Pitting corrosion evaluation: a review

Pitting corrosion is an insidious localized form of corrosion causing much devastating destruction to structural members such as stainless steel in chloride environment. This paper gives a review of the mechanism processes of pitting, stages, factors facilitating pitting corrosion, techniques of evaluating pitting corrosion and some research work on pitting corrosion. The rudimentary knowledge of the mechanisms of pitting corrosion from this work will be of assistance to the selection process, specification and the use of stainless steels and other structural members

Development of a simplified electrochemical noise method to monitor assets under insulation

Corrosion under insulation (CUI) is a serious problem in many industries both on and offshore. When operations are conducted in marine environments the opportunity for CUI is increased due to the harsh environment created by salt water. CUI can damage equipment and piping systems leading to loss of product containment which puts personnel and production in jeopardy. This research determines the current understanding of CUI, methods available for determining corrosion rates and develops a simplified electrochemical noise method to determine and predict CUI through laboratory and field operations. From the high level CUI literature review two areas for further investigation were determined. Pitting corrosion was identified as a significant area of study and as it is as a key mechanism of pipe failure in offshore operations. Electrochemical noise was found to be a promising technique for monitoring CUI due to its ability to identify corrosion mechanism as well as corrosion rate. Two objectives for research were identified: 1. To generate corrosion under insulation data 2. To develop a continuous monitoring technique for assets under insulation To satisfy the objective of CUI data generation a comprehensive experimental plan was developed. This plan develops a field test procedure to study corrosion under insulation (CUI) in marine environments that ensures that data collected is representative of CUI developed in the offshore industry. The experimental design was completed and the facilities and equipment installed with monitoring and analysis of the ongoing experiment will be completed over the next three years. This research developed, verified and applied a simplified EPN method to monitor corrosion. This method can be used to recognise different corrosion mechanisms (localized/uniform) and to estimate corrosion rates. A relationship between isolated electrode EPN, mass loss and corrosion rate was established. The simplified method aided in determining that there is increased corrosion activity under insulation due to retained moisture at the pipe surface. The completion of this research expanded the understanding of how and when CUI occurs, developed new and developed a new simplified electrochemical noise method for online monitoring of CUI. These successes will ultimately improve offshore operations; both improving safety and production

Dynamic corrosion risk-based integrity assessment of marine and offshore systems

Corrosion poses a serious integrity threat to marine and offshore systems. This critical issue leads to high rate of offshore systems degradation, failure, and associated risks. The microbiologically influenced corrosion (microbial corrosion), which is a type of corrosion mechanism, presents inherent complexity due to interactions among influential factors and the bacteria. The stochastic nature of the vital operating parameters and the unstable microbial metabolism affect the prediction of microbial corrosion induced failure and the systems’ integrity management strategy. The unstable and dynamic characteristics of the corrosion induced risk factors need to be captured for a robust integrity management strategy for corroding marine and offshore systems. This thesis proposes dynamic methodology for risk-based integrity assessment of microbially influenced corroding marine and offshore systems. Firstly, a novel probabilistic network based structure is presented to capture the non-linear interactions among the monitoring operating parameters and the bacteria (e.g., sulfate-reducing bacteria) for the microbial corrosion rate predictions. A Markovian stochastic formulation is developed for the corroding offshore system failure probability prediction using the degradation rate as the transition intensity. The analysis results show that the non-linear interactions among the microbial corrosion influential parameters increase the corrosion rate and decrease the corroding system's failure time. Secondly, a dynamic model is introduced to evaluate the offshore system's operational safety under microbial corrosion induced multiple defect interactions. An effective Bayesian network - Markovian mixture structure is integrated with the Monte Carlo algorithm to forecast the effects of defects interactions and the corrosion response parameters’ variability on offshore system survivability under multispecies biofilm architecture. The results reveal the impact of defects interaction on the system's survivability profile under different operational scenarios and suggest the critical intervention time based on the corrosivity index to prevent total failure of the offshore system. Finally, a probabilistic investigation is carried out to determine the parametric interdependencies' effects on the corroding system reliability using a Copula-based Monte Carlo algorithm. The model simultaneously captures the failure modes and the non-linear correlation effects on the offshore system reliability under multispecies biofilm structure. The research outputs suggest a realistic reliability-based integrity management strategy that is consistent with industry best practices. Furthermore, a dynamic risk-based assessment framework is developed considering the evolving characteristics of the influential microbial corrosion factors. A novel dynamic Bayesian network structure is developed to capture the corrosion's evolving stochastic process and the importance of input parameters based on their temporal interrelationship. The associated loss scenarios due to microbial corrosion induced failures are modeled using a loss aggregation technique. A subsea pipeline is used to demonstrate the model performance. The proposed integrated model provides a risk-based prognostic tool to aid engineers and integrity managers for making effective safety and risk strategies. This work explores the microbial corrosion induced failure mechanisms and develops dynamic risk-based tools under different operational scenarios for systems’ integrity management in the marine and offshore oil and gas industries

