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Risk capture is defined as the ability to identify, assess and present risk in a numeric form in cost models (RRV 1994). According to Muller (1978), Maidl (1988) and other researchers such as Sarma & Adeli (1998), and Salazar (2010), the offshore deep- water drilling process requires structured way of capturing risk into models due to the many activities that are involved in drilling project. Maidl (1988), and Sarma & Adeli (1998) recognised that capturing risk in oil projects is inherently difficult primarily because of lack of information but nonetheless recommended the need to find alternative ways to address that challenge as they are crucial to ensuring the accuracy of cost model estimates. A study conducted by Mohammad (2012) on railway and highway cost estimation models revealed that using expert judgement and past data or risk in similar projects helps improve risk capture during modelling. Though there are clear differences between railway and highway projects and projects in the oil industry, they are similar in terms of the amount of repetitive work involved, the mix of skill, disciplines, and the expertise required and the high risks involved. In combination this make the findings from these projects applicable to the oil industry. Evidence from “Korea Train Express” a railway project investigated by Mansfield et al. (1994) and Han et al. (2009) showed that the ability to identify and adequately assign realistic cost values to project risks has the potential to reduce cost overrun by more than 50%. While this conclusion is plausible, it adds little to how risk can be captured and incorporated into models formulation process.

Elinwa & Joshua (2001) argued that since risk is a perception and varies among companies, risk capture could be done through numeric quantification of opinions and perceptions into models. Expert opinion is one of the ways to capture risk and minimise cost overrun. Findings by Sambasivan & Soon (2007) on Malaysian Public

72| P a g e construction and Marzouk et al. (2008) on oil projects in Jordan showed evidence of the use of expert opinion to approximately capture 50% to 60% of project risk and improve project cost estimates. While the findings of Marzouk et al. have not be generally accepted as the standard measure of risk capture requirement for modelling, it can be suggested that its ability to capture half (50%) of a project risk should be a good starting point towards finding a unified measure for risk capture in modelling. However, Xiaotie & Miao (2011), and Dongkun & Xu (2012) argued that some cost estimation models are built on incomplete and inaccurate risk information and therefore recommended that 50% project risk capture should be the acceptable minimum limit as it has the potential to improve project cost estimates.

On the other hand, considering the risk and uncertainty in the offshore deep-water

drilling operations, the need for a robust model seems essential. A model is said to be

robust if “it still provides insight to a problem despite having its assumptions altered or violated” and offers insight to any changing pattern or problem in an activity/operation even when the assumptions or variables are altered (Hoogland and Boomsma 1998). Empirical evidence from the analysis of Monte Carlo cost models showed that there were contradictory conclusions about the predictive abilities of the models based on how robust each model is (Hoogland and Boomsma 1998). While models with high robustness gave consistent estimates despite changing variables and assumptions, the estimated values of the ones with low robustness changed drastically.

A review by Wang et al. (2012) on the sensitivity of it on model performance

concluded that the concept of robustness in modelling is very desirable as it forces model developers to always produce models with high estimation accuracy.

In recent years the persistent call for robustness in models is as a result of the increasing uncertainties and risks in mega projects such as oil and gas projects,

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construction and many others and the need for easy practical adaptation of models into

the operational activities of industries (Collins-Thompson 2009, Dai et al. 2011, and

Svore et al. 2011). It suggests therefore that models built with strong robustness can

help minimise risk and improve estimation. Chalupnik et al. (2006) established that

process robustness has the ability to bring expected results such as accurate project time and budget, and quality of products regardless of any unexpected adverse factors. Again, the ease to consider interdependences between variables and design process when a model is robust makes it a relevant factor for offshore drilling cost models.

However, Chalupnik et al. (2006) warned about the costs associated with developing

a solid and robust process or model stating that normally either a system or project needs to fail or extra resources are spent before improvements in robustness can be achieved in the real world. Hence it is vital to ensure that while robustness is being pursued, quality or performance of some parts of the project is not compromised (Elton & Roe 1998, Evans 2005, and Ford & Sterman 2003). Again, due to the complex nature of offshore drilling projects, robustness is required for almost every section of

the project to ensure high performance and overall improvement (Chalupnik et al.

2006, and Harman 2007). It is therefore appropriate to use robustness as one of the measurement for assessing the fitness of an offshore deep-water drilling cost estimation model in the oil and gas industry because it stands to contribute significantly to the reduction of cost overrun in the industry.