Open Access

This study highlights the modeling of an expert system for the classification of internal microbiologically influenced corrosion (MIC) failures related to pipelines in the upstream oil and gas industry. The model is based on artificial neural networks (ANNs) and involves the participation of 15 MIC experts. Each expert evaluated a number of model case studies ranging from MIC- to non-MIC-driven upstream pipeline failures. The model accounts for variations in microbiological testing methods, microbiological sample types, and degradation morphology, among other variables. It also accounts for missing datasets, which is commonly the case in actual failure assessments. The outcome is an expert system model whose outputs are classes (classification ANN) which comprises MIC potential and data confidence. The performances of two approaches are contrasted in this study. One classifies the output in 5 classes, a 5-output classification (5OC) model; the other in 3 classes, a 3-output classification (3OC) model. The 5OC model had an accuracy of 62.0% while the simpler 3OC model had a better accuracy of 74.8%. This modelling exercise has demonstrated that knowledge from experts can be captured in a reasonably effective model to screen for possible MIC failures. It is hoped that this study not only may contribute to a better understanding of the prevalence of MIC in the oil and gas sector, but also may highlight the key areas necessary to improve the diagnosis of MIC failures in the future.

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.

Underground hydrogen storage (UHS) is a strategic step towards implementing the hydrogen economy. Achieving the required infrastructure by 2050 necessitates advancements in hydrogen-dedicated assets and the evaluation of existing infrastructure. The unique conditions in UHS require an experimental set-up to simulate UHS operating conditions, which allows to assess the readiness of current storage and transmission lines for hydrogen, and develop new technologies for material-resistance, operational-simulations, and risk-assessments. Currently, UHS-experiments for microbiologically-influenced-corrosion (MIC) are performed in standard autoclaves with relatively high volumes/pressures; which do not allow for an extensive evaluation of the system over the experiment, but rather their initial and final conditions. To overcome such limitations, the novel UHS-simulation-set-up developed is designed in a way that (1) it allows for liquid addition during the test, enabling the study of biocides or the evaluation of operating setups, as well as (2) it permits liquid/-gas sampling during the test, allowing for more efficient monitoring of testing conditions and a better understanding of the process over time. Additionally, a low-pressure-release function is added. It prevents degradation of polymers, corrosion products, microorganism during after the simulation is complete. Therefore, preserving the integrity of samples for subsequent evaluation, which otherwise would not be possible.

Monitoring of microbiologically influenced corrosion (MIC) is essential to prevent costly damage to infrastructure, ensure safety, and maintain operational efficiency. MIC can lead to accelerated degradation of materials, particularly in water systems, pipelines, and storage tanks. Ideally, effective MIC monitoring methodologies should be low-cost, minimally labor-intensive, and capable of generating actionable insights to ensure feasibility. In practice, a range of techniques are available, each with distinct strengths and limitations. Coupons are widely used to assess physical deterioration of steel surfaces, offering tangible and visual evidence of corrosion progression. Electrochemical methods, such as linear polarization resistance or electrochemical impedance spectroscopy, provide insights into corrosion kinetics and metal integrity. Chemical monitoring can detect shifts in redox conditions or the presence of corrosive metabolites. Meanwhile, biological approaches, such as qPCR, next-generation sequencing, or microbial culturing, offer detailed information on the microbial communities driving MIC. A comprehensive MIC monitoring campaign must integrate multiple lines of evidence to provide a full diagnostic of the local corrosion environment. This includes not only surface-level observations but also the monitoring of the bulk environment (e.g., water chemistry, nutrient availability, and microbial load), surface evolution (e.g., biofilm formation and scaling), and the impact on metal integrity over time.

The increasing developments to stablish a hydrogen-based economy to enable renewable energies also increase the demand for hydrogen storage and transmission. Consequently, technological advancements in underground hydrogen storage (UHS) (e.g., salt caverns, saline aquifers, oil and gas depleted fields), and the repurpose of natural gas transmission lines for hydrogen applications are currently being investigated. Hydrogen is an energy source which is easy for microorganisms to utilize, and its ready availability to microorganisms in hydrogen systems poses a major question: how do the microorganisms present in storage and distribution systems interfere with the concentration of hydrogen and the hydrogen uptake to the metal – which may result in loss of mechanical integrity and lead to leaks and catastrophic failures? To address this question, a ‘hydrogen biocharging test’ (HBC) is proposed. The HBC consists of exposing metallic samples (e.g., API 5L X80) to biological media (e.g., methanogenium medium 141, artificial seawater) under a hydrogen-containing atmosphere (80% H2 / 20% CO2). Microorganisms are injected in the medium and incubated for up to 28 days under 30°C -37°C. The incubation will enable biofilm formation on the metal surface which will work as the interface for hydrogen charging (biocharging). Two microbiological strains are used in the study: Methanococcus maripaludis S2 (methanogenic archaeon); and Desulfovibrio ferrophilus IS5 (sulphate-reducing bacterium). Flat tensile specimens are used for mechanical characterization, and flat squared coupons to test for hydrogen permeation via the Devanathan-Stachurski Permeation Cell; hydrogen penetration via carrier gas hot extraction (CGHE); and biofilm formation via scanning electron microscopy (SEM). The goal of the study is to show if microorganisms enhance or hinder hydrogen uptake and consequently hydrogen embrittlement; which groups of microorganisms pose higher influence on hydrogen uptake, and how environmental conditions (e.g., temperature, pH, salinity, hydrogen content) affect the behaviour of microorganisms with regard to hydrogen uptake.