Dynamic risk assessment of plugging and abandonment operation process of offshore wells based on Dynamic Bayesian Network. (15th February 2023)

• Record Type: Journal Article
• Authors: Wang, Chuan; Liu, Yuzhong; Lian, Xiaolong; Luo, Jianjun; Liang, Chunping; Ma, Haifeng
• Is Part Of: Ocean engineering. Volume 270(2023)
• Publication Date: 2023-02-15
• Subjects: Dynamic risk assessment -- Plugging and abandonment -- GO method -- Dynamic Bayesian network -- Riserless well intervention system; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.113625
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: The plugging and abandonment (P&A) operation is required when an offshore well is abandoned. This study proposes an analysis method based on dynamic Bayesian network (DBN) to assess the dynamic risk of riserless well intervention systems performing the P&A operation. First, each phase of P&A operation is depicted by a GO model. The fault tree (FT) model of each mission execution system is constructed to analyze the equipment factors leading to the failure of the mission. The mirror DBN model is established from the GO and FT models according to the mapping algorithm and the parameters of the DBN model are obtained by mapping according to the GO and FT model parameters. A complete risk assessment model is established. Next, the standardized plant analysis risk-human reliability analysis method is used to quantify the probabilities of human factors in the DBN model. Finally, the dynamic risk of the operation process is assessed. The developed DBN is verified using a method based on three axioms. Results show that the reservoir abandonment phase has the greatest effect on P&A operation process risk. Sensitivity analysis is performed to identify critical equipment of the system. The proposed method can be a useful tool to assess the dynamic risk of P&A operation process. Highlights: A DBN-based dynamic risk assessment method of the P&A operation process is proposed. Human error probability is calculated using the SPAR-H method. The degree of influence of equipment factors and human factors during mission execution is analyzed. The key phases, missions, components, and high-risk factors are identified. The developed DBN model is validated by using a method based on three axioms.