Modeling of dependent metal loss corrosion defects for oil and gas pipeline system

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The corrosion defects in the system due to metal-loss corrosion can cause failure and economic loss, and interrupt the normal operation and delivery of the energy products. The evaluation and modeling of degradation, such as the depth of corrosion defect, is of importance for oil and gas pipeline industry. Since the growth of the dimension of corrosion defects could be subjected to similar operating environmental conditions, and the uncertainty in material properties may be related to the same manufacture and construction process, the depth of the corrosion pits could be partially correlated. The partial correlation can be dealt with by considering that the marginal distribution of each defect depth is known and the correlation structure is controlled by an arbitrarily selected copula function. The justification of using a particular copula may is not straight forward. Moreover, it may not provide a time varying dependency. The proposal is to investigate the effect of dependent stochastic degradation of multiple corrosion defects on the estimated reliability of pipeline systems. For the investigation, we consider that the time varying depth of each corrosion defect is caused by several independent stochastic processes. Each of the growth processes is represented by random process such as the Geometric Brownian motion or gamma process. Some of the processes could be common for many defects while others are independent for each corrosion defect because of dissimilar environmental conditions and material properties. This consideration results in that the degradation of the corrosion defects is partially correlated. The long term objective is to extend the proposed study by considering risk assessment of oil and gas pipeline systems and optimal condition-based multiple maintenance decisions, including the potential development of geographical information system based decision support system, so the decision makers and engineers can benefit directly from the research results. The funding from NSERC for this project is to be used to support a post-doctoral fellow for six months.