Many integrated evaluation teams use stochastic simulation only to estimate cumulative probability resource curves for exploration assets. Three deterministic cases are then anchored at the P90, P50, and P10 points of the curve and serve as the basis for in-depth engineering and exploration economics modeling. As illustrated by the Delta prospect, discrepancies between resulting NPV approximations and true prospect NPV may be material.

Since it may take another generation to break down the wall between deterministic and stochastic approaches to project assessment, this paper proposes an interim workflow to reconcile both mindsets. The method builds on modern software's ability to pre-estimate the shape of the NPV probability distribution. Once the shape is available, a discretization shortcut is applied to obtain relevant NPV distribution fractiles and weights. Equivalent resource fractiles are then ‘reverse-fitted’ using the stochastic value cloud. Finally, discrete cases reflecting these calculated resource fractiles are provided for deterministic engineering and valuation. With the illustrative Delta prospect, applying the method resulted in selecting P70-P32-P16 resource fractiles to ensure the best fit to the true (stochastic) NPV.

The proposed method provides a practical way to combine the strengths of simulation, discretization, and in-depth engineering. It is supported by Full Cycle tools embedded in industry-standard software for probabilistic risk, resource, and value assessments.