Free Online Probabilistic STOIIP Calculator: Monte Carlo Reserves

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Each of the 10,000 runs draws every input from its triangular range and computes oil in place. The histogram is the honest answer, the P values are the industry summary, and the tornado shows which input swings the result hardest. Change the seed to see a different random sequence give the same statistics.

Reserves as a range, not a number

A single STOIIP number multiplies six guesses and pretends the result is knowledge. The tool above does what evaluation teams do instead: give every input a minimum, most likely, and maximum, run the volumetric equation 10,000 times with values drawn from those ranges, and read the answer as a distribution. P90 is the low case you can bank, P50 the median you plan around, P10 the upside that justifies appraisal. The tornado chart below the histogram ranks the inputs by how hard each one swings the result, which is exactly the map of where the next appraisal dollar belongs.

Histogram of ten thousand Monte Carlo STOIIP outcomes with P90, P50, and P10 marked, beside a tornado chart ranking area, thickness, saturation, and porosity swings
Ten thousand versions of the field, summarized as the three numbers the industry banks, plans, and dreams on.

The simulation is seeded, so the same inputs always reproduce the same statistics, and everything runs instantly in your browser.

Reading the output

  • The spread between P90 and P10 is the real message: a factor of three between them is normal early in field life and shrinks only with data, never with optimism.
  • The mean sits above the median in volumetric products, a mathematical fact that has flattered many portfolios; report both and know why they differ.
  • The tornado almost always crowns area and thickness, which is why seismic and wells beat lab work in shrinking reserve uncertainty.
  • Triangular distributions are the honest minimum: they encode a range and a peak without pretending to know the true shape.

What this tool is for

Prospect screening, teaching probabilistic thinking, and pressure testing a deterministic number someone put in a slide. Full evaluations add dependencies between inputs and lognormal shapes; the engine here is the same idea at educational speed.

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