Frequent corrosion perforation and monitoring effectiveness deviation have been observed in the undulating segments of high-water-cut crude oil gathering pipelines. This study proposes a corrected model and charts for in-pipe probe monitoring effectiveness (defined as the ratio of probe-measured corrosion rate to the circumferential maximum wall corrosion rate at the same cross-section). Computational fluid dynamics (CFD) simulations and loop experiments were conducted to systematically investigate the effects of flow velocity (0.4–1.2 m/s), temperature (20–60 ℃), and monitoring time on probe effectiveness. The results show that monitoring effectiveness exhibits a “rise-then-fall” trend with increasing flow velocity, reaching 72.88% at 0.8 m/s and decreasing to 50.10% at 1.2 m/s. Elevated temperatures gradually reduce effectiveness, from 74.63% at 20 ℃ to 67.00% at 60 ℃. With prolonged monitoring time, effectiveness significantly increases, rising from 5.15% at 12 h to 76.48% at 36 h. Based on these findings, a corrected corrosion rate model coupling velocity, temperature, and time was established, and charts illustrating corrected effectiveness under different conditions were generated. Field application indicates that the corrected probe monitoring effectiveness reaches 97.36% and 93.83% in the upward-inclined and downward-inclined segments, respectively, with an average improvement of 35.72%. The results provide effective technical support for corrosion monitoring and integrity management in complex undulating pipelines.

Internal corrosion is a major threat to the safety of natural gas pipelines, with defect geometry—depth, length, and width—playing a critical role in structural integrity assessments. While corrosion depth prediction has been widely studied, systematic probabilistic modeling of defect length and width remains limited. This study develops a hierarchical Bayesian-Markov Chain Monte Carlo (HB-MCMC) framework to jointly predict corrosion defect dimensions from in-line inspection (ILI) data. The framework integrates non-centered parameterization and adaptive sampling to improve inference efficiency and employs a hierarchical dynamic thresholding procedure for robust data preprocessing and outlier filtering. Field data from two transmission pipelines in Southwest China, comprising 1845 defect records, are analyzed. Results demonstrate that defect length and width both increase with depth, with width exhibiting stronger sensitivity. Model diagnostics confirm convergence and reliable uncertainty quantification. To further explore underlying mechanisms, OLGA multiphase flow simulations are combined with statistical predictions, providing flow-parameter profiles along the pipelines and enabling correlation analysis between local hydrodynamics and defect geometry. The proposed framework not only enhances predictive capability for defect length and width but also provides new insights into flow-corrosion interactions under real operating conditions, offering a reproducible and data-driven tool for corrosion assessment.