The output is the misfit between observed and simulated production rates then, we compare the influence and correlation matrices obtained with DLHG and Monte Carlo samples. First, we evaluate the ability of DLHG to produce output cumulative distribution functions (CDF) that replicate a more exhaustive sampling technique (Monte Carlo) using the Kolmogorov-Smirnov test. The methodology presented in this work is divided in three steps. Such evaluation gains greater importance in complex reservoir models because the number of tests to determine the reservoir scenarios that match dynamic data can be high due to the level of complexity. Both factors should be evaluated in order to improve the project's efficiency and to obtain reliable results. The sample size affects the time demanded and results accuracy in a history matching process because a small sample size can yield inaccurate risk quantification and a high sample size can demand excessive time to reach good results. This work describes a methodology that evaluates the Discrete Latin Hypercube with Geostatistical Realizations (DLHG) sample size for complex models in the history matching under uncertainties process with application to the Norne Benchmark Case.
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