Evaluation of unsupervised machine learning frameworks to select representative geological realizations for uncertainty quantification

Ensembles of geological realizations (GR) are normally processed by numerical simulators to evaluate geological uncertainty during the decision-making process. Although different stochastic spatial algorithms can quickly generate hundreds to thousands of GR to capture the full uncertainty range, the simulation process applied to this number of realizations is computationally expensive. Hence, a small subset of representative geological realizations (RGR) that statistically represent the features of the full ensemble can be used for uncertainty quantification. In this study, unsupervised machine learning (UML) is applied by considering different (1) adjacency matrix construction, (2) dimensionality reduction, and (3) clustering and sampling algorithms to generate several RGR sets. Then, the mismatches between the distribution of different field and well indicators obtained from the RGR sets and the whole ensemble are measured using the Kolmogorov-Smirnov (KS) test to compare the uncertainty space of the subsets and the full set. Furthermore, to measure the pairwise adjacency between the realizations, we use a static reservoir feature called reservoir quality index (RQI). We performed extensive computational analyses to appraise the performance of the UML in two benchmark cases. Each case contains 500 GR. This study can provide a comprehensive assessment of the UML for the RGR selection due to the application of different algorithms. The results showed that the RGR set can be successfully selected without previous flow simulation runs, if an appropriate UML method is employed. This leads to a reduction in the computational cost during uncertainty quantification and risk analysis. Furthermore, we observed that the optimal number of RGR should be chosen due to the geological complexity of each case study. We also found that the type of recovery mechanism has no impact on the optimal number of RGR and on UML methods. The appropriate RGR set can be used for production forecasts and development planning support.