Population-based optimization algorithms are shown to be excellent candidates for improving the speed and solution diversity of history matching and optimization workflows, based on their successful track records for solving real-world problems.
The incorporation of reservoir engineering knowledge within these workflows, however, has been somewhat neglected. In particular, there is a lack of capability for guiding the optimization algorithms to specific regions of the search space. In a previous study, we introduced a framework for helping reservoir engineers incorporate their knowledge into history matching and optimization frameworks, by coupling a rule-based fuzzy system with a population-based sampling method.
The question is how the use of this type of information in history matching affects the performance of the reservoir study during the prediction stage. This paper investigates the effect that the incorporation of reservoir engineering knowledge during the history matching of the Teal South model production data has on reservoir performance in the prediction stage.
Two scenarios are considered. In Case I, we augment the history matching with reservoir engineering knowledge and then produce a forecast. In Case II, production data is history matched using differential evolution (DE), without fuzzy-logic-based engineering knowledge, then a forecast is produced
The results show that incorporating engineering knowledge of the reservoir under study during the history matching process can significantly reduce the uncertainty in the forecast, compared with the case where unrealistic parameter value ranges are used.