Risk-based evaluation of pitting corrosion in process facilities

Pitting is one of the most challenging forms of corrosion to study and model due to complex pit behavior. Pitting can occur in different engineering alloys and can lead to catastrophic consequences. Pits are usually latent or difficult-to-detect and resulting degradation often causes in-service failure of process equipment. Therefore, the ability to predict pit behavior is key to design and maintenance of assets. In particular, pitting corrosion is a significant challenge in marine environments and offshore operations due to remoteness of operations and hidden damage under insulations. Thus, the ability to assess risk and estimate remaining life of assets affected by pitting corrosion is necessary for timely maintenance and safe operation of assets. This thesis proposes a methodology to assess and dynamically update the risk of pressurized components affected by pitting corrosion. To take into consideration the time-dependent growth of pits, the application of non-homogenous Markov process is proposed to model the maximum pit depth. The integration of the developed maximum pit model into a pressureresistance model is proposed to predict the failure probability of affected components. An economic consequence analysis model is developed to estimate both business and accidental losses due to failure of the affected component. Then, risk is estimated by integrating models developed for probability of failure and associated consequences. The application of Bayesian analysis is proposed to update estimated risk as new inspection data gets available and also as economic condition of the process evolves. This work also proposes a risk management strategy including corrosion prevention, control and monitoring measures to make effective decision related to pitting corrosion. The application of the proposed methods is demonstrated using different case studies

Machine Learning Accelerated Discovery of Corrosion-resistant High-entropy Alloys

Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional composition and configuration space, making materials designs via experimental trial-and-error or brute-force ab initio calculations almost impossible. Here we develop a physics-informed machine-learning framework to identify corrosion-resistant high-entropy alloys. Three metrics are used to evaluate the corrosion resistance, including single-phase formability, surface energy and Pilling-Bedworth ratios. We used random forest models to predict the single-phase formability, trained on an experimental dataset. Machine learning inter-atomic potentials were employed to calculate surface energies and Pilling-Bedworth ratios, which are trained on first-principles data fast sampled using embedded atom models. A combination of random forest models and high-fidelity machine learning potentials represents the first of its kind to relate chemical compositions to corrosion resistance of high-entropy alloys, paving the way for automatic design of materials with superior corrosion protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys and we identified composition regions with high corrosion resistance. Machine learning predicted lattice constants and surface energies are consistent with values by first-principles calculations. The predicted single-phase formability and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments. This framework is general in its application and applicable to other materials, enabling high-throughput screening of material candidates and potentially reducing the turnaround time for integrated computational materials engineering

