Latin hypercube sampling): Random Sampling - Tutorial 1. The proposed method exhibited good convergence along the iterations, reducing the variability of the objective function (average normalized misfit) by approximately 90% from the first to the last iteration. Please watch the following short tutorials to quickly learn random sampling (Monte Carlo Vs. To demonstrate the robustness of the proposed method and the consistency of the results, the iterative process was repeated 10 times and the discrepancy among the results was very small. Latin Hypercube PEST Utilities for Latin Hypercube Sampling A number of utility programs are available which facilitate use of PEST in conjunction with a Latin hypercube sampling (LHS) program produced by Sandia National Laboratories. Eight iterations with 450 combinations for each one were adequate to address the problem studied in this paper. The methodology was successfully applied to the UNISIM-I-H benchmark case, which is a reservoir model based on Namorado field, Campos basin, Brazil. One of them is based on a global objective function (GOF) and the others are based on a local objective function (LOF), which is composed of influenced reservoir responses identified with the aim of a correlation matrix. The histograms are smoothed and used to estimate marginal probability densities of reservoir attributes. The iterative procedure consists of selecting a set of models based on the history matching quality (normalized misfit) to generate histograms. This paper describes a new iterative procedure for probabilistic history matching using a discrete Latin Hypercube (DLHC) sampling method and nonparametric density estimation.

Latin hypercube sampling mcmc