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Multiple-Point Statistics for Training Image Selection

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Abstract

Selecting a training image (TI) that is representative of the target spatial phenomenon (reservoir, mineral deposit, soil type, etc.) is essential for an effective application of multiple-point statistics (MPS) simulation. It is often possible to narrow potential TIs to a general subset based on the available geological knowledge; however, this is largely subjective. A method is presented that compares the distribution of runs and the multiple-point density function from available exploration data and TIs. The difference in the MPS can be used to select the TI that is most representative of the data set. This tool may be applied to further narrow a suite of TIs for a more realistic model of spatial uncertainty. In addition, significant differences between the spatial statistics of local conditioning data and a TI may lead to artifacts in MPS. The utilization of this tool will identify contradictions between conditioning data and TIs. TI selection is demonstrated for a deepwater reservoir with 32 wells.

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Acknowledgment

We would like to thank Chevron for providing the 32-well data set used for testing.

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Authors and Affiliations

  1. Center for Computational Geostatistics (CCG), University of Alberta, Edmonton, AB, Canada

    Jeff B. Boisvert & Clayton V. Deutsch

  2. Department of Civil and Environmental Engineering, 5-052 Markin/CNRL Natural Resources Engineering Facility, T6G 2W2, Edmonton, AB, Canada

    Jeff B. Boisvert & Clayton V. Deutsch

  3. Quantitative Stratigraphy, Earth Sciences Research and Development, Chevron Energy Technology Company, Houston, TX, USA

    Michael J. Pyrcz

Authors
  1. Jeff B. Boisvert
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  2. Michael J. Pyrcz
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  3. Clayton V. Deutsch
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Correspondence to Jeff B. Boisvert.

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Boisvert, J.B., Pyrcz, M.J. & Deutsch, C.V. Multiple-Point Statistics for Training Image Selection. Nat Resour Res 16, 313–321 (2007). https://doi.org/10.1007/s11053-008-9058-9

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  • DOI: https://doi.org/10.1007/s11053-008-9058-9

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