Corrosion Rate Prediction Model Research Articles

Offshore Wind Power Foundation Corrosion Rate Prediction Model Based on Improved SHO Algorithm

Offshore Wind Power Foundation Corrosion Rate Prediction Model Based on Improved SHO Algorithm

Safety evaluation of offshore oil and gas well string based on corrosion rate prediction

The combined effects of corrosion, temperature, and pressure can cause safety issues such as shortened lifespan and failure of offshore high temperature and high pressure oil and gas well strings. This paper focuses on a certain sea area and conducts experiments on CO2 corrosion of different string materials under different temperature and partial pressure conditions. Combined with the two-phase flow wellbore temperature and pressure-coupling model, a corrosion rate prediction model based on Back Propagation Neural Network optimized by Genetic Algorithm (GA-BPNN) is established for predicting corrosion rate along the wellbore depth. At the same time, based on the API 5C3 standard, considering the degradation effect of thermally induced metal string strength and the influence of environmental pressure, a safety evaluation model of offshore oil and gas well string based on corrosion rate prediction was established, and analysis of the change law of residual strength of the string and prediction of remaining life under the influence of different factors were carried out.

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Green synthesis and metal oxide composites have attracted much attention from researchers of industry and academia. As a typical application of green synthesis and metal oxide composites, the continuous change of industrial technology and the continuous improvement of the social and economic level, the demand for oil and gas are also increasing. However, the spatial gap between the place of origin and the place of demand for oil and gas resources is large, so the long-distance oil and gas pipeline came into being. However, under the action of time, coupled with the corrosion effect of the soil due to deep burial, some pipelines have serious aging and corrosion phenomena. Therefore, in order to give corresponding guarantees for economic development, we need to conduct in-depth research and analysis of the corrosion of oil and gas long-distance pipelines and give effective solutions. In this paper, the corrosion rate prediction of buried oil and gas pipelines is studied in Changqing gas field. By improving the inertial weights and learning factors of the traditional particle swarm algorithm, the parameters of the generalized regression neural network are optimized and selected, and the corrosion rate prediction model of buried pipelines is finally constructed. Comparative analysis with other swarm intelligence algorithms shows that the improved particle swarm algorithm has stronger convergence ability and higher prediction accuracy than the BP model and SVM model. In addition, based on the detection data collected at the site of the gathering and transportation pipeline in Changqing gas field, this paper uses the extreme value distribution theory and the local corrosion progress formula to establish a prediction model for the residual life of corrosion of buried pipelines. The model established in this paper can effectively determine the risk pipe segment of buried pipeline and provide a decision-making basis for pipeline management departments. The work provides an important application guidance to green synthesis and metal oxide composites.

A Salt Spray Corrosion Prediction Model for Aviation Aluminum Alloy Based on MVO-GRNN Algorithm

It is very important to focus on the corrosion failure of aluminum alloy materials for airframe because of the increasing of aircraft service time. However, due to the long corrosion test cycle, the number of samples for processing alloy corrosion related data with high cost is very small. In this paper, a corrosion rate prediction model for less sample data sets is proposed: a General Regression Neural Network which using Multi-Verse Optimizer to optimize prarmeters in order to improve accuracy. In this paper, according to the public data of China Corrosion and Protection Network, the content of Mg, Cu, Fe and other components in different types of aluminum alloys, the corrosion environment (Cl− concentration, pH value) and outdoor exposure time are taken as the input and the mass loss per unit area of aluminum alloys is taken as the output of the model. Moreover, using radial basis function for comparsion. The experimental results show that the model has a good fitting effect for predicting the salt spray corrosion of aviation aluminum alloy, and its performance is better than the traditional RBFnetwork.

