Model for microbiologically influenced corrosion potential assessment for the oil and gas industry

Abstract

Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.

An overview of maintenance management strategies for corroded steel structures in extreme marine environments

“…Various researchers have reported that the DO concentration in seawater changes with regional surface seawater temperature from about 8.0 mL/L in the Arctic seas to 4.5 mL/L or even less in the tropical seawater. In certain harbour conditions, it further reduces due to the presence of nutrient-rich waters, pollutants and industrial wastages [156][157][158]. Figure 5 illustrates the relationships between various seawater parameters.…”

Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks

“…in Dugan, 2005, 2006;Montani et al, 2008;Kabir et al, 2018). BN-based approaches have been used for hazard analysis in process industries such as in (Khakzad et al, 2011;Yazdi and Kabir, 2017;Deyab et al, 2018;Taleb-Berrouane et al, 2018).…”

A Review of Characterization and Quantification Tools for Microbiologically Influenced Corrosion in the Oil and Gas Industry: Current and Future Trends

“…The screening factor metrics indicate the performance of the subnetworks of the system (design, mitigation) and allow the operator to assess the potential of MIC. This model, however, was verified to be accurate with data from a pipeline incident . Another approach used a simplified model for MIC and incorporated it into an extensive network of abiotic degradation processes for internal corrosion hazard assessment.…”