MATHEMATICAL MODELING FOR PREDICTIVE BIOFILM-INDUCED STRESS CORROSION ON OIL PIPELINES

Abstract

Microbiologically influenced corrosion (MIC) in oil pipelines continues to pose a significant risk to energy infrastructure and public safety due to the intricate biofilm patterns and mechanical forces acting on them. In this work, we propose a spatially resolved predictive model that integrates laboratory assays with field inspection data for pitting prognosis that can be prevented by mitigation measures and is justifiable through experimental data. How? A coupled system of nonlinear partial differential equations is formulated for substrate diffusion, biofilm growth, detachment, and interaction with stress–corrosion at the surfaces of carbon steels. Laboratory boundary conditions were defined by linear polarization resistance and optical coherence tomography (OCT) biofilm thickness measurements in flow loops. Field parameters were obtained from inline inspection (ILI) data with ultrasound and electromagnetic sensors. An adaptive finite-element mesh for spatial heterogeneity in shear stresses (0.1–2 Pa) and flow velocities (0.5–2 m/s) was constructed in COMSOL Multiphysics and MATLAB. Model parameters (diffusivity, growth kinetics, some thresholds for detachment, and stress-intensity factors) were calibrated using Bayesian regression and global sensitivity analysis against empirical measurements of pit depth. Simulations demonstrate that spatially heterogeneous biofilm patches increase the rate of localized corrosion: high shear areas increased peak current density by 38 % and delayed biofilm maturation by more than 35%. The model, using calibrated parameters, demonstrated a field corrosion rate prediction with a root-mean-square error of 0.72 mA/cm². Incorporating sophisticated mathematical modeling with bench-scale and field data enables our tool to provide real-time risk assessments and insights, allowing for the optimization of pigging, biocide application, and maintenance scheduling. This approach sets a new vision for the future of pipeline operations by making them safer and more resilient.