Dynamic risk-based integrity management of subsea pipelines

Subsea pipelines are widely used across the globe for transportation of large quantities of hydrocarbons from offshore wells to onshore locations, playing an important role in procurement of fuel for power generation and transport. Although the recorded failure rates in oil and gas subsea pipelines are relatively lower than pipelines in other facilities such as water distribution or wastewater collection systems, a damaged subsea pipeline has significant environmental risk due to the hazardous properties of hydrocarbons and inaccessibility of the facilities. The loss of asset integrity may also delay production resulting in significant financial consequences for the industry operators. To prevent this, frequent inspections and maintenances are essential. Companies however may incur substantial costs to perform regular maintenance activities in such environment with limited accessibility. This Ph.D. thesis aims at providing a risk-based integrity management framework for maintenance scheduling of subsea pipelines. This method, unlike previous approaches that were mostly concerned with accidents resulting from component failures, focuses on structural deterioration due to fatigue and corrosion phenomena. In this research, advanced probabilistic techniques such as Bayesian Network (BN) are adopted to account for the uncertainty of parameters that are influential in the problem. By estimating the life of a structure and evaluating the consequences of failure, the proposed framework provides a cost-effective maintenance planning solution that ensures a safer and more reliable operation. In the first stage, this study develops a probabilistic methodology for modelling the useful life of a subsea pipeline degraded by corrosion-fatigue damage. Deterioration of the pipeline, in the presence of corrosive agents and cyclic loads, starts with nucleation of corrosion pits which continue to grow in size. These defects can provide the required condition for initiation of fatigue damage and the growth of cracks which may lead to fracture. The entire process is influenced by many factors such as material and process conditions, each incorporating a high level of uncertainty. This methodology presents an integrated Dynamic Bayesian Network (DBN) model, incorporating the temporal nature of the damage process and its varying growth rates. It is observed that the established model is an efficient tool for predicting the fatigue life of subsea pipelines. This is mainly due to the computational efficiency of BN in considering a large number of parameters and its updating capacity that enables the inclusion of monitoring results in the analysis. A fracture in a subsea pipeline may result in an underwater release of hydrocarbons with detrimental impacts on the species in the sea environment. United States Environmental Protection Agency (USEPA) provides an Ecological Risk Assessment (ERA) framework for analyzing risks involved in such accidents. The present study attempts to develop a novel methodology, based on this framework, to predict the exposure concentration of contaminants in a marine environment. For this purpose, the stochastic fate and transport of spilled oil is estimated using a level IV fugacity model. A hierarchical Bayesian approach (HBA) is adopted to estimate the probability distribution of time to reach a concentration, based on the observational data. The estimated times can be utilized for the preparation of contingency and remediation plans by operation and safety managers. Probability distributions are also developed for the exposure concentration in different media in contact with oil (e.g. water column, sediment), using the results of fugacity model. To establish a Bayesian-based ERA methodology for accidental release of oil in marine environment, the 95th percentile of Predicted Exposure Concentration (PEC95%) is used, along with 5th percentile Predicted No Exposure Concentration (PNEC5%). The model incorporates causal effects from the likelihood of each event (e.g. exposure to contaminant) into the risk assessment methodology. Additionally, the presented method uses a DBN model for including the seasonal effects on the ecological risk profile. Upon obtaining the likelihood of fatigue failure and estimating the associated risk with these accidents, a dynamic risk-based methodology is developed for maintenance planning of deteriorating subsea pipelines. The established DBN model of the deterioration process is extended to an Influence Diagram (ID) for identifying the optimum decision alternative, being possible maintenance actions (e.g. continuing operation, repairing the structure). The presented model is able to consider the consequences of failure as well as the cost of each decision alternative, while estimating the expected utilities. Observation of damage state is added into the model to improve the reliability of predictions and efficiency of the decision making process. This methodology can assist asset managers to select the optimum approach for mitigating the consequences of failure while minimizing the maintenance costs. This thesis overall attempts to provide a comprehensive source of knowledge and technique to form a better understanding of the failure of subsea pipelines and associated consequences due to deterioration processes. It will assist in ensuring a safer and more reliable operation of these structures through a more efficient maintenance planning approach.