Corrosion and hydrate formation in natural gas pipelines

Gas industry annually invests millions of dollars on corrosion inhibitors in order to minimize corrosion implications on flow assurance; however, attention has never been focused on possibilities of these chemicals to promote hydrate formation along deepwater pipelines, which would equally result in another flow assurance problem of high magnitude. This study investigated the possibilities of corrosion inhibitors to aid the formation of gas hydrate along offshore (or underwater) pipeline systems; developed a predictive model on corrosion rate for natural gas pipelines with gas hydrates as the corroding agent and finally investigated the ability of pure N2 and H2 gases to inhibit the formation of gas hydrates.All experiments in this thesis were conducted by forming various water-gas systems in a cylindrical cryogenic sapphire cell. The first investigative work on hydrate-corrosion relationship was conducted by allowing contacts between an industrial grade natural gas (with 20% CO2 content) and five different corrosion inhibitors that are commonly used at offshore fields. The equipment, consisting of several fittings could operate at a temperature range of -160oC – 60oC (with accuracy of ± 0.10oC) and pressure range of 1bar to 500bar (with accuracy of ± 0.5bar). Using the ‗Temperature Search‘ method, the hydrate formation temperature point for each inhibitor was located at 500ppm and 100bar and the result compared with that of control experiment. Due to observed significant influence, further investigations were conducted on Dodecylpyridinium Chloride (DPC) at various concentrations and pressures. The corrosion model was developed based on hydrate‘s thermodynamic properties such as the operating temperature, pressure, fluid fugacity, wall shear stress, superficial velocity, enthalpy, entropy and activity coefficient amongst others, and a Matlab computer code was written to simulate the generated solution algorithm. Finally, components interaction study was conducted on various gas mixtures inside the sapphire cell to investigate the ability of pure N2 and H2 gases to inhibit the formation of gas hydrates.The obtained results established that all corrosion inhibitors aid hydrate promotion; this was attributed to their surfactant and hydrogen bonding properties which were essential for hydrate formation. The five investigated inhibitors showed different promotional rates with DPC having the highest promotional ability. The different promotional rate is due to their different sizes and structures, active functional groups and affinity for water molecules which determine the type(s) of hydrogen bonding exhibited by each inhibitor while in solution. The significant performance of DPC compared to other inhibitors was justified by the specific available active functional group which obeys electronegativity trend of periodic table to determine whether the resulting bond type will be polar covalent, ionic or ionic with some covalent characteristic in nature. Also, DPC hydrates revealed strong influence of the chemical‘s surfactant properties at all pressures and concentrations while its Critical Micelle Concentration (CMC) was believed to be 5000ppm due to the various anomaly behaviors exhibited at this particular concentration.The developed mathematical model adequately predicted corrosion rates with gas hydrate as the corroding agent and its effectiveness was confirmed by the level of agreement between its generated results and existing literatures. The resulting corrosion rate from hydrates could be as high as 174mm/yr (0.48mm/day). This is extremely alarming compared to the industry‘s aim to operate below 2mm/yr. At this rate, an underwater pipeline would be subjected to full bore rupture within some days if corrective measures are not quickly taken.Furthermore, the components interaction study revealed that CH4 played key roles on hydrate formation patterns during natural gas transportation through offshore pipeline system; the higher a natural gas CH4 content, the higher the risk of hydrates promotion. It also showed that when alone, CO2 does not form hydrate at low concentrations but showed a remarkable ability to aid hydrate formation when mixed with CH4. This is not surprising since it is also a former with ability to form Type I hydrate due to its very small size. Again, the ability of pure N2 and pure H2 gases to inhibit the formation of gas hydrate was confirmed but with H2 showing more significant effects. This was ascribed to their individual pressure condition to form hydrate. Though, N2 gas with small molecules forms Type II hydrate at a relatively higher pressure above the investigated pressures, it still forms hydrate within higher operating pressures practiced at gas fields during the transportation. However, H2 gas can never form hydrate at any natural gas transportation conditions. H2 gas only forms hydrates at extremely high pressure of about 2000bar because its molecules are too small and usually leaked out of hydrate cage, thus, reducing the amount that could be stored. By extension, these individual properties affect their interactions with natural gas during the hydrate formation process.Conclusively, this study has essentially revealed a new hydrate-corrosion relationship and established the need for comprehensive investigations in this research area. At all the investigated pressures, it was realized that DPC prolonged the complete blockage of the glass orifice at 10000ppm. This special characteristic may suggest the potential in applying the chemical as an additive for natural gas transportation and storage in slurry forms. Finally, the use of pure N2 or H2 as hydrate inhibitor in the offshore pipeline would be very cost effective to the industry. However, extreme care should be taken during the selection process since there are needs to further investigate the safety factors, material availability, cost implication and recovery from the main gas stream in order to choose the better option

A Probabilistic Corrosion Model for Copper-Coated Used Nuclear Fuel Containers

Lifetime predictions of used nuclear fuel containers destined for permanent storage in Deep Geological Repositories (DGRs) are challenged by the uncertainty surrounding the environment and the performance of both containers and engineered barriers over repos- itory timescales. Much of the work to characterise the response of engineered barriers to postulated evolving environmental conditions and degradation mechanisms is limited to very short-term laboratory tests or at best in-situ large-scale experiments spanning less than a few decades. While much is learned from these test programmes, the fact remains that long-term performance of many tens of thousands of Used Fuel Containers (UFCs) across a timescale of 100,000 years or more cannot be estimated with a significant degree of confidence by extrapolating single point results of short-term experiments. This is par- ticularly true when there is a desire to understand the progression of container failures and the timing of contaminants subsequently released into the geosphere. Used Fuel Container (UFC) lifetime predictions require a probabilistic approach to address uncertainty. Accord- ingly, this thesis addresses three objectives. The first is to develop a probabilistic model to estimate the time to penetrate through the copper coating of a UFC, assuming sulphide- induced corrosion is the primary degradation mechanism of concern. Within this model, also develop a framework to account for the design of the Engineered Barrier System (EBS) and proposed repository layout. The second is to enhance the probabilistic corrosion model by integrating the potential effects of latent copper coating defects and the single temper- ature transient predicted for the repository. The third is to develop a stochastic process model for pitting corrosion, integrate the same into the sulphide-induced corrosion model, and estimate the time to penetrate through the copper coating based on both degradation mechanisms. To satisfy the first two objectives, this work presents a unique Monte Carlo probabilistic framework. With respect to the third objective, modelling pitting corrosion in copper under postulated repository environments poses a significant challenge since there is no relevant data and the likelihood of this mechanism remains a much debated topic. To overcome this challenge and facilitate demonstration of the approach to modelling pit growth, surrogate data is utilised. In addition to detailing various options for modelling pit growth, this work presents a novel and more transparent, self-contained approach to the estimation of the underlying process intensity when pit growth is modelled via a non- homogeneous Markov process. Finally, the combined effect of pitting and sulphide-induced corrosion on UFC copper-coating lifetimes is demonstrated. The modelling results are for the purpose of illustrating a potential methodology only