Latin Hypercube Sampling (LHS) Of Input Parameters For Stochastic Simulation (2793a398-dd69-4408-93ce-b71655bebca8)

Stochastic simulation has been routinely used for performing risk and sensitivity analyses in a variety of fields. The commonly used means of generating the simulations has been the Monte Carlo approach which usually employs a Simple Random Sampling (SRS) scheme of the variable distributions. This method is prone to sample clustering and requires a large number of samples to represent the distribution faith-fully. This can become problematic for simulating complex processes where it is impossible to run an arbitrarily large number of replicate simulations. The Latin Hypercube Sampling (LHS) method uses a stratified sampling approach which assures representation of the input distributions for any given number of samples. In this paper the LHS and SRS approaches are compared using a petroleum resource estimation example.