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An Improved Workflow for Permeability Estimation from Image Logs with Uncertainty Quantification

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Abstract

Production forecasts are extremely sensitive to the spatial distribution of certain reservoir parameters, especially permeability, which controls fluid flow and efficiency of many recovery methods. Modeling the heterogeneity in permeability distribution is challenging due to various reasons. First, proper sampling is difficult due to biased sampling, dilation effects and sample disturbance. Second, calibration of core-plug measurements with well test data is problematic because of their vast differences in scale; in the case of heavy oil reservoirs, conventional well tests are not feasible due to bitumen’s ultra-low mobility at initial conditions. Therefore, in addition to flow-based measurements, alternative static data including image logs and core photographs, which capture detailed high-resolution information regarding heterogeneities at the millimeter scale, may be integrated for reliable permeability estimation along the wellbore. This paper examines the existing technique for permeability prediction from image logs (or core photographs) and proposes a set of improvements. For example, automatic variogram modeling is performed over a set of moving windows to simulate the sand–shale indicators along the well trajectory, capturing the locally varying anisotropy in variogram model parameters. In addition, it presents a unified approach to properly quantify the uncertainty in the histogram of core measurements and well log data: Joint histogram uncertainty considering the correlations between variables is incorporated into the existing methodology. The millimeter-scale model is constructed and upscaled to the centimeter scale via steady-state flow-based upscaling. This step is repeated with the centimeter-scale model until the final flow-simulation scale is attained. The entire methodology is applied within individual facies with distinct spatial correlations. A field case study involving data from the McMurray Formation (Alberta, Canada) is presented, where the permeability distributions in five different facies are modeled. The results indicate that considering automatic variogram modeling may lead to more realistic representation of the orientation of certain heterogeneous features that are strongly influencing the permeability to flow; furthermore, incorporating histogram uncertainty enables the modeling of porosity and permeability with improved uncertainty.

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Notes

  1. The lower–upper matrices are calculated from Cholesky decomposition of the covariance matrix.

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Acknowledgments

The financial support of this work was provided by the Natural Sciences and Engineering Research Council of Canada’s Engage Grants Program (No. 507806 held by the second author). We are particularly grateful to the Centre for Computational Geostatistics (CCG) for providing computing resources and software access. We thank PTTEP Canada Ltd. for providing data and permission to publish this manuscript. Finally, we thank one anonymous reviewer and Dr. Michael Pyrcz for their comments and suggestions.

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Authors and Affiliations

  1. Centre for Computational Geostatistics, University of Alberta, 6-247 Donadeo Innovation Centre For Engineering, 9211-116 Street, Edmonton, AB, T6G 1H9, Canada

    Mehdi Rezvandehy

  2. School of Mining and Petroleum Engineering, University of Alberta, 6-285 Donadeo Innovation Centre for Engineering, 9211-116 Street, Edmonton, AB, T6G 1H9, Canada

    Juliana Y. Leung

  3. PTTEP Canada Limited, 1800, 350-7th Avenue SW, Calgary, AB, T2P 3N9, Canada

    Weishan Ren, Ben Hollands & Guoai Pan

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  1. Mehdi Rezvandehy
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  2. Juliana Y. Leung
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  4. Ben Hollands
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  5. Guoai Pan
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Correspondence to Mehdi Rezvandehy.

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Rezvandehy, M., Leung, J.Y., Ren, W. et al. An Improved Workflow for Permeability Estimation from Image Logs with Uncertainty Quantification. Nat Resour Res 28, 777–811 (2019). https://doi.org/10.1007/s11053-018-9418-z

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