Use of a Probabilistic and Multi-Objective History Matching for Uncertainty Reduction for the Norne Benchmark Case

2017 ◽  
Author(s):  
Carla J. Ferreira ◽  
Alessandra Davolio ◽  
Denis J. Schiozer
Keyword(s):  
History Matching ◽  
Uncertainty Reduction ◽  
Multi Objective

Influence of Additional Objective Functions on Uncertainty Reduction and History Matching

2018 ◽  
Author(s):  
Forlan La Rosa Almeida ◽  
Helena Nandi Formentin ◽  
Célio Maschio ◽  
Alessandra Davolio ◽  
Denis José Schiozer
Keyword(s):  
History Matching ◽  
Uncertainty Reduction ◽  
Objective Functions

scholarly journals Development of a robust multi-objective history matching for reliable well-based production forecasts

2016 ◽  
Vol 34 (6) ◽  
pp. 795-809 ◽  
Author(s):  
Baehyun Min ◽  
Joe M Kang ◽  
Hoyoung Lee ◽  
Suryeom Jo ◽  
Changhyup Park ◽  
...  
Keyword(s):  
History Matching ◽  
Multi Objective

Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

2009 ◽  
Author(s):  
M. Li ◽  
N. Williams ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Uncertainty Reduction ◽  
Interval Uncertainty ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Limited Budget ◽  
Output Entropy ◽  
Global Uncertainty ◽  
Significant Attention

Sensitivity analysis has received significant attention in engineering design. While sensitivity analysis methods can be global, taking into account all variations, or local, taking into account small variations, they generally identify which uncertain parameters are most important and to what extent their effect might be on design performance. The extant methods do not, in general, tackle the question of which ranges of parameter uncertainty are most important or how to best allocate investments to partial uncertainty reduction in parameters under a limited budget. More specifically, no previous approach has been reported that can handle single-disciplinary multi-output global sensitivity analysis for both a single design and multiple designs under interval uncertainty. Two new global uncertainty metrics, i.e., radius of output sensitivity region and multi-output entropy performance, are presented. With these metrics, a multi-objective optimization model is developed and solved to obtain fractional levels of parameter uncertainty reduction that provide the greatest payoff in system performance for the least amount of “investment”. Two case studies of varying difficulty are presented to demonstrate the applicability of the proposed approach.

A New Approach to Perform a Probabilistic and Multi-objective History Matching

2014 ◽  
Author(s):  
Forlan La Rosa Almeida ◽  
Alessandra Davolio ◽  
Denis Jose Schiozer
Keyword(s):  
History Matching ◽  
New Approach ◽  
Multi Objective
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