Work

Dynamic Bayesian Belief Network for long-term monitoring and system barrier failure analysis: Decommissioned wells

Year: 2021

Type: article

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.

Source: MethodsX

DOI: 10.1016/j.mex.2021.101600

Authors: Mei Ling Fam, Xuhong He, Dimitrios Konovessis, Lin Seng Ong

Institutions: Singapore Institute of Technology, University of Strathclyde, Nanyang Technological University

Language: English

Topic: Risk and Safety Analysis

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $700

Funders: Nanyang Technological University, Lloyd's Register