Abstract
Corrosion of carbon steel infrastructure in the oil and gas industry can occur via a variety of chemical, physical, and/or microbiological mechanisms. Although microbial corrosion is known to lead to infrastructure failure in many upstream and downstream operations, predicting when and how microorganisms attack metal surfaces remains a challenge. In crude oil transmission pipelines, a kind of aggressive corrosion known as under deposit corrosion (UDC) can occur, wherein mixtures of solids (sands, clays, inorganic minerals), water, oily hydrocarbons, and microorganisms form discreet, (bio)corrosive sludges on the metal surface. To prevent UDC, operators will use physical cleaning methods (e.g., pigging) combined with chemical treatments such as biocides, corrosion inhibitors, and/or biodispersants. As such, it necessary to evaluate the efficacy of these treatments in preventing UDC by monitoring the sludge characteristics and the microorganisms that are potentially involved in the corrosion process.
The efficacies of a biocide, corrosion inhibitor, and biodispersant being used to prevent microbial corrosion in a crude oil transmission pipeline were evaluated. A combination of various microbiological analyses and corrosivity tests were performed using sludge samples collected during pigging operations. The results indicated that the combined treatment using inhibitor, biocide 1 and biodispersant was the most effective in preventing metal damage, and both growth-based and Next-Generation Sequencing approaches provided value towards understanding the effects of the chemical treatments. The efficacy of a different biocide (#2) could be discriminated using these test methods.
The results of this study demonstrate the importance of considering and monitoring for microbial corrosion of crucial metal infrastructure in the oil and gas industry, and the value of combining multiple lines of evidence to evaluate the performance of different chemical treatment scenarios.
Do Oil and Gas Risk Factors Matter in the Malaysian Oil and Gas Industry? A Fama-MacBeth Two Stage Panel Regression Approach