The aim of this project is to predict uncertainty in the reservoir dynamics based on its static characteristics - connectivity and heterogeneity. Applied workflow includes creation of static models (discreet fracture networks (DFN) models generation based on outcrop data, models upscaling) and dynamic models (dynamic simulation of static models). Both static and dynamic models were clustered (k medoid algorithm) in order to find similarity between groups of models. Data uncertainty is covered by creating an ensemble of DFN models with range of properties. Research results show that there is similarity in models distribution, both static and dynamic one. The proposed approach suggested a CPU time saving and cost effective way to capture the range of dynamic response based on a few dynamic model simulation. Whereas the selected dynamic models are based on the variation of the static properties. The obtained results could be used further in well placement optimization and reservoir recovery prediction.
|Publication status||Published - 12 Jun 2017|
|Event||79th EAGE Conference and Exhibition 2017 - Paris, France|
Duration: 12 Jun 2017 → 15 Jun 2017
|Conference||79th EAGE Conference and Exhibition 2017|
|Period||12/06/17 → 15/06/17|
ASJC Scopus subject areas
- Geochemistry and Petrology