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Minimum economic field size estimation and its role in exploration project risks assessment: evaluation of different methodologiesNormal access

Authors: I. Yemez, H. Stigliano, V. Singh and E. Izaguirre
Journal name: First Break
Issue: Vol 34, No 4, April 2016 pp. 29 - 38
DOI: 10.3997/1365-2397.2016004
Language: English
Info: Article, PDF ( 1.05Mb )
Price: € 30

The E&P business is most often rife with risks and uncertainties. The capability of recognizing and quantifying these uncertainties and associated risks are the key to success. For consistent exploration project risk analysis, a standard discounted cash-flow approach, combining the geological risk, Minimum Economic Field Size (MEFS), resource size distribution, development cost, rate streams, commodity price, discount rate and cash flow estimation, has been used. This task requires highly skilled geoscientists, reservoir, facility, drilling engineers and economists to estimate field development costs, generate the economic indicator to rank the exploratory prospects’ potential success and to support the informed business investment decisions. The ‘Exploration Success’ contains two main variables: (1) Probability of geologic success (Pg), and (2) Probability of economic success (Pe). To remove sub-economic volumes from the volumetric distribution, the industry uses the estimation of minimum required (break-even) resources for the full project life-cycle, considering the most likely development scenario in exploration projects. For appraisal and pre-development projects, the minimum required resources are used to benchmark the confidence level of already discovered resources with their chance of success to be economically viable. Despite several contributions made in the past and available in the literature, to the best of the author’s knowledge, most often a deterministic MEFS value is being used for the exploration project risk assessment. This single MEFS value does not allow for the capturing of the risks associated with the different input parameters uncertainties, which are used for the MEFS estimation. Therefore, in this paper, we are reviewing and systematically describing the appropriate MEFS estimation methodology. The influence of key parameters has been analysed through an example, using different MEFS methods, to demonstrate their importance for MEFS estimation. The comparison of results allows clear judgmental insights into the problem and its related uncertainties, understanding and quantifying the exploration risks and hence the chance of overall economic success.

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