ANALYSIS OF CORROSION IN PRODUCTION TUBING USING OLGA SIMULATOR

Corrosion is a problem that occurs in the production system of industrial oil and gas. Control corrosion in tubing production is necessary for maintaining production and well direct as tubing production is the first contact with corrosion fluid and material agent. Corrosion is caused by a chemical reaction between a material and corrosion fluid. A corrosion rate prediction model can be used to predict its rate such as NORSOK and de Waard models. Both methods are used in Olga Simulator software by Schlumberger company that will be used in this paper. Some of the parameters and scenarios that influence corrosion are tested. Evaluation of reservoir pressure 40 bar, 45 bar, 50 bar, temperature 100℃, 125℃, 150℃, mono-ethylene glycol 0%, 20%, and 80% with 66,7%IE and 79,7%IE were used as corrosion inhibitors for looking at the best scenario.

Multi-factor mining and corrosion rate prediction model construction of carbon steel under dynamic atmospheric corrosion environment

For vehicles and vessels like cars and ships, the practical environmental conditions encountered are consequences of dynamic and static operation, include moving off and stopping. Therefore, it is necessary to perform dynamic atmospheric exposure corrosion tests on metal materials under vehicle operating conditions. In this paper, a carbon steel dynamic atmospheric corrosion test was designed considering vehicle operating conditions. After completion of the corrosion test, the corrosion data was collected, processed and predicted by a series of methods and algorithms. Firstly, multiple dedicated test vehicles operating in Tianjin were chosen as carriers for carbon steel dynamic atmospheric corrosion test. Secondly, the driving characteristics data was collected using an on-board positioning data collection and transmission system. Then, the dynamic fuzzy clustering algorithm was introduced to identify the key influencing factors in the parameters of vehicle driving characteristics and to optimize data dimension, the two key index parameters of the vehicle driving characteristics identified being the average vehicle speed and dynamic/static ratio. During the atmospheric corrosion test, a time-weighted weighting algorithm model was constructed and applied to the previously collected meteorological and pollutant data in order to fit the sampling interval of corrosion data. Finally, four models including GA-SVR, unoptimized SVR, GA-BPNN and unoptimized BPNN were constructed to predict the corrosion rate of carbon steel in the dynamic environment. The final results show that the root mean square error and average relative error reached their minimum for GA-SVR with the prediction result R2 = 0.9771, all indicators suggest that GA-SVR holds an advantage over the three models including SVR, GA-BPNN and BPNN.

Corrosion simulation via coupling computational fluid dynamics and NORSOK CO2 corrosion rate prediction model for an outlet header piping of an air-cooled heat exchanger

In this study CO2 corrosion of outlet header pipe of an air-cooled heat exchanger in a natural gas dehydration unit of a gas refinery was investigated by simulation via coupling computational fluid dynamics and NORSOK M-506 CO2 corrosion prediction model. The Euler-Euler approach incorporated with k–ε model for the fluid turbulence was employed to simulate gas-liquid two-phase flow. The simulation results were utilized as the inputs of NORSOK CO2 corrosion prediction model to calculate CO2 corrosion rate. Fluid streamlines, velocity, viscosity, density and temperature profiles and also the distribution of gas and liquid phases through the investigated outlet header pipe were calculated and a three-dimensional corrosion rate map was presented. The results showed that there is a good agreement between the data obtained by corrosion simulation and field observation. It was found here that coupling computational fluid dynamics and CO2 corrosion prediction models can successfully predict regional corrosion rates and determine locations susceptible to CO2 corrosion.

Application of internal corrosion direct assessment in CO2 slug flow submarine pipelines

This paper aims to establish a direct method for evaluating the internal corrosion in submarine pipelines, including four steps: pre-assessment, indirect inspection, detailed inspection, and post-assessment. The evaluation of corrosion severity and the determination of key monitoring points are particularly important in indirect detection. Taking CO2 gathering and transmission pipeline of an offshore oil field as an example, the overall internal corrosion rate of the pipeline is calculated to be 0.253 mm/a. The 12–45 m long pipeline is selected as the focus of corrosion monitoring. Through the comparative analysis of internal detection data and loss wall thickness, it is found that the results of indirect evaluation and direct evaluation are consistent, indicating the correctness of corrosion rate prediction model and loss wall thickness calculation method. The pipeline reevaluation time is five years. The evaluation method can accurately predict the corrosion status of multiphase flow pipeline and locate the corrosion.

