Efficient parameter estimation for a methane hydrate model with active subspaces
Methane gas hydrates have increasingly become a topic of interest because of their potential as a future energy resource. There are significant economical and environmental risks associated with extraction from hydrate reservoirs, so a variety of multiphysics models have been developed to analyze pr...
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ftdatacite:10.48550/arxiv.1801.09499 2023-05-15T17:12:07+02:00 Efficient parameter estimation for a methane hydrate model with active subspaces Parente, Mario Teixeira Mattis, Steven Gupta, Shubhangi Deusner, Christian Wohlmuth, Barbara 2018 https://dx.doi.org/10.48550/arxiv.1801.09499 https://arxiv.org/abs/1801.09499 unknown arXiv https://dx.doi.org/10.1007/s10596-018-9769-x arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Numerical Analysis math.NA Dynamical Systems math.DS Geophysics physics.geo-ph FOS Mathematics FOS Physical sciences 62-07, 65C20, 68U20 article-journal Article ScholarlyArticle Text 2018 ftdatacite https://doi.org/10.48550/arxiv.1801.09499 https://doi.org/10.1007/s10596-018-9769-x 2022-04-01T10:06:37Z Methane gas hydrates have increasingly become a topic of interest because of their potential as a future energy resource. There are significant economical and environmental risks associated with extraction from hydrate reservoirs, so a variety of multiphysics models have been developed to analyze prospective risks and benefits. These models generally have a large number of empirical parameters which are not known a priori. Traditional optimization-based parameter estimation frameworks may be ill-posed or computationally prohibitive. Bayesian inference methods have increasingly been found effective for estimating parameters in complex geophysical systems. These methods often are not viable in cases of computationally expensive models and high-dimensional parameter spaces. Recently, methods have been developed to effectively reduce the dimension of Bayesian inverse problems by identifying low-dimensional structures that are most informed by data. Active subspaces is one of the most generally applicable methods of performing this dimension reduction. In this paper, Bayesian inference of the parameters of a state-of-the-art mathematical model for methane hydrates based on experimental data from a triaxial compression test with gas hydrate-bearing sand is performed in an efficient way by utilizing active subspaces. Active subspaces are used to identify low-dimensional structure in the parameter space which is exploited by generating a cheap regression-based surrogate model and implementing a modified Markov chain Monte Carlo algorithm. Posterior densities having means that match the experimental data are approximated in a computationally efficient way. : 21 pages, 15 figures Text Methane hydrate DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Numerical Analysis math.NA Dynamical Systems math.DS Geophysics physics.geo-ph FOS Mathematics FOS Physical sciences 62-07, 65C20, 68U20 |
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Numerical Analysis math.NA Dynamical Systems math.DS Geophysics physics.geo-ph FOS Mathematics FOS Physical sciences 62-07, 65C20, 68U20 Parente, Mario Teixeira Mattis, Steven Gupta, Shubhangi Deusner, Christian Wohlmuth, Barbara Efficient parameter estimation for a methane hydrate model with active subspaces |
topic_facet |
Numerical Analysis math.NA Dynamical Systems math.DS Geophysics physics.geo-ph FOS Mathematics FOS Physical sciences 62-07, 65C20, 68U20 |
description |
Methane gas hydrates have increasingly become a topic of interest because of their potential as a future energy resource. There are significant economical and environmental risks associated with extraction from hydrate reservoirs, so a variety of multiphysics models have been developed to analyze prospective risks and benefits. These models generally have a large number of empirical parameters which are not known a priori. Traditional optimization-based parameter estimation frameworks may be ill-posed or computationally prohibitive. Bayesian inference methods have increasingly been found effective for estimating parameters in complex geophysical systems. These methods often are not viable in cases of computationally expensive models and high-dimensional parameter spaces. Recently, methods have been developed to effectively reduce the dimension of Bayesian inverse problems by identifying low-dimensional structures that are most informed by data. Active subspaces is one of the most generally applicable methods of performing this dimension reduction. In this paper, Bayesian inference of the parameters of a state-of-the-art mathematical model for methane hydrates based on experimental data from a triaxial compression test with gas hydrate-bearing sand is performed in an efficient way by utilizing active subspaces. Active subspaces are used to identify low-dimensional structure in the parameter space which is exploited by generating a cheap regression-based surrogate model and implementing a modified Markov chain Monte Carlo algorithm. Posterior densities having means that match the experimental data are approximated in a computationally efficient way. : 21 pages, 15 figures |
format |
Text |
author |
Parente, Mario Teixeira Mattis, Steven Gupta, Shubhangi Deusner, Christian Wohlmuth, Barbara |
author_facet |
Parente, Mario Teixeira Mattis, Steven Gupta, Shubhangi Deusner, Christian Wohlmuth, Barbara |
author_sort |
Parente, Mario Teixeira |
title |
Efficient parameter estimation for a methane hydrate model with active subspaces |
title_short |
Efficient parameter estimation for a methane hydrate model with active subspaces |
title_full |
Efficient parameter estimation for a methane hydrate model with active subspaces |
title_fullStr |
Efficient parameter estimation for a methane hydrate model with active subspaces |
title_full_unstemmed |
Efficient parameter estimation for a methane hydrate model with active subspaces |
title_sort |
efficient parameter estimation for a methane hydrate model with active subspaces |
publisher |
arXiv |
publishDate |
2018 |
url |
https://dx.doi.org/10.48550/arxiv.1801.09499 https://arxiv.org/abs/1801.09499 |
genre |
Methane hydrate |
genre_facet |
Methane hydrate |
op_relation |
https://dx.doi.org/10.1007/s10596-018-9769-x |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1801.09499 https://doi.org/10.1007/s10596-018-9769-x |
_version_ |
1766068889620316160 |