Traditional deterministic financial models produce a single NPV, a single IRR, and a single payback period. While these point estimates are useful for quick screening, they can give a misleading sense of precision when the underlying inputs, such as commodity prices, production rates, and capital costs, are inherently uncertain. Monte Carlo simulation addresses this limitation by running thousands of scenarios, each sampling from probability distributions defined for key input variables.
In energy project finance, the most impactful uncertain variables typically include oil or gas price trajectories, initial production rates and decline characteristics, capital cost overruns, and operating expenditure escalation. By assigning triangular, normal, or log-normal distributions to these inputs and running several thousand iterations, analysts can generate a probability distribution of project outcomes. The result is a P10/P50/P90 range for NPV and IRR that communicates risk far more effectively than any single number.
Integrating Monte Carlo analysis into an engineering platform that already handles technical sizing and cost estimation creates a single, connected workflow from reservoir parameters to investment-grade financial metrics. This integrated approach reduces the risk of transcription errors between tools and enables rapid sensitivity analysis when project assumptions change during negotiations or due diligence.