BR 
EN Selecting representative models for ensemble-based production optimization in carbonate reservoirs with intelligent wells and WAG injection
Resumo
by Susana Margarida da Graça Santos, Antonio Alberto de Souza dos Santos, Denis José Schiozer, presented at 17th European Conference on the Mathematics of Oil Recovery, September 2020
Abstract
Production optimization under uncertainty is complex and computationally demanding, a particularly challenging process for carbonate reservoirs subject to WAG injection, represented in large ensembles with high simulation runtimes. Search spaces of optimization are often large, where reservoir models are complex and the number of decision variables is high. The computational costs of ensemble-based production optimization can be decreased by reducing the size of the ensemble with representative models (RM). The validity of this method requires that the RM maintain representativeness throughout the optimization process, where the production strategy changes at each evaluation. Many techniques of RM selection use production forecasts of the ensemble for an initial production strategy, which raises questions about the robustness of the RM. This work investigates approaches to ensure the consistency of RM in ensemble-based long-term optimization. We use a metaheuristic optimization algorithm that finds sets of RM that represent the ensemble in the probability distribution of uncertain attributes and the variability of production, injection, and economic indicators (Meira et al., 2020). Our case study is a benchmark light-oil fractured carbonate with features of Brazilian pre-salt reservoirs and many reservoir and operational uncertainties. We obtained production, injection and economic indicators using different approaches to provide valuable insight for RM selection. We inferred about RM fitness for production optimization based on their adequacy for uncertainty quantification for varying production strategies. Despite the effects of changing decision variables on RM representativity, our results suggest the possible use of RM for ensemble-based production optimizations with limitations related to the estimation of the probabilistic objective function due to mismatches in the probabilities of occurrence. Using production indicators obtained from a base production strategy decreased RM representativeness when compared to RM selection based on a more robust evaluation of reservoir performance using a wide-covering well pattern and no restrictions from production facilities. Finally, our results suggest valid RM selection using production forecasts for intermediate dates of the simulation period, an important contribution for ensembles with very high simulation runtimes. We also provide a broad theoretical background on the uncertain reservoir system and on approaches to obtain reduced ensembles and their
applications.