TY - CONF A1 - An, Biwen Annie T1 - Standardization of MIC laboratory testing: with a special focus on corrosive methanogens N2 - Microbiologically influenced corrosion (MIC) is a highly unpredictable process involving a complex group of microorganisms, including sulfate-reducing bacteria and methanogens. Standard laboratory MIC testing using static serum bottle enrichments is an easy but limited method, offering poor resolution on the biomineralization process of corrosion products. An example of this is the presumed corrosion product siderite by corrosive methanogens (Mi-MIC). Previous publications reported siderite was the sole corrosion product of M. maripaludis using metal coupons incubated under stationary conditions. However, the formation of siderite is closely related to the surrounding environmental conditions, i.e. pH, CO2 concentration, flow and temperature. Thus, siderite as the sole corrosion product of Mi-MIC remain inconclusive and questionable. To study Mi-MIC effectively, a novel versatile multiport flow-column corrosion monitoring system (MFC) was developed. MFC allows sectional corrosion rate determination under flow conditions using different types of material, inoculum and packing material. MFC offers great flexibility, ease of operation and accurate corrosion measurements that can be combined with many other techniques. Using MFC, we studied multiple strains of methanogens and compared it with sulfate-reducing bacteria under neutral and low pH conditions. It was revealed by MFC that corrosive methanogens have equally high corrosion potential as sulfate-reducing bacteria. Additionally, siderite is not the dominant nor sole corrosion product of Mi-MIC. Thus, effective corrosion monitoring and establishing standard laboratory practices, i.e. incorporating MFC as part of regular testing process, will provide deeper understanding of MIC. This will allow further microbial electrophysiology understandings, contributing to effective mitigation strategy development. T2 - EUROPEAN MIC NETWORK WEBINAR CY - Online meeting DA - 19.05.2020 KW - MIC KW - Methanogen KW - FIB/SEM KW - Corrosion products KW - Microbiologically influenced corrosion KW - Flow Model KW - Modelling KW - Korrosion PY - 2020 AN - OPUS4-51554 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abilio, André T1 - Expert System for Screening Microbiologically Influenced Corrosion (MIC) as Internal Failure Cause in Oil and Gas Upstream Pipelines N2 - The analysis of pipeline failures due to Microbiologically Influenced Corrosion (MIC) is challenging due to the complex interaction of many influencing parameters including pipeline operation conditions, fluid chemistry and microbiology, as well as the analysis of corrosion features and products. To help address this challenge, an expert system was developed to assist non-specialists in screening internal pipeline corrosion failures due to MIC related threats. To accomplish this, 15 MIC subject matter experts (with a total of 355 man-years of accumulated MIC based experience) were recruited to evaluate a total of 65 MIC failure cases based on real-life scenarios. These case study parameters and the expert elicited results were input into an Artificial Neural Network (ANN) model to create a model system which can screen whether a given failure scenario is one of three outcomes: a) failure is likely due to MIC, b) failure is likely not due to MIC, or c) the conclusion is inconclusive (analysis needs more data/information). The model system had an overall accuracy of 74.8% and it showcases that knowledge from subject matter experts can be captured in a reasonably effective way to screen for possible MIC failures. Based on that, this presentation will provide details of the model development process and key results to date. Important considerations regarding the level of confidence of the diagnoses and variation between expert opinion will also be discussed alongside with ideas on how to improve the model for field applicability. T2 - ISMOS 9 CY - Edinburgh, United Kingdom DA - 27.06.2023 KW - Expert System KW - Microbiologically Influenced Corrosion (MIC) KW - Oil and Gas Upstream KW - Failure Investigation KW - Artificial neural networks (ANN) PY - 2023 AN - OPUS4-64603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Abilio, André T1 - Working Group 3 (WG3) Inputs N2 - During its first two years, the [WG3 of] COST Action CA20130 (Euro-MIC)progressed the achievement of this as described below Working group 3(WG3) increased collaboration in the field of microbiologically influenced corrosion (MIC) not only by partnering up with other CA20130 WGs to deliver MIC dedicated training sessions, but also by having regular meetings with its members on sensors and technologies for MIC monitoring. Through such online and in-person meetings, members from different countries and different technical backgrounds were brought together for an open share of knowledge and mutual benefit where gaps between microbiology and corrosion were bridged. KW - Monitoring KW - Sensor KW - Microbiologically Influenced Corrosion PY - 2023 SP - 1 EP - 7 PB - COST Action AN - OPUS4-64604 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abilio, André T1 - Expert System for Screening Microbiologically Influenced Corrosion asInternal Failure Cause in Oil and Gas Upstream Pipelines N2 - The analysis of pipeline failures due to Microbiologically Influenced Corrosion (MIC) is challenging due to the complex interaction of many influencing parameters including pipeline operation conditions, fluid chemistry and microbiology, as well as the analysis of corrosion features and products. To help address this challenge, an expert system was developed to assist non-specialists in screening internal pipeline corrosion failures due to MIC related threats. To accomplish this, 15 MIC subject matter experts (with a total of 355 man-years of accumulated MIC based experience) were recruited to evaluate a total of 65 MIC failure cases based on real-life scenarios. These case study parameters and the expert elicited results were input into an Artificial Neural Network (ANN) model to create a model system which can screen whether a given failure scenario is one of three outcomes: a) failure is likely due to MIC, b) failure is likely not due to MIC, or c) the conclusion is inconclusive (analysis needs more data/information). The model system had an overall accuracy of 74.8%and it showcases that knowledge from subject matter experts can be captured in a reasonably effective way to screen for possible MIC failures. Based on that, this presentation will provide details of the model development process and key results to date. Important considerations regarding the level of confidence of the diagnoses and variation between expert opinion will also be discussed alongside with ideas on how to improve the model for field applicability. T2 - ISMOS 9 CY - Edinburgh, United Kingdom DA - 27.06.2023 KW - Expert System KW - Microbiologically Influenced Corrosion (MIC) KW - Oil and Gas Upstream KW - Artificial neural networks (ANNs) KW - Failure analysis PY - 2023 AN - OPUS4-64602 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -