Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/326563
Type: Artigo
Title: Selection Of Representative Models For Decision Analysis Under Uncertainty
Author: Meira
Luis A. A.; Coelho
Guilherme P.; Santos
Antonio Alberto S.; Schiozer
Denis J.
Abstract: The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request. (C) 2015 Elsevier Ltd. All rights reserved.
Subject: Representative Model Selection
Decision Analysis Under Uncertainty
Uncertain Geological Models
Optimization
Unisim-i Benchmark
Editor: Pergamon-Elsevier Science LTD
Oxford
Rights: fechado
Identifier DOI: 10.1016/j.cageo.2015.11.012
Address: http://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S0098300415300893
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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