Identification of failure type in corroded pipelines: a bayesian probabilistic approach

Area of Science:

• Engineering
• Materials Science
• Risk Management

Background:

• Pipeline failures pose significant environmental and safety risks, especially in the oil and gas sector.
• Corrosion exacerbates pipeline integrity issues, increasing the likelihood of hazardous material spills.
• Predictive models are crucial for effective risk evaluation and maintenance planning.

Purpose of the Study:

• To develop a Bayesian probabilistic approach for predicting failure types (leakage vs. rupture) in steel pipelines.
• To model pipeline behavior under realistic environmental corrosion conditions.
• To enhance safety and inform maintenance policies in the petroleum and gas industries.

Main Methods:

• A two-step modeling process was employed, first analyzing mechanical performance to define failure conditions.
• Experimental burst tests were used to establish a probabilistic boundary for uncertain failure regions.
• A probabilistic model treated failure events as random variables, with parameters estimated from experimental data.

Main Results:

• The proposed Bayesian model demonstrated good discrimination capacity in predicting failure types.
• Model validation against a real catastrophic event confirmed its predictive accuracy.
• The approach effectively identifies conditions leading to leakage or rupture in corroded pipelines.

Conclusions:

• The Bayesian probabilistic model offers a robust framework for assessing and predicting pipeline failures.
• Findings support the development of targeted inspection and maintenance policies for large-scale oil and gas pipelines.
• This predictive capability is vital for mitigating risks associated with pipeline corrosion and material degradation.