Probabilistic Approach of Petroleum Reserves Estimation

The study evaluates probabilistic modeling for accurate estimation and classification of petroleum reserves for strategic decision-making, financial planning, and regulatory compliance in the oil and gas sector. Traditional deterministic methods, which use single-point estimates for reservoir and economic parameters, often fail to reflect the full range of uncertainty in subsurface and market conditions. In this study, a probabilistic approach was applied using Monte Carlo simulation with 10,000 iterations to model uncertainties in key reservoir parameters such as porosity with range of (12–22%), water saturation (25–40%), net thickness (15–30 m), formation volume factor (1.05–1.35), and recovery factor (25–38%)—as well as economic variables including oil price ($55–$85/bbl), operational expenditure (OPEX), capital expenditure (CAPEX), and discount rate (8–14%).The simulation produced recoverable reserves estimates of P90 = 4.5 MMbbl, P50 =11.92 MMbbl, and P10 = 21.39 MMbbl, compared to a deterministic estimate of 12 MMbbl. Corresponding Net Present Values (NPVs) at a 10% discount rate were P90 = $14.80 million, P50 = $17.29 million, and P10 = $21.51 million, with an Expected Monetary Value (EMV) P90 = $12.58 million, P50 = $14.69 million, and P10 = $17.05 million. Sensitivity analysis revealed oil price and discount rate as the most influential drivers of economic outcomes, followed by recovery factors. Compared to deterministic estimates, the probabilistic method reduced uncertainty by quantifying confidence levels. The results demonstrate that probabilistic techniques provide novel approach to risk-adjusted investment decisions.