UCLA

Pipeline integrity management refers to an approach of understanding and operating pipelines in a safe and reliable manner. In this work, firstly, a probabilistic predictive model for internal corrosion of natural gas pipelines subject to aqueous CO2/H2S environment has been proposed. The model regards uniform and pitting corrosion as two main corrosion mechanisms and has been calibrated with the experimental data in a deterministic framework. Methodologies of simulating and accounting for temporal and spatial variabilities of operating parameters have been proposed and applied to the model for field applications. The model has been validated against field data from eight wet gas gathering pipelines in a probabilistic framework. Secondly, a reinforcement learning (RL)-based maintenance scheduler has been proposed for pipeline maintenance optimization problems by leveraging the proposed predictive corrosion model and the Q-learning and Sarsa algorithms. A case study has shown the superiority of the proposed maintenance scheduler over the periodic maintenance policy in reducing the maintenance costs. Finally, the previous two parts of work have been integrated into a pipeline system integrity management software featuring pipeline health monitoring, corrosion prognosis, system-level failure analysis, sensor placement optimization, and inspection/maintenance optimization. A case study has been provided to demonstrate the capabilities of the software.