Dynamic Bayesian belief network for long-term monitoring and system barrier failure analysis: decommissioned wells

• Main Authors: Fam, Mei Ling, He, Xuhong, Konovessis, Dimitrios, Ong, Lin Seng
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Offshore Decommissioning; Bayesian Belief Networks
• Online Access: https://hdl.handle.net/10356/164856
• Institution: Nanyang Technological University

There is increasing interest to consider dependent failures and human errors in the offshore industry. Permanently abandoned wells dot most of the subsea environment. The nature of a well plugging and abandonment (Well P&A) run - usually the lowest-cost contractor engaged to plug several wells tapping the same reservoir makes it an ideal case study for incorporating failures based on common causes. The heavy use of operators during a cementing job also provides the case for analysis of human error in such tasks. One proposed method to analyse the above-mentioned is the use of Bayesian Belief Networks to achieve the following objectives (1) to capture better estimates of a well PA event by incorporating dependencies, and meet regulatory requirements by authorities; and (2) to use the same model to provide long term monitoring of a group of wells linked by common dependencies. This model has not only captured the dependencies of multiple variables, but also projected it in a dynamic manner to provide a risk profile for the next decade where well integrity failure is likely to happen. • Proposed adapted method capture better estimates of a well PA event by incorporating dependencies • Method allows for extension of model to long term monitoring of a group of wells linked by common dependencies.