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

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.Economic Development Board (EDB)Nanyang Technological UniversityPublished versionThe authors would like to acknowledge the support of the Lloyd’s Register Singapore, Vysus Group (Stockholm, Sweden), Nanyang Technological University, Singapore Institute of Technology and the Singapore Economic Development Board (EDB) under the Industrial Postgraduate Program in the under-taking of this work