Corrosion risk assessment of geothermal reinjection wellbore in Xining Basin, China

A potential corrosion risk exists during the re-injection of geothermal water in Xining Basin of China due to air entering the well caused by it being open. In this paper, a series of corrosion experiments using metal coupons and geothermal waters was conducted to evaluate the corrosion risk for steels in different typical geothermal waters by dissolved air. A novel and comprehensive prediction model for O2 corrosion rate in reinjection wellbores was established, and a sensitivity analysis of factors influencing air corrosion risk in reinjection wellbores was carried out. The results show that the main corrosive components in the geothermal water from the Xining Basin are Cl−, SO42−, H+ and the O2 added, which can cause a uniform corrosion and a small amount of local corrosion on the surface of carbon steel. The main corrosion products are FeO(OH) and Fe3O4. A high O2 partial pressure and flow velocity, a moderate temperature and salinity, and low pH promote a high corrosion rate for carbon steel. When the geothermal water is injected with a small amount of air, only the deeper part of the wellbore with a high temperature and pressure will suffer a serious corrosion risk, while when a large amount of air is mixed with geothermal water and injected, a huge corrosion risk will be imposed on the entire wellbore. Anti-corrosion measures should be taken if air is added to a wellbore. Removing the O2 from the geothermal water is the most effective solution for carbon steel. In addition, the established corrosion rate prediction model can consider more influencing factors and specializes in the situation of a low O2 partial pressure, which can be used as an effective tool to assess the corrosion risk in reinjection wells.

Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features

The corrosion resistance of low-alloy steel seriously influences its performance, particularly as a class of materials widely used in marine environments. In this study, we collected the marine corrosion data of low-alloy steels and established corrosion rate prediction models with machine learning algorithms. Both the chemical composition of low-alloy steel and environmental factors were used as input features, and the random forest algorithm was selected as the modeling algorithm. Feature reduction methods, including the gradient boosting decision tree, Kendall correlation analysis and principal component analysis, were first applied to select the dominating factors on the corrosion rate. Then, we proposed two feature creation methods to convert the chemical composition features into a set of atomic and physical property features. As a result, the feature creation method crafted a model no longer limited to materials with specific chemical compositions. The machine learning-based corrosion rate prediction model also showed good prediction accuracy of the corrosion rate. This study improved the generalization ability of the corrosion rate prediction model and proved the feasibility of machine learning in corrosion resistance evaluation.

Determination of the Corrosion Rate of Steel Bars in Concrete Based on the Porosity of Interfacial Zone

In the present paper, a simple model to predict the corrosion rate of steel bars in concrete is developed, considering the influence of the porosity of steel-concrete interfacial zone. Firstly, a model for the porosity of the interfacial zone is developed basing on the water-cement ratio gradient in the interfacial zone. Then the relationship between the porosity of the interfacial zone and the corrosion parameters of the steel bar in concrete is discussed, the anode-cathode ratio for the electrochemical corrosion process, the chloride threshold value and the resistivity of concrete are determined by the porosity of steel-concrete interfacial zone. The corrosion rate of steel bars, which is taken as the corrosion current density, is calculated by considering the cathodic concentration polarization of oxygen diffusion and resistance polarization of concrete, basing on the determined parameters above. Finally, the applicability and efficiency of the corrosion rate prediction model is verified by experiments.

