Preliminary results of a new tool to assess internal pipeline failures due to microbiologically influenced corrosion (MIC)

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 diagnosing internal pipeline corrosion failures due to MIC related threats. To accomplish this, 15 MIC subject matter experts were recruited to evaluate over 65 MIC failure case studies 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 tool which can predict whether a given failure scenario is one of three outcomes: a) likely due to MIC, b) likely not due to MIC, or c) inconclusive (analysis needs more data/information). 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.