An experimental corrosion test and mechanistic modelization of microbiologically influenced corrosion (mic) under the variation of carbon source content

ROBLEDO, ALEJANDRO; ESCALADA, LISANDRO; BUSALMEN, JUAN P.; MASSAZZA DIEGO

Los Cocos, Córdoba

Congreso; XVII Congreso Argentino de Microbiología General (SAMIGE); 2022

SAMIGE

Microbiologically influenced corrosion (MIC) is estimated to account for 20% of total corrosiondamages. Because sulfate exists in many environments, biofilms formed by Sulfate anaerobicreducing bacteria (SRB) are responsible for MIC. In oilfield water flood systems, SRB cause the largestnumber of recorded instances of corrosion problems. Current risk-factor probability models areuseful for predicting the MIC likelihood. However, a reliable prediction of the progression of MICpitting and the subsequent changes between sessile (biofilms) and planktonic (motile) bacterialpopulations under different nutritional conditions observed in the oil field has not been reported.Most of the models utilize only a specific strain of bacteria to simulate the corrosion but in order to reflect more realistic environmental conditions such as those observed in the oil field a complexenvironment with hundreds of microorganisms must be considered. This work presents anexperimental/theoretical approach to achieve this complexity and utilized a consortium of SRB. Inthis work, a mathematical model was developed to investigate the interactions between SRB biofilmsand a metallic surface under different nutritional conditions (absence and presence of organicmatter) for a 21-day period. The above experimental conditions were carried out in order to collectcorrosion test data to calibrate the model. Lactate consumption (organic matter) or iron as electrondonors and sulfate as electron acceptor were considered as SRB metabolism. The distribution andconsumption of organic matter, sulfate and bacterial growth were based on the Fick’s Law andMonod equation. A growth limitation hypothesis was implemented by deactivating the sessile cellsduplication upon a growth threshold and by reducing the metabolic rate inversely related to thedistance of the metallic surface. The model was developed in the free programming languagePython, using a hybrid differential-discrete approach. The output of the model accurately predictedparameters such as colonization, growth of a sessile and planktonic population, and corrosion of the steel surface. Computer simulation indicated that under the set conditions, the presence of organic matter favored the free-living planktonic state over the biofilm state(sessile) in contrast to that predicted for starvation conditions where the planktonic bacteria are unfavored. While the corrosion rate increased significantly in the presence of organic materials, the maximum pit depths calculated by the model did not indicate any significant changes across nutritional circumstances.