Marvin Rausand

In the subsea oil and gas industry, new systems and new technologies are often met with skepticism, since the operators fear that they may fail and lead to production loss, costly repair interventions, and hydrocarbon leakages to the sea. Before a new system is accepted, the producer has to convince the operator that it is fit-for-use and has a high reliability. This is often done through a technology qualification program. An important part of the technology qualification program is to predict the failure rate of the new system in its future operational context. Identifying potential problems and estimating the failure rate at an early stage in the system development process are important owing to the high cost of design modifications later in the development process. This article presents a practical approach to reliability prediction of new subsea systems based on available operational data from similar, known systems from the topside environment and a comparison between the two systems. The application of the approach is illustrated by an example of a subsea pump.

Safety-instrumented systems (SISs) are often crucial in the prevention of accidents. In this study, several reliability assessment methods, including the approximation formulas in IEC 61508 and in the PDS handbook, and Markov and Petri net methods, are used to determine the reliability of a redundant SIS configuration with three parallel channels. The average probability of failure on demand (PFD _avg ) is used as reliability measure, and common cause failures and proof tests are considered in the modeling. The results obtained by the different methods are compared, and characteristics of these methods in SIS reliability analysis are discussed.

Reliability estimates play a crucial role in decision making related to the design and operation of safety-instrumented systems. A safety-instrumented system is often a complex system whose performance is seldom fully understood. The safety-instrumented system reliability estimation is influenced by several simplifications and assumptions, both about the safety-instrumented system and its operating context, and therefore subject to uncertainty. If the decision makers are not aware of the level of uncertainty, they may misinterpret the results and select a safety-instrumented system design that is either too complex or too simple, or with an inadequate testing strategy, to provide the required risk reduction. This article elucidates the uncertainties related to safety-instrumented system reliability estimation. The article is limited to safety-instrumented systems that are operated in a low-demand mode, for which the probability of failure on demand is the standard reliability measure. The uncertainty of the probability of failure on demand estimate is classified as completeness uncertainty, model uncertainty, and parameter uncertainty and each category is thoroughly discussed. It is argued that the completeness uncertainty is the most important for safety-instrumented system reliability analyses, followed by parameter and model uncertainty. It is further argued that uncertainty assessment should be an integrated part of any safety-instrumented system reliability analysis, and that the analyst should communicate her judgment about the uncertainty to the decision-makers as part of the analysis results.