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Corrosion Behavior And Mechanism Of X65 Steel In Simulated Deep-sea Environment

Posted on:2019-11-08Degree:DoctorType:Dissertation
Country:ChinaCandidate:Q S LiFull Text:PDF
GTID:1361330626451882Subject:Materials science
Abstract/Summary:PDF Full Text Request
The deep sea is rich in oil and gas resources.The development of deep-sea oil and gas resources can alleviate the energy crisis caused by the gradual depletion of shallow oil and gas reserves and effectively meet the ever-increasing energy demand.The underwater oil and gas transportation structural engineering is one of the indispensable key links in the deep-sea oil and gas development.In most cases,the gathering,storage and transportation of the extracted oil and gas should be carried out through the underwater oil and gas transportation structures.Underwater oil and gas transportation structures generally adopt steel pipelines and have poor corrosion resistance.It is difficult and costly to repair,once the corrosion damage occurs in the deep sea environment.Therefore,it is important to study the corrosion behavior and mechanism of the materials of the underwater oil and gas transportation structure used in the deep-sea environment.The corrosion behavior of X65 steel in the artificial seawater under different hydrostatic pressures and temperatures is investigated by electrochemical measurements and surface analysis techniques.The results indicate that the corrosion rate increases as the hydrostatic pressures or the temperature increases.The influence mechanism of the hydrostatic pressure includes expanding the cracks of the corrosion product layer and promoting the conversion of?-FeOOH to Fe3O4 in the corrosion product.The temperature does not change the corrosion products and the corrosion type of X65 steel.The temperature accelerate the corrosion of X65 steel by increasing the activation of the corrosion reaction.The failure process of the coated X65 steel in the artificial seawater under different hydrostatic pressures is investigated by electrochemical impedance spectroscopy measurements,gravimetric tests,adhesion tests and chemical structure measurements.The results indicate that the diffusion coefficient of the solution in the coating increases as the hydrostatic pressure increases.The mechanism of the hydrostatic pressure is a physical effect.Rapid increase of the water absorption rate causes rapid decrease in the adhesion of the coating.The failure process of the coating is accelerated by the combined effect of increased water absorption and decreased adhesion.The corrosion behavior of X65 steel is investigated in the artificial seawater inoculated with sulfate reducing bacteria?SRB?by electrochemical impedance techniques and surface analysis techniques,together with the most probable number method and inductively coupled plasma mass spectrometry measurements.The results indicate that in the SRB-containing seawater,the X65 steel is corroded in the initial immersion.The corrosion becomes inhibited with the forming of the biofilm during the subsequent immersion.The inhibition efficiency is related to the SRB growth.The biofilm metabolized by SRB played a key role in the corrosion inhibition of X65 steel.The corrosion types of X65 steel in different solutions are discriminated by a machine learning method called gradient boosting decision tree?GBDT?.The data are based on eleven features extracted from the electrochemical noise signals.The results indicate that the GBDT model can discriminate the corrosion types from the mixed corrosion data of X65 steel and 304 stainless steel.The GBDT model shows a good prediction accuracy of 98.4%.Furthermore,the GBDT model has a universal application scope.Among the eleven features used by the GBDT model,the noise resistance Rn,the frequency of events fn and the wavelet dimension of potential WDE play the most significant role with regard to the discrimination of corrosion types.
Keywords/Search Tags:Deep sea, Hydrostatic pressure, Temperature, Sulfate reducing bacteria, X65 steel, Epoxy coating, Gradient boosting decision tree
PDF Full Text Request
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