Please use this identifier to cite or link to this item:
Type: Artigo
Title: Production strategy optimization based on iterative discrete Latin hypercube
Author: Hohendorff Filho, João Carlos von
Maschio, Célio
Schiozer, Denis José
Abstract: This paper proposes a new iterative discrete Latin hypercube sampling based method to maximize the objective function (OF) in production strategy optimization. This methodology adequately treats posterior frequency distributions of discrete random variables and maximizes non-necessarily monotonic objective functions within discontinuous search spaces and many local optimums. To validate the method, we used an exhaustive process with an net present value (NPV) proxy, as the objective function, to be maximized. Using as an application case, the benchmark UNISIM-I-D reservoir model, based on Namorado field, Campos basin, Brazil, the method successfully maximized the NPV in the intermediate phase of production strategy optimization, and even compared favorably with a well-established optimization methodology. Population based optimization using discrete Latin hypercube sampling best suited this methodology, with consistent convergence to global optimum, few OF evaluations and the simultaneous multiple numeric reservoir simulations runs. This easy to use, reliable methodology with low computational time costs is an interesting option for optimization methods in problems of production strategy design related to the oil industry
Subject: Reservatórios
Country: Alemanha
Editor: Springer
Rights: Fechado
Identifier DOI: 10.1007/s40430-016-0511-0
Date Issue: 2016
Appears in Collections:FEM - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-84994756709.pdf862.38 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.