La norma OSHAS 18 001: 1999 Directrices sobre sistemas de gestión de la seguridad y salud en el trabajo.

The Bayesian approach to statistical inference has been described as the explicit quantitative use of experience, expertise and skills in the analysis and interpretation of cost estimation evaluation (Fryback et al. 2001, O’Hagan and Luce 2003, Spiegelhalter et al. 2004, and Ades et al. 2006). From the forgoing discussion of Bayesian approaches in section 4.4.1.3 and the research gap analysis in section 4.6, it has been demonstrated that Bayesian analysis has an important feature of permitting the incorporation of expert opinion in the form of prior distributions. Making decisions or giving prior distributions under uncertainty requires the use of parameters. This makes ABC technique an appropriate model combination option with the Bayesian because it provides traceable information on activities of drilling which can serve as the prior and it is also suitable and applicable to the operation of the offshore drilling industry making. Thus the use of parameters, processes and procedures for costing can easily serve as the parameters on which probability distributions can be given using Bayesian (Felli and Hazen 1999, O’Hagan & Luce 2003, Spiegelhalter et al. 2004, and Ades et al. 2006). Owing to the cost estimation model requirements discussed in section 3.2 in chapter 3, it emphasised the need for improvement in the internal validity to guarantee the generalizability and reliability of such model. As such, since standard probabilistic sensitivity analysis is essentially Bayesian, it makes this approach engaging as it is satisfactorily flexible to enable suitable allowance to be made for all

102| P a g e risks associated with cost variables for offshore drilling projects and can be easily integrated with other decision modelling frameworks as in the case of ABC (Cooper et al. 2002, Parmigiani 2002, Cooper et al. 2004, Qian & Ben-Arieh 2008, Yongqian et al. 2010, and García-Crespo et al. 2011).

Another rationale that justifies the choice of Bayesian and ABC is the fact that there are evidences that suggest these two can offer better solutions compared to the exiting cost models as discussed in sections 4.2, 4.3 and 4.4 (O’Hagan 1994, Congdon 2001, Garthwaite et al. 2006, Jenson 2007, Gelman et al. 2014, and Fenton 2015). Again, because the common limitation of most of the past models is their inability to use past experiences and expert knowledge to improve cost estimates, hence Bayesian which was predominately built to solve these challenges discussed is therefore properly placed as an appropriate approach to use considering the research gap and the objectives of the study (Garthwaite et al. 2006, and Fenton 2015). Moreover, the use of Bayesian to analyse biological effects of oil spill by Linklin et al. (2011), Silva & Costa’s (2012) work on cost estimation model for Seoul oil projects, and Assaf et al. (2011) and Khatibisepehr at al. (2013) findings on the improvement made in cost estimation using Bayesian and parametric technique using data from the Japanese Steam industry critically reviewing in section 4.4.1.3 above all justifies the choice of combining Bayesian with ABC. Thus while ABC provides accurate and traceable cost information on the project at hand Ben-Arieh (2000), Niazi et al. (2006), Qian & Ben- Arieh (2008), and Yongqian et al. (2010); Bayesian techniques can enhance cost estimates by reducing the error levels in ABC when combined which suggests the appropriateness of this choice to help investigate how cost overruns can be reduced in the upstream oil and gas drilling industry.

103| P a g e In addition to the above, the Bayesian method is fundamentally based on probability as discussed earlier and since the inability of current models to accommodate expert experiences into probabilistic form has been identified as one of the flaws in previous cost models, substantiates the adoption of the method. Again, chapters 1 and 2 of the study established the aforementioned deficiencies in the current cost models which is why Bayesian which have these as part of its theoretical components is better placed for this research (David &Baglioni 1988, O’Hagan 1994, Carlin & Thomas 1997, Congdon 2001, O’Hagan & Luce 2003, and Gelman et al. 2014). This is because ABC satisfies requirements discussed in sections 3.2.1 (definition and purpose), 3.2.3 (data input), and 3.2.5 (suitability and applicability) whereas Bayesian on the other hand reasonably cover sections 3.2.2 (theoretical underpinning) and 3.2.4 (risk capture and robustness) which makes their integration into a model cogent as it fulfils the relevant requirements a cost model should possess in view of the problem of cost overrun in the oil industry. Again, findings by Shahab & Abdalla (2001), Roy (2003), H’mida et al. (2006), and Tammineni et al. (2009) revealed that integrating two or more models to from a single model improves cost estimation as the weaknesses in one is complimented with the strengths in the other when integrated. It is in this light that the choice to investigate if integration of Bayesian and ABC models could improve project cost estimation and reduce cost overrun in the offshore deepwater drilling industry is suitable.

Finally, the other models discussed above possess their own strengths and uniqueness and by the adoption of Bayesian and ABC in this study do not in any way suggest that the others are incapable to offer any solutions in addressing the problem of cost overrun. But as it has been thoroughly shown in this chapter (4) and especially in this section 4.7, the integration of Bayesian and ABC benefits the study because of the

104| P a g e possibility to generate probabilistic data usable for cost modelling, the need to formulate a suitable and applicable model for the oil industry among many others which others lack in this context (Zhang et al. 1996, Niazi et al. 2006, Han et al. 2009, Yongqian et al. 2010, Chou 2011, Chaudry et al. 2013, and Fenton 2015).The model results would be better placed than the other methods it directly proffer answers to the gap identified in this study.

4.8 Chapter Summary

This chapter critically reviewed and analysed previous research on cost estimation models for the offshore deepwater drilling industry thus providing a better understanding of the cost estimation practices used in the industry. The types of cost models were discussed under 3 strands namely: quantitative, mixed and qualitative method. More than 9 cost estimation models were analysed based on their strengths and weakness in providing accurate estimations with limited data, precisely capture risk and/or factor probability results of all the cost variables in the offshore deep-water drilling operations into a model and can be suitable and applicable to the systems and operations of the industry. Activity-based costing (ABC) was found to be the most appropriate cost estimation technique to be combined with Bayesian Network approach into a single model for the study. The rationale and benefits of this choice is strongly argued and justified in section 4.7 above. The chapter also highlighted the gap in the literature. The next chapter discusses the current practices of the adopted model technique (Bayesian Network approach). It also provides analysis of the current elicitation process and proposes an improved elicitation process based on Bayesian Network approach for the cost estimation as part of the researcher’s contribution to knowledge. Primary data is collected using the elicitation process for analysis of the model formulation and for the validation of the improved process developed.

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Chapter Five

BAYESIAN APPROACH CURRENT PRACTICE AND EXPERT ELICITATION PROCESS