completely identified by making a correlation between the stochastic pit Depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit Depth distribution mean value with time for different soil types. The simulated pit Depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting Corrosion Depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting Corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting Corrosion.Copyright © 2010 by ASME
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probability distribution of pitting Corrosion Depth and rate in underground pipelines a monte carlo study
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, José M. HallenAbstract:The probability distributions of external-Corrosion pit Depth and pit growth rate were investigated in underground pipelines using Monte Carlo simulations. The study combines a predictive pit growth model developed by the authors with the observed distributions of the model variables in a range of soils. Depending on the pipeline age, any of the three maximal extreme value distributions, i.e. Weibull, Frechet or Gumbel, can arise as the best fit to the pitting Depth and rate data. The Frechet distribution best fits the Corrosion data for long exposure periods. This can be explained by considering the long-term stabilization of the diffusion-controlled pit growth. The findings of the study provide reliability analysts with accurate information regarding the stochastic characteristics of the pitting damage in underground pipelines.
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Markov chain modelling of pitting Corrosion in underground pipelines
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, José M. HallenAbstract:Abstract A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting Corrosion in underground pipelines. The closed form solution of Kolmogorov’s forward equations for this type of Markov process is used to describe the transition probability function in a discrete pit Depth space. The identification of the transition probability function can be achieved by correlating the stochastic pit Depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit Depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting Corrosion Depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting Corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.
Francisco Caleyo - One of the best experts on this subject based on the ideXlab platform.
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markov chain model helps predict pitting Corrosion Depth and rate in underground pipelines
2010 8th International Pipeline Conference Volume 4, 2010Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, José M. Hallen, A EsquivelamezcuaAbstract:A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting Corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the transition probability function in a discrete space of pit Depths. The transition probability function can be completely identified by making a correlation between the stochastic pit Depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit Depth distribution mean value with time for different soil types. The simulated pit Depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting Corrosion Depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting Corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting Corrosion.Copyright © 2010 by ASME
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probability distribution of pitting Corrosion Depth and rate in underground pipelines a monte carlo study
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, José M. HallenAbstract:The probability distributions of external-Corrosion pit Depth and pit growth rate were investigated in underground pipelines using Monte Carlo simulations. The study combines a predictive pit growth model developed by the authors with the observed distributions of the model variables in a range of soils. Depending on the pipeline age, any of the three maximal extreme value distributions, i.e. Weibull, Frechet or Gumbel, can arise as the best fit to the pitting Depth and rate data. The Frechet distribution best fits the Corrosion data for long exposure periods. This can be explained by considering the long-term stabilization of the diffusion-controlled pit growth. The findings of the study provide reliability analysts with accurate information regarding the stochastic characteristics of the pitting damage in underground pipelines.
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Markov chain modelling of pitting Corrosion in underground pipelines
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, José M. HallenAbstract:Abstract A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting Corrosion in underground pipelines. The closed form solution of Kolmogorov’s forward equations for this type of Markov process is used to describe the transition probability function in a discrete pit Depth space. The identification of the transition probability function can be achieved by correlating the stochastic pit Depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit Depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting Corrosion Depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting Corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.
Shahid Sultan - One of the best experts on this subject based on the ideXlab platform.
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fabrication microstructure and corrosive behavior of different metallographic tin leaded bronze alloys part ii chemical corrosive behavior and patina of tin leaded bronze alloys
Materials Chemistry and Physics, 2016Co-Authors: Khalida Kareem, Shahid SultanAbstract:Abstract This paper presents the chemical Corrosion behavior and surface morphologies including cracks, pits and corrosive-Depth of “patina” in the different metallographic Cu–Sn–Pb bronze alloys by the trace amounts of S 2− , SO 4 2− , NO 3 − , Cl − and CO 3 2− aggressive environments. Typical patinas have been formed as brochantite patina (Cu 4 SO 4 ·(OH) 6 ) by SO 4 2− solution with 46–144 μm Corrosion Depth, atacamite patina (Cu 2 (OH) 3 Cl) by Cl − solution through many subsequent “dissolution-ion pairing-precipitation” steps with 15–70 μm Corrosion Depth, gerhardite patina (Cu(NO 3 ) (OH) 3 ) by NO 3 - solution with 0.3 μm corroded layer, gerhardite or lead nitrate (Pb(NO 3 ) 2 ) or malachite (Cu 2 (OH) 2 CO 3 ) patina by CO 3 2− /NO 3 - solution as 390 μm Depth of corrosive layer, atacamite Cu 2 Cl(OH) 3 along with cuprite (Cu 2 O) and cessiterite (SnO 2 ) patina with 11–390 μm multilayered corrosive crust by CO 3 2− /Cl − solution, and a mixed black patina of brochantite, brendtite (SnS 2 ), roxbyite (Cu 7 S 4 ) by S 2− solution with 34–256 μm corrosive Depth. Among all environments, the deterioration rate is evaluated as order of S 2− > CO 3 2− /Cl − > Cl − > CO 3 2− /NO 3 - > SO 4 2− > NO 3 − . The minor bronze disease is observed in highly tin-contained alloys (>19%), while the amount of lead in alloy exhibits no specific role in Corrosion. Based on these results, the phenomenological models of Corrosion behavior due to an internal oxidation, ionic migration and de-alloying of “a protective barrier” are proposed.
