The Reliability of Offshore Jacket Platforms Based on Bayesian Calibration

Abstract

The safety of offshore structures is a key topic in developing offshore oil and gas and offshore wind energy. Due to the harsh offshore environment and costly offshore field tests, offshore field trials to validate the theoretical models for offshore structures are limited, and testing results can rarely be found in the public domain. The Bayesian updating technique combines existing engineering knowledge with the observed performance data about in-service offshore structures to update model uncertainties. Hence, the Bayesian technique overcomes the shortcomings of limited offshore field trials. This paper compiles performance data on offshore jackets in hurricanes in the Gulf of Mexico (GoM) in the past two decades and calibrates the model uncertainties of the API method using the Bayesian technique. With the updated model uncertainty, this paper evaluates the reliability of generic offshore jackets in the GoM and the case study’s offshore wind substation in China. With a typical reserve strength ratio (RSR) of about 2.0 to 2.2, the reliability analysis reveals that the updated annual failure probability of a generic offshore jacket in the GoM is largely less than 1.0×10−3, indicating that the extreme weather overload is not a major concern. However, the RSR of the case study platform in China is greater than 4.5, and the annual failure probability for the case study offshore wind substation is about 2–3 orders of magnitude lower than typical oil and gas jackets. Hence, from the extreme metocean condition perspective, the substation under investigation has sufficient structural capacity, and the design practice for offshore wind substations in northern China may be improved.

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