The results of exploration in individual plays show that, although discovery sizes are highly variable, their distributions have certain properties useful in assessing undrilled prospects, an example being the upper limit of P99. However, such information rarely forms an integral part of the evaluation of an undrilled prospect. Where they form part of the workflow, though, historical data can help define the natural distributions associated with reservoir properties and help constrain geological and commercial risk.

In evaluating a new prospect, deterministic volumetrics can be calculated for a high case, but the probability of encountering this is, by itself, mostly guesswork; low volumetrics cases are even more difficult to constrain. Consequently, probabilistic methods have become the standard way of dealing with uncertainty in exploration. However, there is a problem in defining the trap size or volume of hydrocarbon-bearing gross reservoir (hcbGRV), specifically the distribution defined by the combined cumulative probabilities of the position of seals, reservoirs, and fluid contacts because, prior to drilling, no direct samples of these surfaces are given. Unfortunately, this problem does not have a solution, so we have developed a quality assurance tool that uses deterministic inputs to check the reality of probabilistic outputs. The tool is called “real point resource iteration” (RPRI) and is primarily aimed at improving consistency in volumetrics prediction.

RPRI uses objective criteria to calculate two deterministic cases from which a full-discovery-size distribution is created. The results are then iterated with simple statistics and information from historical data. One of the key elements of the technique involves defining standard hcbGRVs based on the depth of the last closing contour relative to the culmination. The method is quick, transparent, and repeatable and helps avoid overoptimism using empirical observations to predict realistic low case volumes. The outputs are analogous to those obtained from probabilistic techniques, meaning they can easily be compared and adjusted. RPRI can also be used to produce maps and reservoir parameters for specific probabilistic outcomes, providing real cases for input to economics calculations and planning.

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