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

Fam, Mei Ling and He, Xuhong and Konovessis, Dimitrios and Ong, Lin Seng (2021) Dynamic Bayesian belief network for long-term monitoring and system barrier failure analysis : decommissioned wells. MethodsX, 9. 101600. ISSN 2215-0161 (https://doi.org/10.1016/j.mex.2021.101600)

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Abstract

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.

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