Modified Failure Rates for Safety Instrumented Systems based on Operational Experience from the Oil and Gas Industry
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In the oil and gas industry, safety instrument systems (SISs) are designed to ensure production safety, reduce risk and prevent major accidents. SISs should be demonstrated the fulfillment of specified safety requirement by appropriate reliability analysis with particular interest on the types of faults and how often they occur for various SIS equipment. Such operational data provides a basis for reliability quantification for each safety instrumented function (SIF), which is needed to demonstrate that safety integrity level (SIL) has been achieved. Review of operational data indicates that similar equipment can experience different failure rates, even it installed in similar environments. This variation on reliability performance can be explained by inventory- and operational parameters. The main objective of the thesis is to propose an approach to identify the most important parameters based on data analysis, and suggest their relative influence on reliability performance of installed equipment. The result can be used to modify failure rates if significant parameters are identified. Modified failure rates enables the reliability analyst to more precisely quantify the reliability performance in SIL follow-up phases and predict the variations of reliability performance for new facilities, where changes in inventory- and operational conditions can be forecasted. Statistical methods for analyzing inventory- and operational parameters are employed in the project, where shutdown valves have been considered in particular. The data analysis results illustrate the strong relationships between reliability performance and some parameters of the shutdown valves, e.g. sizes, leakage requirement and flow medium. Failure analysis is also performed to explain and verify the results. Modified failure rates of the shutdown valves for a new facility are established as an example in this thesis.In short, the thesis proposes an approach to identify important parameters for reliability performance and modify failure rates based on operational data from the oil and gas industry. The work identifies a number of challenges and limitations in the approach and suggests considerable further work.