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Scenario Reduction Applied to Geostatistical Simulations

Author

Listed:
  • Margaret Armstrong

    (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Aziz Ndiaye

    (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Rija Razanatsimba

    (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Alain Galli

    (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

Abstract

Having a large number of geostatistical simulations of a mineral or petroleum deposit provides a better idea of its upside potential and downside risk; however, large numbers of simulated realizations of a deposit may pose computational difficulties in subsequent decision-making phases. Hence, depending on the specific case, there can be a need to select a representative subset of conditionally simulated deposit realizations. This paper examines and extends an approach developed by the stochastic optimization community based on stochastic mathematical programming with recourse and is discussed here in the context of mineral deposits while it is possibly suitable for other earth science applications. The approach is based on measuring the "distance" between simulations and the introduced distance measure between simulated realizations of a mineral deposit is based on the metal above a given set of cutoff grades while a pre-existing mine design is available. The approach is tested on 100 simulations of the Walker Lake data with promising results.

Suggested Citation

  • Margaret Armstrong & Aziz Ndiaye & Rija Razanatsimba & Alain Galli, 2013. "Scenario Reduction Applied to Geostatistical Simulations," Post-Print hal-00768562, HAL.
    • Handle: RePEc:hal:journl:hal-00768562
    DOI: 10.1007/s11004-012-9420-7
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    Cited by:

    1. Yangming Qiao & Shaohua Li & Wanbing Li, 2023. "Uncertainty Evaluation Based on Bayesian Transformations: Taking Facies Proportion as An Example," Energies, MDPI, vol. 16(19), pages 1-16, October.

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