Conditional simulation as a tool for measuring uncertainties in petroleum exploration

A fundamental task for petroleum exploration decision-making is to evaluate the uncertainty of well outcomes. The recent development of geostatistical simulation techniques provides an effective means to the generation of a full uncertainty model for any random variable. Sequential indicator simulation has been used as a tool to generate alternate, equal-probable stochastic models, from which various representations of uncertainties can be created. These results can be used as input for the quantification of various risks associated with a wildcat drilling program or the estimation of petroleum resources. A simple case study is given to demonstrate the use of sequential indicator simulation. The data involves a set of wildcat wells in a gas play. The multiple simulated stochastic models are then post-processed to characterize various uncertainties associated with drilling outcomes. © 1997 International Association for Mathematical Geology.