A numerical external pitting damage prediction method of buried pipelines

Abstract The work introduces a numerical external damage prediction method for buried pipelines. The external pitting initiation and corrosion rate of oil or gas pipelines are affected by pipeline age, physicochemical properties of soils and cathodic protection performance as well as coating conditions. Before developing the damage prediction model, the influencing factors were weighed by grey relational analysis, and then the relationship among the pitting depth and the influencing factors of external corrosion was established for corrosion damage prediction through artificial neural network (ANN). Subsequently, the established ANN was applied to predict corrosion damage and corrosion rate for some selected cases, and the neural network prediction model was analyzed and compared to another corrosion rate prediction models. Through the analysis and comparison, a few opinions were proposed on the external corrosion damage prediction and pipeline integrity management.

Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm

The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.

Synergistic effects of corrosion time and stress on corrosion of casing steel in H2S/CO2 gas wells

Effect of corrosion duration on C110 casing steel was investigated in the stimulated gas field solution containing both 1 MPa CO2 and 1.5 MPa H2S at 90 °C by using a high temperature autoclave. And the corrosion rate prediction model was established based on the corrosion duration effect. In addition, effect of stress on corrosion of steel was analyzed from the thermodynamics and electrochemistry theories, moreover, the casing corrosion life prediction model was established under three axis stress state with considering the influences of stress and corrosion effect. The results reveal that the corrosion rate of C110 casing steel decreases significantly as corrosion time increases, then gradually tends toward stability. In addition, the casing steel is subjected to load and corrosion solution, the thermodynamic activity and the electrochemical reaction potential are changed, ultimately aggravating the process of corrosion. Compared to corrosion time and wall thickness, the internal pressure is the main factor governing the casing corrosion. Therefore, it is suggested that the synergistic effects of corrosion time and stress should be considered in casing safety design and operation.

A method for grounding grid corrosion rate prediction

Involved in a variety of factors, prediction of grounding grid corrosion complex, and uncertainty in the acquisition process, we propose a combination of EAHP (extended AHP) and fuzzy nearness degree of effective grounding grid corrosion rate prediction model. EAHP is used to establish judgment matrix and calculate the weight of each factors corrosion of grounding grid; different sample classification properties have different corrosion rate of contribution, and combining the principle of close to predict corrosion rate.The application result shows, the model can better capture data variation, thus to improve the validity of the model to get higher prediction precision.

Sensitivity of chloride-induced corrosion rate of steel in concrete to cover depth, crack width and concrete quality

This paper presents the results of analyses carried out to assess the sensitivity of chloride-induced corrosion rate to change(s) in cover depth, concrete quality and crack width. An empirical chloride-induced corrosion rate prediction model is used to carry out the sensitivity analyses. The main model input parameters for the model are surface crack width (w cr), cover (c) and concrete quality (characterized by a long-term diffusion coefficient, D 90). A two tier sensitivity analysis is carried out to establish (i) which individual input parameter (D 90, c or w cr) corrosion rate is most sensitive to, and (ii) which combination of two input parameters out of the possible three (D 90, c and w cr) corrosion rate is most sensitive to. The sensitivity analyses results show that in cracked concrete change in corrosion rate is greater if concrete quality is varied than if either crack width or cover depth is varied i.e. concrete quality has a dominating role on the effect of either crack width or cover depth on corrosion rate. Generally, in cracked concrete corrosion rate is more sensitive to change in concrete quality than change in either crack width or cover depth. Even though cover cracking increases corrosion rate, and that it should be minimized, a holistic approach considering the effect of cover depth, crack width, and more importantly, concrete quality on corrosion rate should be adopted.

Chloride-induced corrosion of steel in cracked concrete—Part II: Corrosion rate prediction models

Chloride-induced corrosion rate (icorr) prediction models for RC structures in the marine tidal zone that incorporate the influence of crack width (wcr), cover (c) and concrete quality are proposed. Parallel corrosion experiments were carried out for 2¼years by exposing one half of 210 beam specimens (120×130×375mm long) to accelerated laboratory corrosion (cyclic wetting and drying) while the other half underwent natural corrosion in the tidal zone. Experimental variables were wcr (0, incipient crack, 0.4, 0.7mm), c (20, 40mm), binder type (PC, PC/GGBS, PC/FA) and w/b ratio (0.40, 0.55). The two proposed models (one each for accelerated and natural icorr) can aid not only in quantifying the propagation phase, but also provide a novel way to select c, wcr and concrete quality.

