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Hierarchical stochastic modeling and optimization for petroleum field development under geological uncertainty
Affiliation:1. Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, 334 SN, Boston, MA 02115, USA;2. School of Mathematical and Statistical Sciences, Clemson University, O-110 Martin Hall, Clemson, SC 29631, USA;1. Western Kentucky University, 1906 College Heights Blvd, Bowling Green, KY 42101, USA;2. SAS, 100 SAS Campus Drive, Cary, NC 27513, USA;3. Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA;1. Decision-Aid Department, Center for Naval Systems Analysis, Praça Barão de Ladário, s/n, Ilha das Cobras, Centro, 20091000 Rio de Janeiro, RJ, Brazil;2. Production Engineering Department, Fluminense Federal University, Rua Passo da Pátria 156, São Domingos, 24210240 Niterói, RJ, Brazil;1. Business School, University of Mannheim, Mannheim, Germany;2. Chair of Logistics and Quantitative Methods in Business Administration, Julius-Maximilians-Universität, Würzburg, Germany;3. MIT-Zaragoza International Logistics Program, Zaragoza Logistics Center, Spain
Abstract:Real-world simulation optimization (SO) problems entail complex system modeling and expensive stochastic simulation. Existing SO algorithms may not be applicable for such SO problems because they often evaluate a large number of solutions with many simulation calls. We propose an integrated solution method for practical SO problems based on a hierarchical stochastic modeling and optimization (HSMO) approach. This method models and optimizes the studied system at increasing levels of accuracy by hierarchical sampling with a selected set of principal parameters. We demonstrate the efficiency of HSMO using the example problem of Brugge oil field development under geological uncertainty.
Keywords:Stochastic and simulation  System uncertainty  Numerical optimization  Petroleum field development  Energy production
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