This case history describes the creation of a spreadsheet-based, Monte-Carlo economic model for exploration prospects. Looking at "probabilistic economics" instead of "probabilistic reserves" allows decision-makers a better understanding of the potential outcomes associated with a prospect. Although commercial software exists for such analyses, creating this spreadsheet model proved more efficient in this case through the utilization of spreadsheet algorithms already in place. The simulation model is now used routinely in-house for individual prospect analysis and is considered a viable alternative to the commercial packages.

A probabilistic reserve distribution can be generated using uncertainty distributions for volumetric input parameters and then sampling the input distributions through Monte Carlo simulation. After sampling the input distributions and calculating the corresponding reserves many times, a probabilistic distribution of reserves is created. Often in rank exploration prospects, a given reserve size is not a unique solution to the volumetric equation. Different reserve scenarios, e.g. combinations of lateral extent and recovery per acre, may yield the same reserve size but may differ in capital requirements by an order of magnitude or more. In these cases especially, it is appropriate to consider not only the range of reserve outcomes resulting from the full range of possible scenarios, but also the range of corresponding economic outcomes for the many possible scenarios. Meanwhile every single realization within the range of outcomes must be realistic. Furthermore, by calculating economics stochastically, other probabilistic methods such as mean reverting price models may be incorporated.

The spreadsheet economic model was used because it provides a significant advantage where evaluation time is limited. For the evaluation described in this paper, the analysis of exploration prospects was accomplished within a very short time frame. As the prospects were in remote locations outside of the Lower 48 and estimation of capital expenditures and production taxes was complicated, a spreadsheet program provided the desired degree of flexibility. The model was created quickly since spreadsheet algorithms already existed to calculate reserves, capital, production taxes, mean reverting price forecasts, and AFIT economics.

This paper describes how five existing spreadsheet algorithms were integrated to develop a robust Monte Carlo economic model for exploratory prospect evaluations. Emphasis is placed on the advantages of using the spreadsheet platform to establish dependencies between variables and to facilitate customization of the model.

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