Abstract
Reports of corrosion failures in oil transportation pipelines implicating bacterial activity (in particular the activity of Sulphate-reducing Bacteria (SRB)) continue to appear in the literature and documented case histories. Whilst corrosion risk assessment tools are available for electrochemical corrosion rate influencers such as pH, CO2, H2S, etc. it has not been possible to produce a formulaic tool to quantify predicted pitting rates due to a Microbial Influenced Corrosion (MIC) risk. Assessing the risk of MIC in oilfield environments has been attempted using various strategies. Some risk assessments attempt to quantify MIC against bacterial numbers and activity whilst others only include bacterial growth and activity qualitatively. Whilst admirable attempts have been made and useful models have been developed, most authors recognise that their models provide no more than best guesses for predicting pitting rates. It is unlikely that a quantitative MIC model will be available in the near future and so in the meantime there is a requirement to further enhance the basic models to make them more relevant and develop practical tools to aid in the interpretation of monitoring data.
Often MIC has occurred despite the fact that biocide treatments have been dosed into the affected system. Upon close investigation, however, it is not uncommon to find that the treatment regime is erratic at best and in some cases may be considered haphazard. On the other hand, there are numerous reports of SRB contaminated pipelines where no significant MIC has been detected. The apparent paradox of SRB activity but no MIC can be explained by the requirement for abiotic influencers (oxygen, deposits, stagnation, etc.) in combination with SRB activity before MIC is initiated.
This paper presents tools to predict biocide performance and MIC which can also be utilized to interpret bacterial monitoring data from the field. Often the design of the pipeline and the constantly changing nature of the fluids being transported will severely impact upon the ability to maintain complete control of bacterial activity. The emphasis is, therefore, on prediction of MIC and monitoring of key performance indicators which allow assurance that problematic bacteria and associated abiotic influencers are controlled to sufficiently low levels to mitigate MIC.
