Scenario Reduction Applied to Geostatistical Simulations

Margaret Armstrong (margaret.armstrong@mines-paristech.fr), Aziz Ndiaye, Rija Razanatsimba and Alain Galli
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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

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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.

Keywords: stochastic optimization; multi-stage programming with recourse; computer simulation; decision making; geostatistics; optimization; petroleum; stochasticity; Nevada; United States; Walker Lake (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

Published in Mathematical Geosciences, 2013, 45 (2), pp.165-182. ⟨10.1007/s11004-012-9420-7⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00768562

DOI: 10.1007/s11004-012-9420-7

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