Open Collections

Induced seismicity and corrosion vulnerability assessment of oil and gas pipelines using a Bayesian belief network model

University of British Columbia

A substantial amount of oil and gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. Because of the adverse environmental impact and significant financial losses, the integrity of these pipelines is essential. British Columbia Oil and Gas Commission (BCOGC) has indicated metal loss due to corrosion as one of the primary causes of pipeline failures. Therefore, it is important to identify pipelines subjected to severe corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this thesis presents a Bayesian belief network (BBN)-based probabilistic corrosion hazard assessment approach for oil and gas pipelines. A cause-effect BBN model has been developed by considering various types of information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network in order to estimate corrosion defects and the associated probability of failure. Besides corrosion hazard, BCOGC has reported multiple cases of anthropogenic seismicity, which also may compromise the pipeline integrity. To this end, this thesis explores the potential impact of induced seismicity on the oil and gas pipeline infrastructure. Spatial clustering analysis is used for earthquakes, previously registered in the region, to delineate areas, which are particularly prone to the induced seismicity. The state of the art ground motion prediction equation for induced seismicity is applied in a Monte Carlo simulation to obtain a stochastic field of the seismic intensity. Based on the established seismic fragility formulations for pipelines and mechanical characteristics as well as corrosion conditions, spatial and probabilistic distributions of the repair rate and probability of failure have been obtained and visualized with the aid of the Geographic Information System. The proposed model can help to identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision making process. To demonstrate the application of the proposed approach, two case studies for the Northeastern British Columbia oil and gas pipeline infrastructure are presented.

Text

2016-04-13

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/57569

Civil Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/