Resistivity-based chloride-induced corrosion rate prediction models and hypothetical framework for interpretation of resistivity measurements in cracked RC structures

To present resistivity-based chloride-induced corrosion rate prediction models and hypothetical framework for interpretation of resistivity measurements in cracked RC structures. Parallel corrosion experiments were carried out by exposing one half of 210 beam specimens (120 × 130 × 375 mm) to accelerated laboratory corrosion (cyclic 3 days wetting with 5 % NaCl solution followed by 4 days air-drying) while the other half were left to undergo natural corrosion in a marine tidal zone. The specimens were cast using five concretes made using two w/b ratios (0.40 and 0.55) and three binders (100 % CEM I 42.5N (PC), 50/50 PC/GGBS and 70/30 PC/FA). Other variables in the experiments included cover depth (20 and 40 mm), crack width (0, 0.4 and 0.7 mm). Corrosion rate and resistivity were monitored bi-weekly in the specimens. The results relevant to this paper are presented and discussed. Experimental results (both natural and accelerated) show that there is an inverse relationship between corrosion rate and concrete resistivity in both cracked and uncracked RC structures. The results also show that for a given concrete resistivity value, corrosion rate of steel in concrete of a given binder type and w/b ratio increases with increase in crack width. Furthermore, for a given crack width, corrosion rate in cracked concrete is also shown to be influenced by concrete quality. Resistivity-based chloride-induced corrosion rate prediction models for cracked RC structures are proposed. This paper also advocates for the improvement in the interpretation of concrete resistivity measurements with respect to steel corrosion in cracked RC structures. A hypothetical framework to aid in this is proposed.

The Prediction of Grounding Grid Corrosion Rate Based on Vector Similarity and Fuzzy Closeness

Aiming at the grounding grid corrosion prediction involves many factors, the relationship is complex, and there are uncertainty and ambiguity in gaining involved factors, an efiective grounding grid corrosion rate prediction model is proposed. Consider sample set of every corrosion factories as feature vectors and then standardized them; by calculating the similarity of feature vectors and corrosion rate vectors, the weight of each corrosion factor on corrosion rate was obtained; treat membership function based on relevant relationship and half-Gaussian distribution as the core to determine the membership; use difierent sample properties have difierent corrosion rate contributions to do classiflcation, and combine closeness principles to predict corrosion rate. The application results show that the model can efiectively predict the grounding grid corrosion rate of those ground web sites who have near distance or similar soil physical and chemical indicators.

Strength Time Varying Analysis of Casing with CO2 Corrosion Defects

The time-varying of the casing string strength is analyzed in view of the CO2 corrosion effect. The pressure-temperature coupling calculation is given with the dual iterative computation method. In the direction of well depth, the distribution of pressure and temperature are obtained, and then the computation prediction model of CO2 corrosion rate for casing string is built based on the Norsok model. According to the well testing theory in the fixed value production stage, the relationship between the wellhead pressure and the time is obtained through the well logging data regression. By introducing the pressure-temperature time-varying law into the CO2 corrosion rate prediction model, the remaining strength time-varying analysis model of the casing string in CO2 corrosion environment is established. Calculation results indicate that the casing strength decreases with time because of the wall thickness reduction due to CO2 corrosion, the remaining strength of casing string drop quickly in the early stable stage, and getting slow down as the time increases. The reducing extent of casing limit collapse destroyed strength is bigger than the limit internal strength and the body tensile strength by uniform corrosion affection.