Statistical Analysis of Failure Incidence and Pipeline Repair Planning Based on In-Line Inspection Data

Abstract

Abstract

This article presents a seven-year empirical analysis (2019–2025) of an in-line inspection (ILI) dataset from an Arctic gas-gathering network. The work integrates failure-incidence statistics with probabilistic risk-based modelling to quantify the latent risk contained in the backlog of deferred category B defects, an analytical step undocumented for deep-reservoir permafrost infrastructure. The relevance stems from aging pipeline assets, the operating conditions of gas-gathering systems linked to deep high-pressure gas-condensate reservoirs in the Arctic cryolithozone, and the limits of deterministic approaches insensitive to instrumental error and variability in metal-degradation rates. The aim is to refine repair-planning approaches based on empirical ILI data. The novelty lies in the synthesis of a seven-year diagnostic dataset (380 defects: 40 category A and 340 category B) with probabilistic risk-based maintenance models. Elastic-plastic bends dominate the population (82%) alongside carbon-dioxide corrosion. Across 2019–2025 the cumulative elimination rate reached 80% for category A (32 of 40) and 6.8% for category B (23 of 340), leaving 317 unresolved anomalies. Monte Carlo projections indicate 5.1% of this backlog will exceed the 300 mm deflection threshold within three years and 10.0% within five years. Bayesian analysis flags 20 defects as misclassified through ILI tool uncertainty alone.