Use of clustering techniques for automated lumping of components in compositional models

Compositional reservoir simulation run times scale with the square of the number of components used to characterize the fluid. Therefore, having usable and practical reservoir models requires that we minimize the number of components without sacrificing prediction accuracy. In this work we: 1. Validate a novel approach that automates the compositional lumping by using clustering techniques that allow the number of components to be controlled 2. Discuss its implementation into a simulator pre-processor to allows quick evaluation of the impact on production profiles and run-times of reducing the number of components in actual simulation runs 3. Explore alternative approaches to compressing the compositional data in the simulator cells to allow component convection to be carried out in compressed format using: (a) Singular Value Decomposition (SVD) (b) Non-negative matrix factorization (NNMF) (c) Auto-encoders (AE