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Data assimilation method for fractured reservoirs using mimetic finite differences and ensemble Kalman filter

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Abstract

Optimal management of subsurface processes requires the characterization of the uncertainty in reservoir description and reservoir performance prediction. For fractured reservoirs, the location and orientation of fractures are crucial for predicting production characteristics. With the help of accurate and comprehensive knowledge of fracture distributions, early water/CO 2 breakthrough can be prevented and sweep efficiency can be improved. However, since the rock property fields are highly non-Gaussian in this case, it is a challenge to estimate fracture distributions by conventional history matching approaches. In this work, a method that combines vector-based level-set parameterization technique and ensemble Kalman filter (EnKF) for estimating fracture distributions is presented. Performing the necessary forward modeling is particularly challenging. In addition to the large number of forward models needed, each model is used for sampling of randomly located fractures. Conventional mesh generation for such systems would be time consuming if possible at all. For these reasons, we rely on a novel polyhedral mesh method using the mimetic finite difference (MFD) method. A discrete fracture model is adopted that maintains the full geometry of the fracture network. By using a cut-cell paradigm, a computational mesh for the matrix can be generated quickly and reliably. In this research, we apply this workflow on 2D two-phase fractured reservoirs. The combination of MFD approach, level-set parameterization, and EnKF provides an effective solution to address the challenges in the history matching problem of highly non-Gaussian fractured reservoirs.

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Acknowledgments

This research is partially funded by DOE grant DE-FE0023314. The authors acknowledge the financial support from the King Abdullah University of Science and Technology Academic Excellence Alliance.

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  1. Center for Subsurface Modeling, Institute of Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th Street, Austin, TX, 78712, USA

    Jing Ping, Omar Al-Hinai & Mary F. Wheeler

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  1. Jing Ping
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  2. Omar Al-Hinai
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  3. Mary F. Wheeler
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Correspondence to Jing Ping.

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Ping, J., Al-Hinai, O. & Wheeler, M.F. Data assimilation method for fractured reservoirs using mimetic finite differences and ensemble Kalman filter. Comput Geosci 21, 781–794 (2017). https://doi.org/10.1007/s10596-017-9659-7

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  • DOI: https://doi.org/10.1007/s10596-017-9659-7

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