Uncertainty quantification of an asset evaluation for an oilfield property incorporating response-surface monte-carlo simulation with stochastic oil price models

Abstract

This study analyzes the sensitivities parameters for a reliable asset evaluation, through response-surface monte-carlo simulation. The developed model yields more reliable asset evaluation integrating contractual terms, oil price changes, and production-decline estimation and distinguishes the significance of each variable. The uncertainty explains the larger range of the response. The sensitive variables in stochastic price models to net present value are drift in GBM (Geometric Brownian Motion), equilibrium price in MR (Mean Reversion), and maximum price in MRJ (Mean Reversion with Jumps). Reserves and average oil price influence significantly the cash-flow under fixed contractual terms.