A Multiscale Approach for Geologically and Flow Consistent Modeling

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Abstract

Subsurface geological models are usually constructed on high-resolution grids in a way that various complexities and heterogeneities are depicted properly. Such models, however, cannot be used directly in the current flow simulators, as they are tied with high computational cost. Thus, using upscaling, by which one can produce flow consistent models that can alleviate the computational burden of flow simulators, is inevitable. Although the upscaling methods are able to reproduce the flow responses, they might not retain the initial geological assumptions. The reservoir models are initially constructed on uniform and high-resolution grids and then, if necessary, are upscaled to be used for flow simulations. A subsurface modeling approach that not only preserves the geological heterogeneity but also provides models that can be used, straight or with a small level of upscaling, in the flow simulators is desirable. In this paper, a new multiresolution method based on (1) the importance of conditioning well data and (2) being geologically and flow consistent is presented. This method discretizes the initial model into several regions based on the available data. Then, the initial assumed geological model is converted into, for example, various high- and low-resolution models. Next, the high-resolution model is used for regions with high-quality data (e.g., well locations), while the low-resolution model is used for the remaining areas. Finally, the patterns of these areas are interlocked, which result in a multiresolution geologically and flow consistent subsurface model. The accuracy of this method is demonstrated using two-phase flow simulation on four complex subsurface systems. The results indicate that the same flow responses, in a much less time, are reproduced using the multiscale models. The speed-up factor gained using the proposed method is also several orders of magnitude.

Keywords

Two-phase flow Multiresolution modeling Geological model SPE-10 Data assimilation 

Notes

Acknowledgements

The financial support from the University of Wyoming for this research is greatly acknowledged. The critical reviews from the Associate Editor and anonymous reviewers that led to improving the initial submission are greatly appreciated. The significant help of S. Kamrava in the performing some of the computations is also greatly acknowledged.

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Authors and Affiliations

  1. 1.Department of Petroleum EngineeringUniversity of WyomingLaramieUSA

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