Cost Efficient MIC Management System Based on Molecular Microbiological Methods

Abstract

Abstract The tools available for microbiological surveillance of oil field systems have significantly improved during the last decade. The introduction of molecular microbiological methods (MMM) has made it possible to reliably monitor the distribution of microorganisms involved in MIC. Thus, the limiting factor in MIC surveillance is no longer the quality of the microbiological data, but the conversion of these into a reliable risk assessment. Here, we describe a model used to perform such a conversion. The model calculates a MIC risk factor as well as worst-case pitting corrosion rates. The calculations are based on numbers of MIC-causing microorganisms measured by quantitative PCR (qPCR), reactions and stoichiometries for the electron flow at the metal surface, and empirically determined cell-specific reaction rates. The microorganisms included in the model are sulfate-reducing prokaryotes (SRP) and methanogens since these microbial groups are known to cause enhanced corrosion rates on steel surfaces. The application of the MIC model is demonstrated through field cases from the Danish Sector of the North Sea. The field cases show how MMM-based surveillance in combination with a suitable model for MIC risk assessment allows the Operator to take timely precautions in order to prevent production failures due to MIC.