Reservoir characterization techniques have made possible geological reservoir models with multi-million grid blocks populated with permeability, porosity, and fluid saturations. These geological models are often too large to be simulated because of computational limits. These computational limits mean that typical full-field reservoir simulation models are limited to fewer than 1 million cells - at least two orders of magnitude smaller than the geological models. Upscaling techniques have been used to bridge the gap between these geological models and full-field reservoir simulation. Although there have been significant efforts in developing single-phase and two-phase upscaling algorithms, a limited verification of upscaling methods has been performed on a full-field basis. In addition to upscaling techniques, parallel simulation approaches have been developed to solve multi-million cell models with reasonable computational efficiency. Parallel simulations take up to a few hours of CPU time instead of days to run multi- million cell models. However, when many simulations are to be performed over a large range of parameter values for uncertainty studies, parallel simulations again become prohibitive and upscaling must be employed. On the other hand, the results from these upscaled simulations must be validated with results from fine-scale simulations to give confidence on the reliability of the results. There is really no way of knowing how good the results are unless we are able to perform the fine-scale simulations for verification. Parallel ultra-fine-scale simulations may provide the tool for this verification requirement. In this work, we developed several new single-phase upscaling algorithms, and investigated the verification of these techniques applied to a reservoir model and a synthetic model. For complicated multi-phase flow, the single-phase upscaling may lead to large errors. To overcome the inaccuracy, a new relative permeability upscaling approach was investigated in this dissertation research. The new approach was verified by using three-phase, 3D, and highly heterogeneity reservoir model. Based on case studies, the results from the fine-scale model may appropriately be used to guide the upscaling. The parallel simulation may guide engineers to find appropriate upscaled models through a tuning procedure. This tuning procedure has been explored in the current study to obtain results that are in close agreement with the fine-scale simulation results. The combination of parallel simulation technology and upscaling algorithms can be used to provide a better estimation of the amount of uncertainty in predicted oil recovery for real fields. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/15689 |
| Date | 04 June 2012 |
| Creators | Wang, Kefei |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. |
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