A comprehensive risk assessment method for Christmas tree combined with a multi source information fusion algorithm and an improved HAZOP method.

ABSTRACT: The Christmas tree faces significant safety risks during installation and requires extremely high reliability during operation. In this study, a comprehensive risk assessment method combined GA-BP neural network and improved HAZOP method is established, which is the first application used in the field of risk analysis for offshore oil equipment. The sample risk data during the installation and operation of the China's first domestically produced Christmas tree is extracted through multi-source fusion, and the possibility and severity levels of risk occurrence are classified and judged. By training and testing the model it shows that the average error rate of the proposed GA-BP multi-source information fusion method is only 5.10%. Through risk data extraction and evaluation, the failure pro

SUBMITTER: Wang Q 

PROVIDER: S-EPMC12768369 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature