The computer may be considered a laboratory and experiments performed on the computer (like running ECLIPSE) may be designed and analyzed in much the same way as non-computer experiments. Experimental design methods replace traditional sensitivity studies where one input variable is varied at the time. The basic idea is to vary several input variables simultaneously and intelligently. Damsleth, Hage and Volden [1] demonstrate that the number of simulations may be reduced 30-40% without loosing information. Based on the output from ECLIPSE a relation between the response variable (‘Production after 4 years’ in our example) and the input variables (Lobes, STOOIP,…) is estimated. This relation is used as a surrogate simulator: The values of the input variables are sampled from assigned probability distributions to produce a large number of Monte Carlo simulated values for the response variable.

We extend and discuss the results of the mentioned paper. In particular we investigate the impact of dependence between the input variables. Moreover, the relevance of Geostatistical methods (kriging) are documented. By modeling dependence between different computer runs, the precision of predictions are significantly improved. The methods are demonstrated on the case study discussed in [1].

You can access this article if you purchase or spend a download.