Multilevel Monte Carlo methods for computing failure probability of porous media flow systems

被引:7
|
作者
Fagerlund, F. [1 ]
Hellman, F. [2 ]
Malqvist, A. [3 ,4 ]
Niemi, A. [1 ]
机构
[1] Uppsala Univ, Dept Earth Sci, Box 337, SE-75105 Uppsala, Sweden
[2] Uppsala Univ, Dept Informat Technol, Box 337, SE-75105 Uppsala, Sweden
[3] Chalmers Univ Technol, Dept Math Sci, SE-41296 Gothenburg, Sweden
[4] Univ Gothenburg, SE-41296 Gothenburg, Sweden
基金
瑞典研究理事会;
关键词
CDF estimation; Failure probability; Porous media flow simulation; Multilevel Monte Carlo; Selective refinement; RANDOM-COEFFICIENTS; CO2; SEQUESTRATION; ERROR ESTIMATION; ELLIPTIC PDES; SIMULATION;
D O I
10.1016/j.advwatres.2016.06.007
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
We study improvements of the standard and multilevel Monte Carlo method for point evaluation of the cumulative distribution function (failure probability) applied to porous media two-phase flow simulations with uncertain permeability. To illustrate the methods, we study an injection scenario where we consider sweep efficiency of the injected phase as quantity of interest and seek the probability that this quantity of interest is smaller than a critical value. In the sampling procedure, we use computable error bounds on the sweep efficiency functional to identify small subsets of realizations to solve highest accuracy by means of what we call selective refinement. We quantify the performance gains possible by using selective refinement in combination with both the standard and multilevel Monte Carlo method. We also identify issues in the process of practical implementation of the methods. We conclude that significant savings in computational cost are possible for failure probability estimation in a realistic setting using the selective refinement technique, both in combination with standard and multilevel Monte Carlo. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:498 / 509
页数:12
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