Francois Ayello

Effect of Liquid Impurities on Corrosion of Carbon Steel in Supercritical CO2

The increasing urgency to mitigate global warming has driven many efforts to control green house gas emissions. One solution among many is carbon capture and storage. However, CO2 emitters are not necessarily in the close vicinity of potential geologic storage sites. In consequence CO2 will be transported from generation site to storage sites under high pressures. This will necessitate a network of pipelines gathering supercritical CO2 from diverse sources and transporting it through transmission lines to the storage sites. These pipelines will be under corrosion risks, particularly because of possible carryover of trace impurities produced from the different sources, such as water, chloride, NOx , SOx , and O2 . The effects of impurities on corrosion in supercritical CO2 have yet to be evaluated systematically. Corrosion of carbon steel associated with water and impurities in supercritical CO2 was studied by Electrochemical Impedance Spectroscopy in autoclaves. Five impurities were studied by introducing them in the liquid condensed phase: water, amine, HCl, HNO3 and NaOH. Results were analyzed in terms of the phase behavior and speciation.Copyright

Quantitive Assessment of Corrosion Probability— A Bayesian Network Approach

Corrosion processes mainly affect the probability of failure, which then leads to consequences, such as, fire, explosion, or environmental damage. This paper focuses on the use of Bayesian network models for assessing the probability of corrosion. The Bayesian network approach incorporates cause- effect relationships of complex systems in the form of conditional probabilities. This method considers both knowledge uncertainties (i.e., modeling uncertainties) and data uncertainties to make more informed decisions. The Bayes theorem allows the model to predict the probability of events from their causes, and, if a particular event is known to have occurred, predict probable causes of that event. Two case studies, the first one involving internal corrosion and the second involving external corrosion of oil and gas pipeline, are presented, along with validation using field measurements. The extension of the approach to predicting stress corrosion cracking of pipelines is discussed.

Corrosion threat assessment of crude oil flow lines using Bayesian network model

Abstract Abu Dhabi Company for Onshore Oil Operations operates multiple carbon steel oil flow lines, which are emplaced on the desert surface of Abu Dhabi. The companys main oil pipelines are buried with coating and cathodic protection and internally protected by chemical inhibition, but the flow lines are without coating and cathodic protection. Over the years, this approach has been successful for flow lines, but the frequency of corrosion related leaks has increased recently due to changing operating and external conditions. This paper describes the use of a Bayesian network model that combines physics based models and expert knowledge of the flow lines to predict corrosion flaws depth and leak probability. It is shown that the Bayesian network approach can be useful in estimating location specific probability of failure and thus providing input to the prioritisation of inspections and corrosion mitigation. The approach was validated for five selected flow lines, where detailed field examination was available for comparison to model predictions.

Development of a Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks: A Concept

Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it can remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stress, external soil environment, and the electrolyte chemistry beneath disbonded coatings. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult.In an attempt to assess the SCC probability, a Bayesian network model was created. The model links events by cause-consequence connections. The strengths of these connections are adjusted using expert knowledge, analytical models, and data from the field. Bayesian network modeling was chosen because it takes into account the high degree of uncertainty in the input parameters. Other models have been developed to assess SCC: such as indexing methods, heuristics models, and mechanistic models. However, their main limitation is the uncertainty of the input parameters. One other strength of the Bayesian model is that calculations can be run in two directions: the forward direction from cause to consequence and the backward direction from observation to causative factors. In the forward direction, the model evaluates the probability of SCC failure using various input probabilities of factors that are important to SCC. In the backward direction, the model can evaluate the effect of any known occurrence of SCC failure on the probabilities of causative factors and thus condition the Bayesian network to evaluate the future failure probability.In this paper, we discuss a Bayesian network model for high-pH SCC. The conceptual framework, acquisition of data, and the inclusion of uncertainties are described. In addition, an example of the model application to high pH SCC is given. The effects of service and field conditions such as soil type, soil chemistry, coating type, surface preparation techniques, stresses, residual stress due to pipe manufacturing conditions, welds, dents, location such as proximity to rivers, wetting and drying cycles, etc. on the SCC probability can be assessed with the model. The model details shown in this publication will only cover the stress affect due to surface preparation, welds, dents, and manufacturing conditions and temperature effect. The effects of other factors and validation against field experience will be discussed in future publications.Copyright

Energy Storage Via Electrochemical Conversion of CO2 Into Specialty Chemicals

There is significant interest in technologies that reduce or mitigate greenhouse gases in the atmosphere because of their contribution to climate change. In addition, concerns for energy security are linked to political, environmental, and economic factors that threaten supply of hydrocarbon sources for fuels and the petrochemical feedstock that support the production of plastics, fertilizers, and chemical supply chains. With these climate and energy security concerns, there is a need for technologies that can economically address both issues. In addition, with increased integration of renewable energy systems into the grid, there are major concerns about grid instability and the need for energy storage. Significant research is being done on both topics, but there is a need to more efficiently transmit and use energy (which is the focus of the Smart Grid initiatives) as well as store energy for future use. Electrochemical conversion of CO2 to useful products will be discussed including analyses of the energy and carbon balances required for the process, the value of the end use chemicals as energy storage media, and the energy density of the end use chemicals compared to other energy storage technologies.Copyright