El grupo local tiene control suficiente en la tierra que le garantice la conservación de su modo de vida y su desarrollo sostenible

This chapter explained how technical, human and organizational factors are understood and used in this thesis. Further, a comprehensive list of all major accidents and incidents have been compiled and used in the identification of accident scenarios and as initiating causes for hazardous events. MTO-analyses have been performed for some accidents where the causes were rooted in all of the MTO factors. As a result of the identification of causes, a list of the most common RIFs have been established. This list will be used in the next chapter when the BBN is modeled. It is interesting to notice how this RIF identification analysis compares to the results of the RNNP (2014) survey. These two analysis were done simultaneously and hence not influenced by each other, and the results are very similar. Both analyses emphasizes the importance of competence and knowledge for the crew, HMI problems, maintenance and inspections. In addition, both analyses recognizes that most of the problems are rooted in organizational factors.

The main focus in this thesis is the operational issues regarding loss of stability and buoyancy of semi-submersible units. This means that only the events that affects stability is considered. Evaluation of the consequences beyond loss of stability or buoyancy, such as evacuation and loss of life, have not been treated in this analysis. This is an important and related topic that is recommend for further research.

The selected RIFs are considered as the root causes for events that can lead to loss of stability. As a comparison to the RABL program, that was conducted in the late 1980’s, the RIFs listed in table 5.4 are more focused on root causes, whereas RABL focus more on technical and op- erational factors (Standing, 2003). RABL report no. 2 (Østby et al., 1987) notes that the history of ballast system failures includes numerous examples where human operational failures have

caused significant loss of buoyancy, but does not consider human error as an initiating event (Standing, 2003).

One of the conclusions in the RABL project is that human maloperation, combined with a sin- gle component failure was probably the most critical combination of failure events (Østby et al., 1987). The final recommendations suggested that efforts should be made to identify system failures that might lead to critical ballast operator errors (Standing, 2003). It was also consid- ered important to prepare procedures and establish a sound understanding of how to handle system failures during critical operations (Standing, 2003).

From the accident investigations presented in this chapter it is clear that the recommendations given by RABL more than 25 years ago are still valid today. By utilizing the BBN approach to describe the risk of loss of stability and buoyancy, it is possible to describe root causes in a more refined way than the RABL project could do. In this way, a more thorough risk analysis with focus on technical, human and organizational factors can be performed.

Modeling of Stability Risk

In this chapter a model of the proposed BBN will be presented. The rationale behind the model, assumptions and simplifications are discussed. The quantification process is also explained and exemplified. The model is implemented into two different software tools for quantitative analyses. Last, some general comments about the model and the modeling process is pre- sented. A more thorough evaluation is given in chapter 7.

In the development of a BBN to model stability risk the most applicable level of modeling is on a macro level1. This model will then treat stability in a general picture. More specific modeling may be required for the sub-systems or sub-tasks encountered in ballast operations. The chal- lenge is then that this model has to fulfill the requirements of both being reasonable complete and the same time practically usable, which means to balance two conflicting requirements, but above all, it has to fit the purpose (Øien, 2001a). The purpose of this model is to describe the operational risk level of a semi-submersible with regards to stability.

Øien and Sklet (2001) states that it seems to be an increasingly intrusive problem to say some- thing about how the safety or the risk level is developing in the North Sea, both for individual installations and for the industry as a whole. Øien and Sklet (2001) also claims that if the risk level is not systematically monitored during operations, then we cannot say anything about how the risk is developing, not whether it increases or decreases, and we can definitely not say anything about how much it increases or decreases. The model presented in this chapter ad- dress this issue by suggesting a framework that can be used to estimate the development the operational risk level, and also give a quantitative interpretation of the development.

The RNNP (2014) survey found that 55.5% of the persons asked claimed that marine systems is an area of risk analysis that does not get the attention that it requires. One representative from an engineering company said that “ballast and bilge is not as hot as process – where the values are created” (RNNP, 2014). More attention have been placed on hydrocarbon leaks, than on marine systems in the past. This view is also expressed by PSA, which states in the RNNP

(2014) report that it is necessary to increase the attention and status to marine systems. The method presented in this chapter is a suggestion for how to answer the statements raised by PSA and the industry.

6.1 Structure of the Bayesian Belief Network

The proposed BBN in this thesis is built on the defined barrier functions and the RIFs identified in chapter 5.4. The BBN is first modeled separately for each barrier function, then a combined model is presented. It is believed that this is a structured way of presenting the RIFs and illus- trates how the different RIFs influences the risk of failure for the barrier functions. Description of RIFs can be found in appendix C

The nodes in this network are color coded. The colors represents factors and events and are set up in the following structure:

Yellow: Organizational factors Green: Technical factors Blue: Human factors

Purple: Non controllable factors

White: Clusters for grouping related RIFs Orange: Status of a barrier function Red: Stability condition

Since the BBN consists of three separate parts with a number of concurrent RIFs (i.e. the same RIF influences more than one barrier function), the complete model presented in chapter 6.5 should be regarded as the core of this model.