Even if you put in a porosity distribution and a

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Unformatted text preview: Monte Carlo analysis. However, petroleum reservoirs are heterogeneous, and reservoir parameters vary from sample to sample. Therefore, Simpson et al. (1999) argue, reservoir modelling requires field-wide weighted average values, derived from detailed mapping of parameters, should be used to derive these probability distributions. All respondents agreed that the lack of prescription in the literature contributes to the overall dissatisfaction with the process and to companies’ reluctance to endorse Monte Carlo simulation: “…that is actually one of the reasons why some people are uncomfortable with Monte Carlo simulation because they are not convinced that dependencies are properly handled. They are suspicious of a mathematical black box. And there’s a relationship between porosity and water saturation for example, they are not convinced that that it is recognised. Even if you put in a porosity distribution and a porosity water function they are still uncomfortable.” (P) There was broad agreement that a study indicating the shape of distributions and the nature of the dependencies that should be used for different reservoir parameters, in different geological formations at various depths, is long overdue: “I wish someone would come up with a British standard for these things – it would make life a lot easier.” (N1) and, “[The] ideal scenario is that there would be an industry standard” (R4) All of the companies use some software to assist with the Monte Carlo simulation. The most popular packages are Crystal Ball™, @risk™ and PEEP™. There was a general recognition that whilst the mechanics of the simulation is straightforward: 136 “…the clever bit is in the process that goes on before you press the button and the numbers are churning round in [the] Monte Carlo [simulation]. The clever bit is in the model that you set up where you’ve got the risk … and you’ve got the relationship…. That’s the clever bit. So you can have a fantastic tool that does Monte Carlo inside and out but [it’s]garbage in-garbage out.” (N2) The respondent from company D also stressed: “…like a lot of black boxes you’ve got to be careful that you understand the input.” (D) In Chapter 5, three other techniques were highlighted as being useful to the oil industry. Preference theory has been applied to oil industry investment decisions in the literature since the 1960s. However, software has only recently become available to assist with the generation of individuals’ preference curves. Option and portfolio theories are tools from the finance industry that have only recently been adapted to petroleum investment decisions. Consequently, at the time of the previous studies into the use of decision analysis techniques by the industry (for example, Schuyler (1997) and Fletcher and Dromgoole (1996)), these tools were not widely perceived to be particularly applicable to the oil industry. Hence, the findings from this research concerning the levels of awareness and usage of th...
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This document was uploaded on 03/30/2014.

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