Dynamic Corrosion Risk Assessment in the Oil and Gas Production and Processing Facility

  • Skovhus, Torben Lund (Projektdeltager)
  • Khan, Faisal (Projektleder)
  • Taleb Berrouane, Mohammed (Projektdeltager)
  • Hawboldt, Kelly (Projektdeltager)



    Corrosion is a major cause of process equipment deterioration in the oil and gas industry. It represents a significant threat to asset integrity and process safety. Corrosion can lead to leakage, which subsequently, leads to contamination by the spill of hazardous materials, vapour cloud explosions or toxic releases, depending on the geolocation and nature of the fluid carried inside the process equipment. For metal structures, the deteriorative process caused by corrosion reduces the residual ultimate strength leading to structural failure when exceeding the total stress. Localized corrosion is reported to be the most hazardous form of corrosion leading to catastrophic failures. Among corrosion modes, microbiologically influenced corrosion (MIC) is particularly complex to predict, detect and mitigate. Hence, significant attention should be given to prediction of the occurrence of MIC and assessment of the associated risks. Several studies by microbiologists and corrosion scientists focused on the understanding of MIC initiation and development mechanisms. However, in-depth assessment of MIC susceptibility and risk quantification is still lacking. This thesis advances the understanding of MIC susceptibility and risk assessment by providing enhanced probabilistic models developed to fit the complexity of the microbiological corrosive process. Bayesian analysis was employed to assess the potential of having MIC while considering: chemical, physical, biological and molecular variables. A new modelling tool based on Stochastic Petri-nets enhanced with Bayesian updating capabilities was developed to address the main shortcomings of traditional Bayesian networks. This work also proposes an MIC risk assessment framework using Bow-Tie
    iv analysis and a corrosion resilience model based on Stochastic Petri-nets. The application of the proposed methods is demonstrated using different case studies. The outcomes of this research provide advanced probability-based methods adapted to the corrosion field. Application of the proposed methods enhances the prediction and remediation of localized corrosion processes, especially MIC.
    Effektiv start/slut dato29/10/1807/01/20


    • Memorial University of Newfoundland (Projektpartner)
    • VIA (leder)


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