While many companies utilize advanced systems for quantifying and normalizing geologic risks, and some have standardized above ground risk characterization, relatively few use this information effectively when assessing risks at a corporate portfolio or aggregate level. Detailed project assessments are often distilled into expected value representations when combined at a corporate level, leaving the company exposed to potential risk concentrations and aggregate performance variability. This paper demonstrates an approach to assessing value and risks at a portfolio level when the underlying components contain opportunity specific and potentially unique risk drivers. Identifying critical risks at the corporate portfolio level and translating this information into efficient data compilation requests and allocation decisions requires a structured and disciplined approach. While the techniques in describing geological and above ground risks at a project level are commonly applied and portfolio aggregations of projects are routinely conducted, the tripping point for many organizations is in effectively leveraging the detailed stochastic information at a portfolio level. Gaining insights at the aggregate level and maintaining the appropriate perspective on these uncertainties can yield significant value to organizations as they mitigate risks and adjust plans to exploit opportunities. Many of the techniques used to quantify ‘portfolio risk’, such as efficient frontier risk measures, multi-objective scoring, or combined approaches fail in terms of providing decision makers with tangible and practical guidance as to the real impact of specific decisions on objectives. This paper codifies an approach that makes it possible to align the degree of acceptable corporate level uncertainties (risk) with the underlying, project level descriptions. This basic methodology may be employed and expanded to a wide range of asset types and diverse portfolio sets, independent of company size or complexity. A portfolio model was constructed, with individual projects described with varying degrees of uncertainty detail. This provides a clear example of the methodology while demonstrating the relative importance of project specific uncertainties as these are translated into an aggregate corporate view. The simple analytic structure described in this paper may be scaled to represent much more complex uncertainty environments and more diverse assets as required. The value in properly capturing project specific uncertainties and maintaining these descriptions when assessing portfolio value and risks is quantified for the examples depicted.